Accounting Research Center, Booth School of Business, University of Chicago
Comparing the Accuracy and Explainability of Dividend, Free Cash Flow, and Abnormal
Earnings Equity Value Estimates
Author(s): Jennifer Francis, Per Olsson and Dennis R. Oswald
Source: Journal of Accounting Research, Vol. 38, No. 1 (Spring, 2000), pp. 45-70
Published by: Wiley on behalf of Accounting Research Center, Booth School of Business,
University of Chicago
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Journal of Accounting Research
Vol. 38 No. 1 Spring 2000
Printed in US.A.
Comparing the Accuracy and
Explainability of Dividend, Free
Cash Flow, and Abnormal Earnings
Equity Value Estimates
JENNIFER FRANCIS,* PER OLSSON,t
AND DENNIS R. OSWALD:
1. Introduction
This study provides empirical evidence on the reliability of intrinsic
value estimates derived from three theoretically equivalent valuation
models: the discounted dividend (DIV) model, the discounted free cash
flow (FCO) model, and the discounted abnormal earnings (AE) model.
We use Value Line (VL) annual forecasts of the elements in these models
to calculate value estimates for a sample of publicly traded firms fol-
lowed by Value Line during 1989-93.1 We contrast the reliability of value
*Duke University; tUniversity of Wisconsin; London Business School. This research
was supported by the Institute of Professional Accounting and the Graduate School of
Business at the University of Chicago, by the Bank Research Institute, Sweden, and Jan
Wallanders och Tom Hedelius Stiftelse for Samhallsvetenskaplig Forskning, Stockholm,
Sweden. We appreciate the comments and suggestions of workshop participants at the
1998 EAA meetings, Berkeley, Harvard, London Business School, London School of Eco-
nomics, NYU, Ohio State, Portland State, Rochester, Stockholm School of Economics,
Tilburg, and Wisconsin, and from Peter Easton, Frank Gigler, Paul Healy, Thomas Hem-
mer, Joakim Levin, Mark Mitchell, Krishna Palepu, Stephen Penman, Richard Ruback,
Linda Vincent, Terry Warfield, and Jerry Zimmerman.
I We collect third-quarter annual forecast data over a five-year .
1. IntroductionThis paper examines sell-side analysts’ perce.docxketurahhazelhurst
1. Introduction
This paper examines sell-side analysts’ percep-
tions of ‘earnings quality’. Analysts are primary
users of accounting information and their role as
information intermediaries is well established in
the capital markets (e.g. Schipper, 1991). Previous
evidence suggests that their stock recommenda-
tions, price targets, earnings forecasts and written
reports are relevant to share price formation (e.g.
Womack, 1996; Barber et al., 2001; Brav and
Lehavy, 2003, Asquith et al., 2005). One of the
main inputs in analysts’ forecasting and valuation
models is earnings, and analysts’ perceptions of
‘earnings quality’ are therefore important. There
is, however, little direct evidence in the literature
on what these perceptions are and on what role
they have in decision-making.
This paper seeks first to understand earnings
quality as interpreted by analysts, and it then tests
this interpretation against its actual usage in ana-
lysts’ research reports. In the paper’s research de-
sign, an inductive approach is used that combines
interview data with content analysis, and the find-
ings are interpreted in the light of findings from
market-based and other research. We conducted 35
interviews with sell-side analysts from 10 leading
investment banks and we carried out content
analysis on 98 equity research reports for FTSE
100 companies covered by the interviewees.
The interview evidence is that earnings quality is
a multifaceted concept and that analysts use both
accounting-based and non-accounting-based infor-
mation when assessing earnings quality. When
using accounting-based information, analysts
make adjustments to reported earnings that we find
to be consistent both with prior survey evidence
and with expectations from theory and prior mar-
ket-based evidence. There is relatively little evi-
dence in the literature, however, on the relative
usage of accounting-based and non-accounting-
based information, and we explore this issue fur-
ther in the content analysis. We find that there is a
greater prevalence of non-accounting-based infor-
mation relating to earnings quality, and that this
relative usage is consistent across sectors.
Motivated by market-based and survey evidence
that sell-side analysts are favourably biased to-
wards companies but nevertheless motivated to
sell news stories to the market, we explore whether
Accounting and Business Research, Vol. 38. No. 4. pp. 313-329. 2008 313
Analysts’ perceptions of ‘earnings quality’
Richard Barker and Shahed Imam*
Abstract—This paper examines sell-side analysts’ perceptions of ‘earnings quality’. Prior research suggests that
analysts’ stock recommendations, price targets, earnings forecasts and written reports are relevant to share price for-
mation. One of the main inputs in analysts’ forecasting and valuation models is earnings, and analysts’ perceptions
of earnings quality are therefore important. There is, however, little direct evidence in the literature on what these
percepti.
1. IntroductionThis paper examines sell-side analysts’ perce.docxjeremylockett77
1. Introduction
This paper examines sell-side analysts’ percep-
tions of ‘earnings quality’. Analysts are primary
users of accounting information and their role as
information intermediaries is well established in
the capital markets (e.g. Schipper, 1991). Previous
evidence suggests that their stock recommenda-
tions, price targets, earnings forecasts and written
reports are relevant to share price formation (e.g.
Womack, 1996; Barber et al., 2001; Brav and
Lehavy, 2003, Asquith et al., 2005). One of the
main inputs in analysts’ forecasting and valuation
models is earnings, and analysts’ perceptions of
‘earnings quality’ are therefore important. There
is, however, little direct evidence in the literature
on what these perceptions are and on what role
they have in decision-making.
This paper seeks first to understand earnings
quality as interpreted by analysts, and it then tests
this interpretation against its actual usage in ana-
lysts’ research reports. In the paper’s research de-
sign, an inductive approach is used that combines
interview data with content analysis, and the find-
ings are interpreted in the light of findings from
market-based and other research. We conducted 35
interviews with sell-side analysts from 10 leading
investment banks and we carried out content
analysis on 98 equity research reports for FTSE
100 companies covered by the interviewees.
The interview evidence is that earnings quality is
a multifaceted concept and that analysts use both
accounting-based and non-accounting-based infor-
mation when assessing earnings quality. When
using accounting-based information, analysts
make adjustments to reported earnings that we find
to be consistent both with prior survey evidence
and with expectations from theory and prior mar-
ket-based evidence. There is relatively little evi-
dence in the literature, however, on the relative
usage of accounting-based and non-accounting-
based information, and we explore this issue fur-
ther in the content analysis. We find that there is a
greater prevalence of non-accounting-based infor-
mation relating to earnings quality, and that this
relative usage is consistent across sectors.
Motivated by market-based and survey evidence
that sell-side analysts are favourably biased to-
wards companies but nevertheless motivated to
sell news stories to the market, we explore whether
Accounting and Business Research, Vol. 38. No. 4. pp. 313-329. 2008 313
Analysts’ perceptions of ‘earnings quality’
Richard Barker and Shahed Imam*
Abstract—This paper examines sell-side analysts’ perceptions of ‘earnings quality’. Prior research suggests that
analysts’ stock recommendations, price targets, earnings forecasts and written reports are relevant to share price for-
mation. One of the main inputs in analysts’ forecasting and valuation models is earnings, and analysts’ perceptions
of earnings quality are therefore important. There is, however, little direct evidence in the literature on what these
percepti ...
This document discusses factors that influence stock prices of industrial companies listed on the Indonesia Stock Exchange. It presents a literature review on debt ratio, price-earnings ratio, earnings per share, company size, and company value as independent variables that may predict stock price as the dependent variable. The document then describes the research methodology, which uses a quantitative multiple linear regression analysis of secondary data from 114 industrial companies to determine the relationship between the independent and dependent variables. The results of the analysis show that all four independent variables (debt ratio, price-earnings ratio, earnings per share, size) have a significant influence on stock price both simultaneously and partially, with earnings per share having the strongest influence. Conclusions are that companies should manage these
This document introduces the Two Sigma Factor Lens, which is a framework for constructing a parsimonious set of risk factors that individually describe independent risks across many asset classes yet collectively explain much of the risk in typical institutional investor portfolios. The lens is intended to capture the majority of risk in a holistic yet concise manner so that changes to factor exposures can easily translate to asset allocation changes. The document discusses how analyzing portfolios through a risk factor lens allows investors to better understand overlapping risk sources across asset classes and more efficiently manage portfolio risk.
These documents summarize several academic studies on hedge fund performance and investor returns:
1) One study finds that annualized returns for hedge fund investors are 3-7% lower than buy-and-hold returns for the same funds, due to poor timing of capital flows. Risk-adjusted returns are close to zero.
2) Another examines how fund life cycles are affected by flows, size, competition and performance. It finds increasing competition in a category decreases fund survival probabilities.
3) A third study finds macroeconomic risk explains a significant portion of hedge fund return dispersion, but not for mutual funds. Higher macroeconomic risk is positively related to future hedge fund returns.
This document summarizes a project that used text analysis of SEC filings to predict the relative risk of investing in publicly traded companies. The researchers analyzed sections 7 and 7A of companies' 10-K reports, which describe financial status and risks. They used logistic regression and neural networks to build models predicting investment risk based on textual patterns. Testing on new companies achieved around 90-95% accuracy. The researchers concluded text analysis of 10-K reports provides useful insights into future company performance and stock trends.
This paper examines the "variance premium", which is the difference between the squared VIX index and expected realized variance. The authors show that the variance premium captures attitudes toward economic uncertainty and predicts future stock returns over short horizons. They develop a generalized long-run risks model that generates a time-varying variance premium consistent with market return and risk-free rate levels. The model requires extensions to match the large size, volatility and skewness of the variance premium and its short-term return predictability. Calibrating the model to cash flow and asset pricing targets allows it to generate the variance premium and its return predictability features.
RAF6,4442Review of Accounting and FinanceVol. 6 No.docxmakdul
RAF
6,4
442
Review of Accounting and Finance
Vol. 6 No. 4, 2007
pp. 442-459
# Emerald Group Publishing Limited
1475-7702
DOI 10.1108/14757700710835087
Alternative evidence on financial
analysts’ use of financial
statement information
Donal Byard
Stan Ross Department of Accounting, Baruch College – CUNY,
New York, New York, USA, and
Fatma Cebenoyan
Department of Economics, Hunter College – CUNY, New York, New York, USA
Abstract
Purpose – Financial analysts are frequently viewed as information intermediaries who process and
interpret firms’ financial reports for other market participants. Much recent research, however, has
cast doubts on analysts’ ability to fully utilize the information in firms’ financial reports. Using an
alternative approach, this study aims to provide evidence on how sophisticated analysts are at using
information in firms’ financial reports.
Design/methodology/approach – The paper estimates different measures of firms’ operational
efficiency, all of which are derived from financial statement data, and compares the strength of the
association between these measures and analysts’ absolute forecast errors. It then compares a
sophisticated frontier-based measure of firms’ operational efficiency that evaluates firms’
performance relative to their competitors with three more traditional efficiency measures;
specifically the return on asset (ROA) ratio, industry-adjusted ROA, and the return on equity ratio.
Findings – The results indicate that the more sophisticated frontier-based measure is more strongly
negatively associated with analysts’ absolute forecast errors than the other three measures. The
results thus suggest that analysts are capable of undertaking a sophisticated analysis of the
information in firms’ financial reports, at least as it pertains to operational efficiency.
Originality/value – To the extent that analysts serve as a key group of users of financial
information, these results are likely to be of interest to accounting policy makers.
Keywords Financial reporting, Financial analysis, Accounting information
Paper type Research paper
1. Introduction
Financial analysts play an important role in financial markets, a role that seems to
have increased in importance in recent years. Analysts are frequently viewed as
information intermediaries who gather, process, and disseminate firm information for
investors (e.g. see Schipper, 1991). Indeed, much of the accounting literature views
analysts as sophisticated agents who process or interpret firms’ disclosures for
investors. Consistent with this view, Lang and Lundholm (1996) document that firms
with higher levels of voluntary disclosure attract a larger analyst following.
The view of analysts as sophisticated information intermediaries can, however, be
challenged. A large body of literature provides evidence that analysts do not efficiently
use all the information contained in firms’ past financial reports. For example, DeBondt
and Thaler (1990) and Abar ...
1. IntroductionThis paper examines sell-side analysts’ perce.docxketurahhazelhurst
1. Introduction
This paper examines sell-side analysts’ percep-
tions of ‘earnings quality’. Analysts are primary
users of accounting information and their role as
information intermediaries is well established in
the capital markets (e.g. Schipper, 1991). Previous
evidence suggests that their stock recommenda-
tions, price targets, earnings forecasts and written
reports are relevant to share price formation (e.g.
Womack, 1996; Barber et al., 2001; Brav and
Lehavy, 2003, Asquith et al., 2005). One of the
main inputs in analysts’ forecasting and valuation
models is earnings, and analysts’ perceptions of
‘earnings quality’ are therefore important. There
is, however, little direct evidence in the literature
on what these perceptions are and on what role
they have in decision-making.
This paper seeks first to understand earnings
quality as interpreted by analysts, and it then tests
this interpretation against its actual usage in ana-
lysts’ research reports. In the paper’s research de-
sign, an inductive approach is used that combines
interview data with content analysis, and the find-
ings are interpreted in the light of findings from
market-based and other research. We conducted 35
interviews with sell-side analysts from 10 leading
investment banks and we carried out content
analysis on 98 equity research reports for FTSE
100 companies covered by the interviewees.
The interview evidence is that earnings quality is
a multifaceted concept and that analysts use both
accounting-based and non-accounting-based infor-
mation when assessing earnings quality. When
using accounting-based information, analysts
make adjustments to reported earnings that we find
to be consistent both with prior survey evidence
and with expectations from theory and prior mar-
ket-based evidence. There is relatively little evi-
dence in the literature, however, on the relative
usage of accounting-based and non-accounting-
based information, and we explore this issue fur-
ther in the content analysis. We find that there is a
greater prevalence of non-accounting-based infor-
mation relating to earnings quality, and that this
relative usage is consistent across sectors.
Motivated by market-based and survey evidence
that sell-side analysts are favourably biased to-
wards companies but nevertheless motivated to
sell news stories to the market, we explore whether
Accounting and Business Research, Vol. 38. No. 4. pp. 313-329. 2008 313
Analysts’ perceptions of ‘earnings quality’
Richard Barker and Shahed Imam*
Abstract—This paper examines sell-side analysts’ perceptions of ‘earnings quality’. Prior research suggests that
analysts’ stock recommendations, price targets, earnings forecasts and written reports are relevant to share price for-
mation. One of the main inputs in analysts’ forecasting and valuation models is earnings, and analysts’ perceptions
of earnings quality are therefore important. There is, however, little direct evidence in the literature on what these
percepti.
1. IntroductionThis paper examines sell-side analysts’ perce.docxjeremylockett77
1. Introduction
This paper examines sell-side analysts’ percep-
tions of ‘earnings quality’. Analysts are primary
users of accounting information and their role as
information intermediaries is well established in
the capital markets (e.g. Schipper, 1991). Previous
evidence suggests that their stock recommenda-
tions, price targets, earnings forecasts and written
reports are relevant to share price formation (e.g.
Womack, 1996; Barber et al., 2001; Brav and
Lehavy, 2003, Asquith et al., 2005). One of the
main inputs in analysts’ forecasting and valuation
models is earnings, and analysts’ perceptions of
‘earnings quality’ are therefore important. There
is, however, little direct evidence in the literature
on what these perceptions are and on what role
they have in decision-making.
This paper seeks first to understand earnings
quality as interpreted by analysts, and it then tests
this interpretation against its actual usage in ana-
lysts’ research reports. In the paper’s research de-
sign, an inductive approach is used that combines
interview data with content analysis, and the find-
ings are interpreted in the light of findings from
market-based and other research. We conducted 35
interviews with sell-side analysts from 10 leading
investment banks and we carried out content
analysis on 98 equity research reports for FTSE
100 companies covered by the interviewees.
The interview evidence is that earnings quality is
a multifaceted concept and that analysts use both
accounting-based and non-accounting-based infor-
mation when assessing earnings quality. When
using accounting-based information, analysts
make adjustments to reported earnings that we find
to be consistent both with prior survey evidence
and with expectations from theory and prior mar-
ket-based evidence. There is relatively little evi-
dence in the literature, however, on the relative
usage of accounting-based and non-accounting-
based information, and we explore this issue fur-
ther in the content analysis. We find that there is a
greater prevalence of non-accounting-based infor-
mation relating to earnings quality, and that this
relative usage is consistent across sectors.
Motivated by market-based and survey evidence
that sell-side analysts are favourably biased to-
wards companies but nevertheless motivated to
sell news stories to the market, we explore whether
Accounting and Business Research, Vol. 38. No. 4. pp. 313-329. 2008 313
Analysts’ perceptions of ‘earnings quality’
Richard Barker and Shahed Imam*
Abstract—This paper examines sell-side analysts’ perceptions of ‘earnings quality’. Prior research suggests that
analysts’ stock recommendations, price targets, earnings forecasts and written reports are relevant to share price for-
mation. One of the main inputs in analysts’ forecasting and valuation models is earnings, and analysts’ perceptions
of earnings quality are therefore important. There is, however, little direct evidence in the literature on what these
percepti ...
This document discusses factors that influence stock prices of industrial companies listed on the Indonesia Stock Exchange. It presents a literature review on debt ratio, price-earnings ratio, earnings per share, company size, and company value as independent variables that may predict stock price as the dependent variable. The document then describes the research methodology, which uses a quantitative multiple linear regression analysis of secondary data from 114 industrial companies to determine the relationship between the independent and dependent variables. The results of the analysis show that all four independent variables (debt ratio, price-earnings ratio, earnings per share, size) have a significant influence on stock price both simultaneously and partially, with earnings per share having the strongest influence. Conclusions are that companies should manage these
This document introduces the Two Sigma Factor Lens, which is a framework for constructing a parsimonious set of risk factors that individually describe independent risks across many asset classes yet collectively explain much of the risk in typical institutional investor portfolios. The lens is intended to capture the majority of risk in a holistic yet concise manner so that changes to factor exposures can easily translate to asset allocation changes. The document discusses how analyzing portfolios through a risk factor lens allows investors to better understand overlapping risk sources across asset classes and more efficiently manage portfolio risk.
These documents summarize several academic studies on hedge fund performance and investor returns:
1) One study finds that annualized returns for hedge fund investors are 3-7% lower than buy-and-hold returns for the same funds, due to poor timing of capital flows. Risk-adjusted returns are close to zero.
2) Another examines how fund life cycles are affected by flows, size, competition and performance. It finds increasing competition in a category decreases fund survival probabilities.
3) A third study finds macroeconomic risk explains a significant portion of hedge fund return dispersion, but not for mutual funds. Higher macroeconomic risk is positively related to future hedge fund returns.
This document summarizes a project that used text analysis of SEC filings to predict the relative risk of investing in publicly traded companies. The researchers analyzed sections 7 and 7A of companies' 10-K reports, which describe financial status and risks. They used logistic regression and neural networks to build models predicting investment risk based on textual patterns. Testing on new companies achieved around 90-95% accuracy. The researchers concluded text analysis of 10-K reports provides useful insights into future company performance and stock trends.
This paper examines the "variance premium", which is the difference between the squared VIX index and expected realized variance. The authors show that the variance premium captures attitudes toward economic uncertainty and predicts future stock returns over short horizons. They develop a generalized long-run risks model that generates a time-varying variance premium consistent with market return and risk-free rate levels. The model requires extensions to match the large size, volatility and skewness of the variance premium and its short-term return predictability. Calibrating the model to cash flow and asset pricing targets allows it to generate the variance premium and its return predictability features.
RAF6,4442Review of Accounting and FinanceVol. 6 No.docxmakdul
RAF
6,4
442
Review of Accounting and Finance
Vol. 6 No. 4, 2007
pp. 442-459
# Emerald Group Publishing Limited
1475-7702
DOI 10.1108/14757700710835087
Alternative evidence on financial
analysts’ use of financial
statement information
Donal Byard
Stan Ross Department of Accounting, Baruch College – CUNY,
New York, New York, USA, and
Fatma Cebenoyan
Department of Economics, Hunter College – CUNY, New York, New York, USA
Abstract
Purpose – Financial analysts are frequently viewed as information intermediaries who process and
interpret firms’ financial reports for other market participants. Much recent research, however, has
cast doubts on analysts’ ability to fully utilize the information in firms’ financial reports. Using an
alternative approach, this study aims to provide evidence on how sophisticated analysts are at using
information in firms’ financial reports.
Design/methodology/approach – The paper estimates different measures of firms’ operational
efficiency, all of which are derived from financial statement data, and compares the strength of the
association between these measures and analysts’ absolute forecast errors. It then compares a
sophisticated frontier-based measure of firms’ operational efficiency that evaluates firms’
performance relative to their competitors with three more traditional efficiency measures;
specifically the return on asset (ROA) ratio, industry-adjusted ROA, and the return on equity ratio.
Findings – The results indicate that the more sophisticated frontier-based measure is more strongly
negatively associated with analysts’ absolute forecast errors than the other three measures. The
results thus suggest that analysts are capable of undertaking a sophisticated analysis of the
information in firms’ financial reports, at least as it pertains to operational efficiency.
Originality/value – To the extent that analysts serve as a key group of users of financial
information, these results are likely to be of interest to accounting policy makers.
Keywords Financial reporting, Financial analysis, Accounting information
Paper type Research paper
1. Introduction
Financial analysts play an important role in financial markets, a role that seems to
have increased in importance in recent years. Analysts are frequently viewed as
information intermediaries who gather, process, and disseminate firm information for
investors (e.g. see Schipper, 1991). Indeed, much of the accounting literature views
analysts as sophisticated agents who process or interpret firms’ disclosures for
investors. Consistent with this view, Lang and Lundholm (1996) document that firms
with higher levels of voluntary disclosure attract a larger analyst following.
The view of analysts as sophisticated information intermediaries can, however, be
challenged. A large body of literature provides evidence that analysts do not efficiently
use all the information contained in firms’ past financial reports. For example, DeBondt
and Thaler (1990) and Abar ...
This research paper discusses enhancing value investment strategies by incorporating expected profitability.
For small cap value strategies, the paper proposes excluding stocks in each country with the lowest direct profitability, with the percentage excluded depending on the stock's price-to-book ratio.
For large cap value strategies, the paper suggests selecting stocks based on both low price-to-book ratios and high direct profitability. It also proposes overweighting stocks that have higher profitability, lower market capitalization, and lower relative price.
The goal is to structure portfolios to better capture the dimensions of expected returns related to company size, relative price, and expected profitability, while maintaining appropriate diversification and managing costs.
This document summarizes a student paper on the low-volatility anomaly. The paper examines whether low-volatility stocks achieve higher risk-adjusted returns compared to predictions of CAPM and MPT. It reviews literature explaining the anomaly through various behavioral biases. The paper tests the anomaly using 30 S&P 500 stocks over 20 years. Regression analysis finds no significant relationship between past stock volatility and future returns, providing no support for either CAPM or the low-volatility anomaly based on the sample. Statistical tests confirm the results and inability to reject the null hypothesis of no relationship between risk and return.
Columbia Business School - RBP MethodologyMarc Kirst
This paper describes a methodology called Required Business Performance (RBP) which uses current stock prices to imply expectations of future sales growth. Section 1 outlines the paper. Section 2 summarizes common approaches to estimating intrinsic firm value from dividends, free cash flows, book values or earnings. Section 3 explains how stock prices reflect both public and private information, and how expectations of key value drivers like future sales can be implied from current market prices.
This document summarizes a study on the determinants of capital structure in Thailand. The study analyzed data on 144 Thai listed companies from 2000 to 2011 to examine how firm-specific factors like size, profitability, asset tangibility, growth opportunities, and volatility influence a company's leverage ratios. The results showed that leverage ratios increased significantly with firm size but decreased significantly with profitability, in line with trade-off and pecking order theories. However, tangibility, growth, and volatility did not have significant relationships with leverage ratios. Therefore, the study concluded that firm size and profitability are the main determinants of capital structure for companies in Thailand.
1. The document analyzes whether systematic rules-based strategies based on traditional and alternative risk factors can successfully replicate the performance of various hedge fund strategies.
2. Regression analysis shows the factors explain a substantial portion of hedge fund returns, though the explanatory power is higher in-sample than out-of-sample. More dynamic strategies are harder to replicate than directional ones.
3. Out-of-sample, a rolling-window approach to estimating time-varying factor exposures works as well or better than a Kalman filter model for most strategies. Replication quality varies by strategy, with more directional strategies like short selling replicating better than dynamic ones.
Arbitrage pricing and investment performance in the Nigerian capital market ...Newman Enyioko
This document summarizes a research paper that applied the Arbitrage Pricing Theory to examine the relationship between investment performance and macroeconomic variables in the Nigerian capital market from 1988 to 2017. The paper used data from five quoted companies to test if inflation, interest rates, exchange rates, money supply, GDP, and treasury bill rates explained investment performance, as measured by earnings per share. The results found that the selected macroeconomic factors did not strongly explain investment performance in the Nigerian capital market, contrary to the objectives of the Arbitrage Pricing Theory. The paper recommends policies to manage market realities and ensure stability to improve investment performance.
The document discusses defining a "Quant Cycle" to capture cyclical behavior in factor returns. The author argues traditional business cycle indicators do not adequately explain factor return variations. Instead, factors seem to follow their own cycle driven by abrupt changes in investor sentiment.
The author proposes a simple 3-stage Quant Cycle model consisting of: 1) a normal stage where factors earn long-term premiums, interrupted by 2) occasional large drawdowns in the value factor due to growth rallies or value crashes, typically lasting 2 years, followed by 3) subsequent reversals where outperforming factors reverse and underperforming factors recover. Empirically, this model captures a large amount of time variation in factor returns compared to traditional frameworks
1) The study examines the economic importance of accounting information by analyzing how accounting data from financial statements can improve portfolio optimization for US equities.
2) Using a parametric portfolio policy method, the researchers modeled portfolio weights as a linear function of three accounting characteristics - accruals, change in earnings, and asset growth - and compared it to weights based on size, book-to-market, and momentum.
3) They found that the accounting-based portfolio generated an out-of-sample annual information ratio of 1.9 compared to 1.5 for the price-based portfolio, indicating accounting information provides valuable signals for optimizing equity investments.
Effect of Portfolio Diversification on Commercial Banks Financial Performance...inventionjournals
The study examined the effect of portfolio diversification on Commercial Banks financial performance. Mixed method of research design was used and data was collected using questionnaires and interview schedules. Target population was 43 licensed Commercial Banks in Kenya from which one hundred and thirty three (133) managers were randomly selected to form sample size. Validity of the research instruments was ensured through content, face and construct validity testing. Data was analyzed using descriptive statistics and inferential statistics which included correlation analysis and bivariate regression analysis. The study established a positive statistically significant relationship between portfolio diversification and financial performance. The portfolio diversification explained 68% of the changes in the financial performance of commercial banks in Kenya and that most banks diversify their investments which has enabled them to increase profits and performance in the past years.The study recommended that financial institutions should invest in a combination of assets which are negatively correlated because this maximizes revenue (returns) and minimizes losses (risks). Further study should be undertaken to establish the best combination of assets that can yield an efficient portfolio.
In Value We Trust Or Not - Industry Report 3Julia Chow
The survey of over 700 accounting professionals found higher trust in financial statements that use both historical cost and fair value accounting (64%) compared to those using only historical cost (53%) or only fair value (39%). Fair value information for trading securities, available for sale securities, and real estate were seen as most useful, while estimates for assets like property, plant, and equipment received less trust. Overall trust in fair value estimates was split between those who trust (31%) and distrust (26%) them. However, skepticism increased for Level 3 estimates (39% distrust) compared to Level 1 and 2 (13-23% distrust). Most respondents believed fair value accounting increases costs but also provides net benefits, with analysts most supportive of net
IFRS 2 (International Accounting and International Financial Reporting Standa...Ro'ya Abd Elhafez
The development of the market economy and the maturity of the security market was accompanied by the increasing reliance of companies on share-based payment, which means that companies give outside parties or employees equity tools in order to obtain goods or services from them. So that the equity tools here are shares, share options, and others. Share-based payments first appeared in the 50s at 20th century in America's high-tech companies. However, the emergence of financial deception by World-Link Communications and World Com, in addition to the famous Enron incident, has made investors doubt and fears about paying on shares. Therefore in 2004, the FASB amended SFAS123 to strictly regulate share-based payment. And The IASB formally issued IFRS 2 (Share-Based Payment), which came into effect in 2005.
Standard & poor's 16768282 fund-factors-2009 jan1bfmresearch
This document summarizes a study by Standard & Poor's on factors that predict investment fund performance. The study analyzed both qualitative factors like fund size, expenses, and age as well as quantitative metrics like Jensen's alpha and information ratio. The key findings were:
- For developed markets, larger funds with lower expenses tended to outperform. But for emerging markets, smaller funds did better due to differences in liquidity.
- Jensen's alpha and information ratio best predicted future performance of developed market equity funds over shorter time periods.
- Past performance was informative over 2 years but less so over 1 year due to noise. Fund selection should focus on factors predicting shorter term outperformance.
This document presents an estimated arbitrage-free model that jointly models nominal and real US Treasury yields. It estimates separate arbitrage-free Nelson-Siegel models for nominal and real yields, finding a three-factor model fits nominal yields well and a two-factor model fits real yields. It then estimates a four-factor joint model that fits both yield curves. The joint model is used to decompose breakeven inflation rates into inflation expectations and inflation risk premium components.
This document summarizes a journal article that examines how stale prices impact the performance evaluation of mutual funds. The article introduces a model to estimate "true alpha" based on the true returns of underlying fund assets, independent of biases from stale pricing. Empirical tests show true alpha is about 40 basis points higher than observed alpha and remains positive on average. The difference between the two alphas consists of three components - a small statistical bias, dilution from long-term fund flows, and a large and significant dilution effect primarily from short-term arbitrage flows exploiting stale prices.
The document summarizes a quantitative relative value model (RVM) that aims to identify potentially undervalued and overvalued stocks. The RVM aggregates valuation measures relative to a stock's long-term history to generate a score between -100 and 100, with negative scores indicating potential undervaluation and positive scores potential overvaluation. Key features include incorporating concepts from the efficient market hypothesis and focusing on factual data to reduce behavioral biases. The model can be applied across developed equity markets and identifies broad market and sector valuation trends for both long and short investment strategies.
This document provides an overview of ratio analysis, outlining various types of ratios used to analyze a company's financial health and performance. It discusses short-term and long-term solvency ratios, profitability ratios, liquidity ratios, efficiency ratios, and valuation ratios. For each ratio type, several example ratios are defined and the factors that influence them are described. Comparative standards and benchmarks for analyzing ratios are also outlined.
Here are the key points about hyperlactatemia in pediatric patients:
- Hyperlactatemia occurs when there is an imbalance between tissue oxygen supply and demand, leading to increased anaerobic glycolysis and lactate production.
- It is commonly seen in pediatric ICU patients, especially following surgery, trauma, or septic shock which cause multiple organ dysfunction.
- Higher lactate levels are associated with worse clinical outcomes and prognosis in critically ill children.
- The PRISM III score, which evaluates the risk of mortality in pediatric ICU patients, was calculated for the patients in this study.
- Treatment aims to support organ function, optimize tissue oxygen delivery, and address any underlying causes contributing to the hyper
Superior performance by combining Rsik Parity with Momentum?Wilhelm Fritsche
This document examines different strategies for global asset allocation between equities, bonds, commodities and real estate. It finds that applying trend following rules substantially improves risk-adjusted performance compared to traditional buy-and-hold portfolios. It also finds trend following to be superior to risk parity approaches. Combining momentum strategies with trend following further improves returns while reducing volatility and drawdowns. A flexible approach that allocates capital based on volatility-weighted momentum rankings of 95 markets produces attractive, consistent risk-adjusted returns.
This study examines factors that affect interest rates in peer-to-peer lending by analyzing data from 2,500 loans through Lending Club. A multiple regression model related interest rate to four factors: FICO score, monthly income, debt-to-income ratio, and length of employment. The analysis found that all factors except length of employment had a statistically significant relationship with interest rate, though the effects were small. FICO score had the strongest negative relationship, while income and debt-to-income ratio had a positive relationship. This suggests credit risk still impacts interest rates in peer-to-peer lending, as do income levels and debt burdens to a lesser degree.
Valuation Insights: Second Quarter 2017Duff & Phelps
This document summarizes key topics from Duff & Phelps' Valuation Insights newsletter, including:
1) A study of over 3,000 fairness opinions filed with the SEC which found that fairness opinions generally provide a useful valuation range for boards, with narrower ranges for larger deals.
2) An article discussing the evolving standards and best practices for valuing fund interests, noting increased focus on transparency and robust valuation processes.
3) A report on global financial regulation which surveyed professionals on the impact and priorities of regulation as well as compliance budgets.
4) Duff & Phelps' acquisition of a leading Asian transfer pricing advisory firm.
Your NamePractical ConnectionYour NameNOTE To insert a .docxnettletondevon
Your Name
Practical Connection
Your Name
NOTE: To insert a different Cover Page select the Insert tab from the Ribbon, then the cover page you want. Insert Your Name. Enter Your Industry and Phase below. You can use this template if you wish. Please erase this note before you submit.
Table of Contents
Phase 1: Educational and Employment History 2
Educational History and Goals (Include Certifications) 2
Employment History and Goals (Do NOT mention the name of the company you are writing about). 2
Phase 2: Telecommunications and Network Security Protocols implemented by your company (Fully describe 3 of the following components. Do NOT mention your company’s name, only the Industry) 2
Hosts and Nodes 2
IPv4 versus IPv6 2
Firewall 2
Virtual Private Network (VPN) 3
Proxy Servers 3
Network Address Translation (NAT) 3
Routers, Switches, and Bridges 3
The Domain Name System (DNS) 3
Intrusion Detection Systems and Intrusion Prevention Systems (IDS/IPS) 3
Network Access Control 3
Phase 3: As a Security Consultant and based on what you have learned in this course, how would you improve your company’s Telecommunications and Network Security Protocols? 3
Improvement 1 3
Improvement 2 3
Improvement 3 3
Bibliography 4
NOTE: To include a Word generated TOC select the References tab from the Ribbon, then Table of Contents. Select the format you wish. Remember, to use the built-in TOC you must use the MS Word “Styles” format from the Ribbon, specifically “Heading 1” for each phase heading, “Heading 2” for the phase sub-headings and “Normal” for the body.
Remember to update the TOC after adding any data to the body of the paper. To update the TOC simply click anywhere in the TOC, select Update Table, then select Update entire table and OK.
Please erase this note before you submit.Phase 1: Educational and Employment HistoryEducational History and Goals (Include Certifications)
Type Your Data Here.
NOTE: For each Phase you must have at least 2 references. Please use the References feature of Microsoft Word to manage your references.
To add a reference to the database do the following:
Select References from the Ribbon
Select Style, then APA
Select Insert Citation
Select Add New Source
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Fill in the required information, select OK
To insert a reference from the database do the following:
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Please erase this note before you submit.Employment History and Goals (Do NOT mention the name of the company you are writing about).
Type Your Data Here. Type Your Data Here. Type Your Data Here. Type Your Data Here. Type Your Data Here.Phase 2: Telecommunications and Network Security Protocols implemented by your company (Fully describe 3 of the following components. Do NOT mention your company’s name, only the Industry)Hosts and Nodes
Type Your Data Here. Type Your Data Here. Type Your Da.
Your namePresenter’s name(s) DateTITILE Motivatio.docxnettletondevon
Your name:
Presenter’s name(s):
Date:
TITILE:
Motivation(s)/Statement of problem(s):
Objective(s):
Approach(s):
a. Materials:
b. Methods:
Findings:
Conclusions
LETTERS
nature materials | VOL 3 | APRIL 2004 | www.nature.com/naturematerials 249
T issue engineering aims to replace, repair or regeneratetissue/organ function, by delivering signalling molecules andcells on a three-dimensional (3D) biomaterials scaffold that
supports cell infiltration and tissue organization1,2. To control cell
behaviour and ultimately induce structural and functional tissue
formation on surfaces, planar substrates have been patterned with
adhesion signals that mimic the spatial cues to guide cell attachment
and function3–5. The objective of this study is to create biochemical
channels in 3D hydrogel matrices for guided axonal growth. An agarose
hydrogel modified with a cysteine compound containing a sulphydryl
protecting group provides a photolabile substrate that can be
patterned with biochemical cues. In this transparent hydrogel we
immobilized the adhesive fibronectin peptide fragment, glycine–
arginine–glycine–aspartic acid–serine (GRGDS),in selected volumes of
the matrix using a focused laser.We verified in vitro the guidance effects
of GRGDS oligopeptide-modified channels on the 3D cell migration
and neurite outgrowth. This method for immobilizing biomolecules in
3D matrices can generally be applied to any optically clear hydrogel,
offering a solution to construct scaffolds with programmed spatial
features for tissue engineering applications.
Hydrogels have been widely studied as tissue scaffolds because they
are biocompatible and non-adhesive to cells, allowing cell adhesion
to be programmed in6–8. Current microfabrication methods for
3D hydrogel matrices with controlled intrinsic structure mainly
include photolithographic patterning9–11, microfluidic patterning12,
electrochemical deposition13 and 3D printing14. Notably, although these
layering techniques can conveniently shape the hydrogel on X–Y planes,
they have limited control over both the coherence of the layers along the
z direction and the local chemistry. Combining photolabile hydrogel
matrices with focused light provides the possibility of eliminating the
layering process and directly modifying the local physical or chemical
properties in 3D. This results in a promising (and perhaps facile) way to
fabricate novel tissue constructs15,16, as is described herein to control cell
behaviour by controlling the local chemical properties of gels.
Reconstituting adhesive biomolecules into biomaterials is of great
importance to understanding cell–substrate interactions that can be
translated to tissue-regeneration designs. Using 2D lithographic
techniques, adhesive biomolecules can be localized in arbitrary shapes
and sizes17,18. For example, patterning narrow strips of the extracellular
matrix (ECM) adhesion protein, laminin, on non-cell-adhesive 2D
substrates elicited.
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Your NamePractical ConnectionYour NameNOTE To insert a .docxnettletondevon
Your Name
Practical Connection
Your Name
NOTE: To insert a different Cover Page select the Insert tab from the Ribbon, then the cover page you want. Insert Your Name. Enter Your Industry and Phase below. You can use this template if you wish. Please erase this note before you submit.
Table of Contents
Phase 1: Educational and Employment History 2
Educational History and Goals (Include Certifications) 2
Employment History and Goals (Do NOT mention the name of the company you are writing about). 2
Phase 2: Telecommunications and Network Security Protocols implemented by your company (Fully describe 3 of the following components. Do NOT mention your company’s name, only the Industry) 2
Hosts and Nodes 2
IPv4 versus IPv6 2
Firewall 2
Virtual Private Network (VPN) 3
Proxy Servers 3
Network Address Translation (NAT) 3
Routers, Switches, and Bridges 3
The Domain Name System (DNS) 3
Intrusion Detection Systems and Intrusion Prevention Systems (IDS/IPS) 3
Network Access Control 3
Phase 3: As a Security Consultant and based on what you have learned in this course, how would you improve your company’s Telecommunications and Network Security Protocols? 3
Improvement 1 3
Improvement 2 3
Improvement 3 3
Bibliography 4
NOTE: To include a Word generated TOC select the References tab from the Ribbon, then Table of Contents. Select the format you wish. Remember, to use the built-in TOC you must use the MS Word “Styles” format from the Ribbon, specifically “Heading 1” for each phase heading, “Heading 2” for the phase sub-headings and “Normal” for the body.
Remember to update the TOC after adding any data to the body of the paper. To update the TOC simply click anywhere in the TOC, select Update Table, then select Update entire table and OK.
Please erase this note before you submit.Phase 1: Educational and Employment HistoryEducational History and Goals (Include Certifications)
Type Your Data Here.
NOTE: For each Phase you must have at least 2 references. Please use the References feature of Microsoft Word to manage your references.
To add a reference to the database do the following:
Select References from the Ribbon
Select Style, then APA
Select Insert Citation
Select Add New Source
Select Type of Source
Fill in the required information, select OK
To insert a reference from the database do the following:
Place the cursor just before the period at the end of the sentence. Then select Insert Citation and select the correct reference from the list (Sewart, 2014).
Please erase this note before you submit.Employment History and Goals (Do NOT mention the name of the company you are writing about).
Type Your Data Here. Type Your Data Here. Type Your Data Here. Type Your Data Here. Type Your Data Here.Phase 2: Telecommunications and Network Security Protocols implemented by your company (Fully describe 3 of the following components. Do NOT mention your company’s name, only the Industry)Hosts and Nodes
Type Your Data Here. Type Your Data Here. Type Your Da.
Your namePresenter’s name(s) DateTITILE Motivatio.docxnettletondevon
Your name:
Presenter’s name(s):
Date:
TITILE:
Motivation(s)/Statement of problem(s):
Objective(s):
Approach(s):
a. Materials:
b. Methods:
Findings:
Conclusions
LETTERS
nature materials | VOL 3 | APRIL 2004 | www.nature.com/naturematerials 249
T issue engineering aims to replace, repair or regeneratetissue/organ function, by delivering signalling molecules andcells on a three-dimensional (3D) biomaterials scaffold that
supports cell infiltration and tissue organization1,2. To control cell
behaviour and ultimately induce structural and functional tissue
formation on surfaces, planar substrates have been patterned with
adhesion signals that mimic the spatial cues to guide cell attachment
and function3–5. The objective of this study is to create biochemical
channels in 3D hydrogel matrices for guided axonal growth. An agarose
hydrogel modified with a cysteine compound containing a sulphydryl
protecting group provides a photolabile substrate that can be
patterned with biochemical cues. In this transparent hydrogel we
immobilized the adhesive fibronectin peptide fragment, glycine–
arginine–glycine–aspartic acid–serine (GRGDS),in selected volumes of
the matrix using a focused laser.We verified in vitro the guidance effects
of GRGDS oligopeptide-modified channels on the 3D cell migration
and neurite outgrowth. This method for immobilizing biomolecules in
3D matrices can generally be applied to any optically clear hydrogel,
offering a solution to construct scaffolds with programmed spatial
features for tissue engineering applications.
Hydrogels have been widely studied as tissue scaffolds because they
are biocompatible and non-adhesive to cells, allowing cell adhesion
to be programmed in6–8. Current microfabrication methods for
3D hydrogel matrices with controlled intrinsic structure mainly
include photolithographic patterning9–11, microfluidic patterning12,
electrochemical deposition13 and 3D printing14. Notably, although these
layering techniques can conveniently shape the hydrogel on X–Y planes,
they have limited control over both the coherence of the layers along the
z direction and the local chemistry. Combining photolabile hydrogel
matrices with focused light provides the possibility of eliminating the
layering process and directly modifying the local physical or chemical
properties in 3D. This results in a promising (and perhaps facile) way to
fabricate novel tissue constructs15,16, as is described herein to control cell
behaviour by controlling the local chemical properties of gels.
Reconstituting adhesive biomolecules into biomaterials is of great
importance to understanding cell–substrate interactions that can be
translated to tissue-regeneration designs. Using 2D lithographic
techniques, adhesive biomolecules can be localized in arbitrary shapes
and sizes17,18. For example, patterning narrow strips of the extracellular
matrix (ECM) adhesion protein, laminin, on non-cell-adhesive 2D
substrates elicited.
Your nameProfessor NameCourseDatePaper Outline.docxnettletondevon
Your name
Professor Name
Course
Date
Paper Outline
Thesis: Thesis statement here
I. Rough draft of introduction to essay/paper
II. First Major Point
A. Secondary point
B. Secondary point
C. Transition sentence into next paragraph
III. Second Major Point
A. Secondary point
B. Secondary point
C. Transition sentence into next paragraph
IV. Third Major Point
A. Secondary point
B. Secondary point
C. Transition sentence into next paragraph
(If there are more points, add them as items V, VI, etc. appropriately)
1
V. Rough draft of conclusion of essay/paper
A. Summary of discussion
B. Final observations
Works Cited
Livingston, James C. Anatomy of the Sacred: An Introduction to Religion. 6th ed. Upper Saddle River, N.J.: Pearson/Prentice Hall, 2009.
Rodrigues, Hillary, and John S. Harding. Introduction to the Study of Religion. Routledge, 2009.
.
Your name _________________________________ Date of submission _.docxnettletondevon
Your name: _________________________________ Date of submission: ______________________
ENG201 Milestone 4: #MyWordsChangeLives Project Outline
#MyWordsChangeLives Project Outline
#wordschangelives
Instructions: Save this document on your own computer. Type into each box and expand it as needed for the length of your response. Answer thoroughly!
PART 1: PERSONAL REFLECTION
TOPIC: What is one problem, issue, or need in the world, or in your own community, that you care a lot about?
PERSONAL CONNECTION: Why is this particular issue important to you? Is there something in your life experience or academic studies that relates?
ROOT CAUSE HYPOTHESIS: What do you think are some of the root causes of this issue? Explain.
AUDIENCE HYPOTHESIS: Based on the causes you have identified, who would be a good audience for you to try to make a change on this issue? Why?
RESEARCH QUESTIONS: The next step is research, What are 3 questions related to your issue that you want to answer during your research? Think of information that might help you better understand the issue in order to address or solve it.
PART 2: RESEARCH SUMMARY
SOURCE #1: Include APA-formatted citation here, including link if applicable:
How can you tell that this is a reliable source?
In this column, make a list of the most important facts or statistics you learned from this source:
In this column, explain in your own words why the facts you included to the left are important:
What was the most important thing you learned from this source? Why?
SOURCE #2: Include APA-formatted citation here, including link if applicable:
How can you tell that this is a reliable source?
In this column, make a list of the most important facts or statistics you learned from this source:
In this column, explain in your own words why the facts you included to the left are important:
What was the most important thing you learned from this source? Why?
SOURCE #3: Include APA-formatted citation here, including link if applicable:
How can you tell that this is a reliable source?
In this column, make a list of the most important facts or statistics you learned from this source:
In this column, explain in your own words why the facts you included to the left are important:
What was the most important thing you learned from this source? Why?
PART 3: PROJECT PLANNING OUTLINE
CREATE YOUR OWN TEXT-BASED CAMPAIGN!
Start outlining the components of your final project here.
You will explain each choice in greater detail and polished prose for your final project.
Headline: What is the “headline” of your campaign? What phrase or hashtag will you use? Why those words?
Message: What is the subtext of the campaign? In other words, what messages are you communicating by the headline?
Audience: With whom is your campaign de.
Your NameECD 310 Exceptional Learning and InclusionInstruct.docxnettletondevon
Your Name
ECD 310: Exceptional Learning and Inclusion
Instructor
Date
Inclusive and Differentiated Learning and Assessments
Hint 1: This template is intended to guide you; however, you’re encouraged to add or delete from this format as long as your final product aligns with the assignment requirements found under Week 3>Assignment.
Hint 2: Delete these highlighted “hints” before final submission.
Hint 3: Delete the prompt text included on each slide and replace it with your own content.
Only use this template if you are enrolled in the Bachelor of Arts in Early Childhood Education
Introduction
On this slide, provide a brief introduction to the topic of standardized assessment.
Hint: For help creating and editing slides in PowerPoint, see this guide on Creating PowerPoint Presentations.
Including All Students
On this slide, describe how you will ensure that all students are included in assessments and how you will make decisions about how children participate in assessments.
Accessibility for All
Summarize how you will make sure that the assessments are designed for accessibility by all.
Ensuring Fairness and Validity
Explain how you will make sure the assessment results are fair and valid.
Reporting the Results
Describe the importance of reporting the results of the assessment for all students.
Evaluating the Process
Examine how you will continually evaluate the assessment process to improve it and ensure student success.
Hint: Use scholarly sources in your presentation to support your ideas. Remember to include in-text citations.
Rationale
Explain your rationale, based on the age of children you plan to work with, the reasons why you would use standardized assessments.
Some reasons might be programmatic planning, differentiating instruction, identifying individual needs, and ensuring alignment with standards.
Hint: Make sure to support your reasoning with at least one scholarly source.
Collaboration
Discuss how, as an early childhood educator, you will collaborate with your colleagues to differentiate the assessment tools you will use to support the children you work with.
Conclusion
Include a brief conclusion to bring closure to your presentation.
.
Your Name University of the Cumberlands ISOL634-25 P.docxnettletondevon
The document discusses defensible space and crime prevention through environmental design (CPTED). It defines defensible space as using barriers and surveillance to control an environment and divide it into zones. CPTED is defined as a multidisciplinary approach to reducing crime through the design of environments in a way that improves safety and allows for better physical and operational controls.
Your Name Professor Name Subject Name 06 Apr.docxnettletondevon
Your Name:
Professor Name:
Subject Name:
06 April 2019
Active exhibition
For most people, a hospital is a place that we don't want to go, but we may have to go if
we get ill. Pain and death brought by diseases terrify us, which make us avoid thinking
of a hospital, not to mention visiting a hospital if not necessary. As for me, a hospital is a
special place. My father is a doctor who helps thousands of patients get well. I spent my
childhood watching him cure patients and bring happiness back to their life. A hospital
represents hope and wellness to patients and their loved ones, and we cannot simply
correlate it with the negative image brought by diseases, form an idea for illness and
even hospital fear. I want to propose a series of exhibitions to awaken people's outdated
and even prejudiced views, just like “A Hacker Manifesto” taught us. We need to bring
this spirit to break the perception in the traditional sense. This exhibition, I hope to let
patients or visitors think more deeply about what disease or disability has brought us.
Inspired by ‘A Hacker Manifesto’, I want to subvert mundane ideas and provide a
completely new experience to hospital visitors through this exhibition. Many relate their
past bad experiences and sad stories with hospitals. Thus, they hold a negative and
prejudice attitude toward the hospital and refuse to change. In this exhibition, I will
present the ‘hope’ and ‘wellness’ side of the hospital. Instead of breaking us down, a
hospital is protecting us from losing health or even life. Also, I want to exhibit the
optimism and fortitude the patients have when they fight against diseases. The shining
qualities they maintain to win the battle of life are so inspiring. We can understand the
meaning of life better from the hospital exhibition.
To organize an impressive exhibition, I choose a comprehensive hospital with a large
amount of patients. In this way, more people will be attracted to the exhibition in the
hospital than in smaller hospitals. They can enjoy the exhibition works when they wait in
line. There are many kinds of patients in general hospitals. I hope to bring some new
concept or idea to the patient.
After comparing several local hospitals in San Francisco, I decided to choose the
hospital in Kaiser Permanente. Kaiser's hospitals are widely distributed, and almost all
of California's medical systems are involved. Exhibitions can have more widely flowed,
and the community around Kaiser is rich. There are companies as well as residential
areas and even schools. The success of the exhibition can benefit the surrounding
communities more broadly.
Kaiser Permanente Campus in San Francisco
For a specific location, I chose the Kaiser Permanente San Francisco Medical Center
and Medical Offices (2425 Geary Blvd, San Francisco, CA 94115). In the lobby of the
entrance, you can see a very wide area, on the righ.
Your muscular system examassignment is to describe location (su.docxnettletondevon
Your muscular system exam/assignment
is to describe location (superior & inferior attachments, action and innervations of the following muscles: please make sure to describe that mentioned above on each muscles.
Deltoid
Triceps brachii
Biceps brachii
Coracobrachialis
Brachialis
Brachioradialis
Sternocleidomastoid
Trapezius
Latissimus Dorsi
Supraspinatus
Infraspinatus
Subscapularis
Sartorius
Iliotibial tract/band
Tensor Fascia Lata
Describe glenohumeral joint (anatomy, ligaments, and movements at this articulation).
.
Your midterm will be a virtual, individual assignment. You can choos.docxnettletondevon
Your midterm will be a virtual, individual assignment. You can choose one of the following to complete:
-Website (sites.google.com or wordpress.com)
-Blog (blogger.com or tumblr)
-Vlog
You have to find a way to tie in
ALL
of the following topics in your multimedia midterm project:
-Cellular Reproduction
-Meiosis
-DNA structure/Function
-Bacteria and Archaea
-Protists
You'll either have to explain your information at an elementary, lay (someone not familiar with science), or the scientific level.
Your midterm project will be due on February 26, 2020 at 11:59 pm.
In your project you aren’t giving definitions, you’re explaining in a unique way how all the topics tie in together. If you choose elementary you need to be creative and engaging as they have a short attention span and have little to no knowledge of science. For the lay audience you’ll need to relate it to the real world or real world events. Think of this audience as explaining these subjects to your mother or grandmother. For the scientific audience, you must use scientific language and present your information in a matter of fact way. This requires an innovative mindset.
.
Your local art museum has asked you to design a gallery dedicated to.docxnettletondevon
Your local art museum has asked you to design a gallery dedicated to works of art from one of the following movements:
Modernism
You may use Word or PowerPoint to design your gallery.
You will design your gallery as if you were guiding a visitor to each work of art.
In your gallery, include the following:
A brief introduction to your gallery, which includes a description of the movement and the time period to which your gallery is dedicated.
Six images of works of art that incorporate the characteristics significant to movement and time period. Along with each image of a work of art, include the citation for the work of art. A summary of how the media (materials), methods, and subject are significant to that time period and region, using appropriate art terminology.
A summary of how iconographic, historical, political, philosophical, religious, and social factors of the movement are reflected in the work of art.
Make use of at least three scholarly sources
Cite your sources
.
Your letter should include Introduction – Include your name, i.docxnettletondevon
Your letter should include:
Introduction – Include your name, if you are a full-time or part-time student, your program name and your semester of study.
Body of letter – Why do you think you qualify for an award? Include your volunteer work within the community.
Conclusion – Show your appreciation for being considered and include how receiving an award will assist with your education.
.
Your legal analysis should be approximately 500 wordsDetermine.docxnettletondevon
Your legal analysis should be approximately 500 words
Determine whether Mr. Johnson discriminated against Ms. Djarra based on religion.
Discuss whether Mr. Johnson offered reasonable accommodations to Ms. Djarra.
Identify the amount and type of damages to be awarded, if any.
The Religious Discrimination – Reasonable Accommodations analysis
Tip for what I need for the analysis section: An analysis section draws meaning from the events that occurred. Go in depth about the implications of their viewpoints or actions.
.
Your Last Name 1Your Name Teacher Name English cl.docxnettletondevon
Your Last Name 1
Your Name
Teacher Name
English class number
Due Date
Title
Start typing here. Delete the notes below after you read through them.
Indent each paragraph and use double spacing and the following formatting:
1 inch margins
Times New Roman
12 point font type
DO NOT use any of the following:
NO border,
NO word art,
NO drawings,
NO ALL CAPS,
NO exclamation points!,
Your Last Name 2
NO underlining,
NO bold,
NO italics (except for references to literature)
NO different font types, sizes or colors.
.
Your job is to delegate job tasks to each healthcare practitioner (U.docxnettletondevon
Delegate tasks to healthcare practitioners during the day shift by filling out a staffing table or describing each person's tasks. Use a primary, team, or modular nursing staffing model to help make delegation decisions. Follow APA style guidelines by typing responses into a Microsoft Word document and uploading the completed staffing table or document.
Your job is to look at the routing tables and DRAW (on a piece of pa.docxnettletondevon
Your job is to look at the routing tables and DRAW (on a piece of paper) the topology based on the information in the routing tables. All of the LANS have the first address (.1). Your deliverable is to draw the topology, with the router names, with the interface names and addresses based on the information given. Please take a picture of your drawing and attach it to the dropbox.
I already did this assignment. i am attaching my work also, i am so confused about these ports. i am attaching, my professors note as well. PLEASE READ IT CAREFULLY. and fix it
you did not list the serial ports correctly. The serial ports are what connect the routers together. 2 connecting serial ports will have addresses on the SAME network. The serial port does not stick out of the router like the LANs, the serial ports connect the routers to each other.
.
Your job is to design a user interface that displays the lotto.docxnettletondevon
Your job is to design a user interface that displays the lotto balls that are drawn when drawing up to balls from 5 total of 30 balls.
Use 5 image elements to display the ball images from this zipfile:
lottoballs.zip
(I WILL ATTACH THE FILE)
Use a button to perform the drawing.
Use a Lotto class object in the script lotto-class.js to simulate drawing the balls.
Use a CSS file to set the fonts, colors, and sizes of the elements on your page.
Include a link back to your index page. ** ONLY SHOW FIVE BALLS IN HTML
The Lotto class object draws the balls with replacement and sorts them in numeric order before outputting them.
Allow the user to choose how many balls from which to draw and how many balls to draw. This provides a variety of Lotto games to play.
.
Your Introduction of the StudyYour Purpose of the stud.docxnettletondevon
Your
Introduction
of the Study
Your
Purpose
of the study
Your
Methodology
Add your ethical considerations for the survey to your Methodology
Add your measurement strategy to your Methodology
Include a copy of the questionnaire or survey in the Methodology
Provide your
Data Analysis
with survey results
Data results should be provided in graphic form, making them user-friendly information
Provide your
Conclusion
regarding the study. Be sure to tell how well you answered your research question, the status of your hypothesis (true/false), and the value of your survey results for your topic moving forward
USE the attached paper to complete final.
.
Your instructor will assign peer reviewers. You will review a fell.docxnettletondevon
Your instructor will assign peer reviewers. You will review a fellow student's Week 1 materials and provide substantive and constructive feedback to them on the direction for their final paper (250 word minimum). Is something useful missing from the outline? Do you know additional sources (or places to find good sources) the person might want to include? Do you understand clearly his or her topic and thesis?
Fellow Student week I material:
Title of Paper: Long Term Effects of Child Abuse and Neglect.
Introduction:
The voice that is hardly heard. Child abuse and neglect have become predators within human history. As time has passed the outstanding cases that have come about over the many years have raised many eyebrows and society has become appreciative to the revilement of these evil acts within all communities. Child abuse and neglect can take place in a home as well as outside a home places many couldn’t even imagine such as within our school system as well as playgrounds. Even though many times these evil acts take place within a home it can be done by family, friends and acquaintances of the child. Child abuse and neglect can be performed in various ways such as neglect, physical abuse, sexual abuse, psychological abuse and emotional abuse.
Direct Statement and Research Question:
The voice that is hardly heard. Can child abuse and child neglect affect an individual?
Proposal:
The paper that I am presenting to you today will explore the aspects of child abuse, child neglect, effects of the abuse, signs of abuse, signs of neglect, symptoms, risk factors, treatment and prevention. Individuals have their own presumptions of their definition of child abuse as well as child neglect. Some of those presumptions that I have heard were the failure to provide enough love to a child, the failure to provide enough necessities to a child. Child neglect and abuse goes deeper than this the emotional neglect, physical neglect and medical neglect. Where a child sustained physical injuries due to the act of hitting, shaking, burning and kicking describes physical abuse. Sexual activity that the child cannot consent of or comprehend refers to sexual abuse. These acts involve anal and genital intercourse, oral contact, and fondling. Emotional as well as psychological abuse involves those words of putting children down, vulgar language, screaming and yelling can all involve emotional as well as psychological abuse towards a child.
Methodology and Data:
I plan on delivering my methodology through statistics such as research journals and individuals in society that also work with children who have been abused as well as neglected such as interviewing social workers, teachers, health professionals and individuals within society. Understanding that many abused children do not come forward because of that fear that has been placed in them. The fear of becoming the blame, the fear of being rejected or refused, the fear of the blame and the fear of being ashamed so.
Your initial reading is a close examination of the work youve c.docxnettletondevon
Your initial reading is a close examination of the work you've chosen before you read about it. In order to describe what you see, you might consider:
What do you notice first? Why? What do the colors convey? How? How is the space occupied? Is there a foreground and a background (2D) or is the piece sculptural (3D) with mass and volume? Is there an implied shape, such as a triangle, square, or circle, that brings balance to the composition? Are there diagonal lines that make it dynamic?
Next, read the materials provided about the work of art. You are welcome to do additional research on the internet as long as you use reputable websites, such as those from museums and art publications. Go back to your piece and take an even closer look. Think about what you've read and what you see. How does its meaning deepen from additional information the work of art?
Then, consider how the formal elements play into the artist's intention or audience's interpretation of the work. Making connections and observations about form and content are the key to writing a strong analysis. Remember to cite as appropriate.
Include several of areas from the first and second points to bring you to the third point.
1. Initial Reading (what do you see and understand when you first look at the work?)
Medium (materials)
Formal Elements
Subject
2. Contextual Research
Content
History
Emphasis
Effect
Symbolism
Relevance
Political Parallels
Social Implications
Audience?
Influences?
Captions/Title/Text
Ethical/Logical/Emotional Appeal?
3. Meaning
Bring it together. What does the work of art mean? Develop a persuasive, cohesive analysis that includes what you see through form and context.
.
Your initial posting must be no less than 200 words each and is due .docxnettletondevon
Your initial posting must be no less than 200 words each and is due
no later than Wednesday 11:59 PM EST/EDT.
The day you post this will count as one of your required four unique postings.
Identify the standard that courts use to qualify someone as an expert witness. Then discuss the standards used to allow that individual's testimony in court. Here, you will want to refer to the Federal Rules of Evidence as well as the Daubert Standard and several other important landmark cases. Include in your response the Saint Leo core value of integrity.
Saint Leo Core Value of Integrity:
The commitment of Saint Leo University to excellence demands that its members live its mission and deliver on its promise. The faculty, staff, and students pledge to be honest, just, and consistent in word and deed.
.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Accounting Research Center, Booth School of Business, Universi.docx
1. Accounting Research Center, Booth School of Business,
University of Chicago
Comparing the Accuracy and Explainability of Dividend, Free
Cash Flow, and Abnormal
Earnings Equity Value Estimates
Author(s): Jennifer Francis, Per Olsson and Dennis R. Oswald
Source: Journal of Accounting Research, Vol. 38, No. 1 (Spring,
2000), pp. 45-70
Published by: Wiley on behalf of Accounting Research Center,
Booth School of Business,
University of Chicago
Stable URL: http://www.jstor.org/stable/2672922
Accessed: 11-09-2016 22:17 UTC
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access to Journal of Accounting Research
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2016 22:17:51 UTC
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Journal of Accounting Research
Vol. 38 No. 1 Spring 2000
Printed in US.A.
Comparing the Accuracy and
Explainability of Dividend, Free
Cash Flow, and Abnormal Earnings
Equity Value Estimates
JENNIFER FRANCIS,* PER OLSSON,t
AND DENNIS R. OSWALD:
1. Introduction
This study provides empirical evidence on the reliability of
intrinsic
value estimates derived from three theoretically equivalent
valuation
models: the discounted dividend (DIV) model, the discounted
free cash
3. flow (FCO) model, and the discounted abnormal earnings (AE)
model.
We use Value Line (VL) annual forecasts of the elements in
these models
to calculate value estimates for a sample of publicly traded
firms fol-
lowed by Value Line during 1989-93.1 We contrast the
reliability of value
*Duke University; tUniversity of Wisconsin; London Business
School. This research
was supported by the Institute of Professional Accounting and
the Graduate School of
Business at the University of Chicago, by the Bank Research
Institute, Sweden, and Jan
Wallanders och Tom Hedelius Stiftelse for
Samhallsvetenskaplig Forskning, Stockholm,
Sweden. We appreciate the comments and suggestions of
workshop participants at the
1998 EAA meetings, Berkeley, Harvard, London Business
School, London School of Eco-
nomics, NYU, Ohio State, Portland State, Rochester,
Stockholm School of Economics,
Tilburg, and Wisconsin, and from Peter Easton, Frank Gigler,
Paul Healy, Thomas Hem-
mer, Joakim Levin, Mark Mitchell, Krishna Palepu, Stephen
Penman, Richard Ruback,
4. Linda Vincent, Terry Warfield, and Jerry Zimmerman.
I We collect third-quarter annual forecast data over a five-year
forecast horizon for all
December year-end firms followed by VL in each of the years
1989-93. After excluding
firms with missing data, the final sample contains between 554
and 607 firms per year
(2,907 observations in the pooled sample).
45
Copyright ?, Institute of Professional Accounting, 2000
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2016 22:17:51 UTC
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46 JOURNAL OF ACCOUNTING RESEARCH, SPRING 2000
estimates in terms of their accuracy (defined as the absolute
price scaled
difference between the value estimate and the current security
price)
and in terms of their explainability (defined as the ability of
value esti-
mates to explain cross-sectional variation in current security
prices).
5. In theory, the models yield identical estimates of intrinsic
values; in
practice, they will differ if the forecasted attributes, growth
rates, or dis-
count rates are inconsistent.2 Although by documenting
significant dif-
ferences across DIM FCF, and AE value estimates our results
speak to the
consistency question, our objective is to present a pragmatic
exercise
comparing the reliability of these value estimates, recognizing
that the
forecasts underlying them may be inconsistent. That is, we try
to repli-
cate the typical situation facing an investor using a valuation
model to
calculate an estimate of the intrinsic value of a firm. Under this
view, the
empirical work addresses which series of forecasts investors
seem to use
to value equity securities.
The results show that AE value estimates perform significantly
better
than DIV or FCF value estimates. The median absolute
prediction error
6. for the AE model is about three-quarters that of the FCF model
(30%
versus 41%) and less than one-half that of the DIVmodel (30%
versus
69%). Further, AE value estimates explain 71% of the variation
in cur-
rent prices compared to 51% (35%) for DIV (FCF) value
estimates. We
conclude that AEvalue estimates dominate value estimates
based on free
cash flows or dividends.
Further analyses explore two explanations for the superiority of
AE
value estimates. AEvalue estimates may be superior to DIVand
FCFvalue
estimates when distortions in book values resulting from
accounting pro-
cedures and accounting choices are less severe than forecast
errors and
measurement errors in discount rates and growth rates. This
effect is
potentially large for our sample, as indicated by the high
proportion of
AE value estimates represented by book value of equity (72%
on aver-
7. age) and the high proportion of FCF and DIV value estimates
repre-
sented by terminal values (82% and 65%, on average, versus
21% for AE
value estimates).3 Value estimates may also differ when the
precision and
the predictability of the fundamental attributes themselves
differ. 4 Cet-
eris paribus, more precise and more predictable attributes
should result
in more reliable value estimates. Tests of these conjectures
suggest that
the greater reliability of AE value estimates is driven by the
ability of
2For example, inconsistencies arise if the attributes violate
clean surplus, if discount
rates violate the assumptions of no arbitrage, unlimited
borrowing, and lending at the rate
of return, or if growth rates are not constant (i.e., the firm is
not in steady state).
3We focus on the terminal value calculation because it is likely
the noisiest component
of the value estimate, reflecting errors in forecasting the
attribute itself, the growth rate,
and the discount factor.
4We define precision as the absolute difference between the
predicted value of an attri-
bute and its realization, scaled by share price. We define
8. predictability as the ease with
which market participants can forecast the attribute, and we
measure this construct as the
standard deviation of historical year-to-year percentage
changes in the attribute.
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COMPARING EARNINGS EQUITY VALUE ESTIMATES 47
book value to explain a large portion of intrinsic value and,
perhaps, by
the greater precision and predictability of AE forecasts.
Moreover, nei-
ther accounting discretion nor accounting conservatism has a
significant
impact on the reliability of AEvalue estimates, suggesting that
the supe-
riority of the AE measure is robust to differences in firms'
accounting
practices and policies.
To our knowledge, this is the first study to provide large-
sample evi-
9. dence on the relative performance of these models using
individual se-
curity value estimates based on forecast data. As discussed in
section 2,
Penman and Sougiannis [1998] (henceforth PS) provide
empirical eval-
uations of these models for a large sample of firms, for
portfolio value
estimates based on realized attributes. The forecast versus
realization dis-
tinction is important because realizations contain unpredictable
compo-
nents which may confound comparisons of the valuations
models (which
are based on expectations).5 PS use a portfolio design to
average out the
unpredictable components of the valuation errors, whereas the
use of
forecasts avoids this problem entirely and permits a focus on
individual
securities' valuation errors. Another important difference
between the
two studies concerns the performance metrics: bias in PS and
accuracy
10. and explainability in our study. PS focus on bias (we believe)
because
their portfolio approach is better suited to describing the
relation be-
tween value estimates and observed prices for the market as a
whole.
Specifically, under a mean bias criterion, positive and negative
predic-
tion errors offset within and across portfolios to yield estimates
of the net
amount that portfolio value estimates deviate from observed
prices. In
our individual security setting, we have no reason to believe
that indi-
vidual shareholders care about net prediction errors or care
more (or
less) about over- versus undervaluations of the same amount.
Thus, we
believe accuracy rather than bias better reflects the loss
function of an
investor valuing a given security. Explainability is also an open
question
in an individual security setting but is not well motivated in a
portfolio
11. setting where the random assignment of securities to portfolios
and the
aggregation of value estimates and observed prices within the
portfolio
significantly reduce the variation in these variables.
Our final analysis links the two studies by examining whether,
for our
sample, their design yields the same results as our approach.
We draw the
same conclusion as PS concerning bias in portfolio prediction
errors
based on realizations: AEvalue estimates have smaller (in
absolute terms)
5Realizations and forecasts also differ because realizations
generally adhere to clean
surplus, but forecasted attributes may not. Over two-thirds of
the sample forecasts adhere
to clean surplus in years 0, 1, and 3 but not in years 2, 4, and 5
because of the assumptions
used to construct a series of five-year forecasts (described in
section 3). We do not believe
the differences across value estimates documented in this study
are driven by violations of
clean surplus both because the violations of clean surplus are
12. modest relative to the docu-
mented absolute prediction errors and because we find similar
patterns when we repeat
our analyses using a one-year forecast horizon and include only
those securities' forecasts
which adhere to clean surplus.
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48 J. FRANCIS, P. OLSSON, AND D. R. OSWALD
bias than FCF or DIV value estimates. However, when
forecasts rather
than realizations are used to calculate value estimates, this
ordering de-
pends on the assumed growth rate: for g= 0% we find the same
ranking,
but for g = 4% we find that FCF value estimates have the
smallest (abso-
lute) bias, followed by AE and DJVvalue estimates.6 In terms
of accuracy,
we find that AE value estimates generally outperform FCF and
DIV value
estimates regardless of whether forecasts or realizations are
13. used. Abso-
lute prediction errors are, however, significantly (at the .00
level) smaller
when forecasts rather than realizations are used to calculate
value esti-
mates. The forecast versus realization distinction is also
important for
comparing DIV and FOF value estimates. While we find that
FEF value es-
timates based on realizations are more biased than DIV value
estimates
based on realizations (consistent with PS), we also find that
FCFvalue es-
timates based on forecasts dominate DIVvalue estimates based
on fore-
casts in terms of both bias and accuracy.
Section 2 describes the three valuation models and reviews the
results
of prior studies' investigations of estimates derived from these
models.
Section 3 describes the sample and data and presents the
formulations
of the DIV, FCF, and AE models we estimate. The empirical
tests and re-
sults are reported in section 4, and section 5 reports the results
14. of apply-
ing PS's design to our sample firms. Section 6 summarizes the
results
and concludes.
2. Valuation Methods
2.1 MODELS
The three equity valuation techniques considered in this paper
build
on the notion that the market value of a share is the discounted
value of
the expected future payoffs generated by the share. Although
the three
models differ with respect to the payoff attribute considered, it
can be
shown that (under certain conditions) the models yield
theoretically
equivalent measures of intrinsic value.
The discounted dividend model, attributed to Williams [1938],
equates
the value of a firm's equity with the sum of the discounted
expected div-
idend payments to shareholders over the life of the firm, with
the termi-
15. nal value equal to the liquidating dividend:
T DIV
tF 1 (1+rE)t (1)
where:
VDIV = market value of equity at time F;
F = valuation date;
6For all other growth rates examined (2%, 6%, 8%, and 10%),
we find that AEvalue es-
timates dominate FCFand DIVvalue estimates in terms of
accuracy and smallest absolute bias.
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COMPARING EARNINGS EQUITY VALUE ESTIMATES 49
DJVt = forecasted dividends for year t;
rE = cost of equity capital; and
T = expected end of life of the firm (often T -o).
(For ease of notation, firm subscripts and expectation operators
are
suppressed. All variables are to be interpreted as time F
expectations
for firm j.)
16. The discounted free cash flow model substitutes free cash
flows for divi-
dends, based on the assumption that free cash flows provide a
better
representation of value added over a short horizon. Free cash
flows
equal the cash available to the firm's providers of capital after
all re-
quired investments. In this paper, we follow the FCF model
specified by
Copeland, Koller, and Murrin [1994]:7
T
C - ECE + ECMSF - DF - PSF (2)
VF t=1 (1+rwAcdt
FCFt = (SALESt - OPEXPt - DEPEXPt) (1-I)
+ DEPEXPt - A WCt - CAPEXPt (2 a)
rWACC = WD(l -)rD + wpSrpS + WErE (2b)
where:
VFCF = market value of equity at time F;
SALESt = sales revenues for year t;
OPEXPt = operating expenses for year t;
DEPEXPt = depreciation expense for year t;
AWCt = change in working capital in year t;
CAPEXPt = capital expenditures in year t;
17. ECMSt = excess cash and marketable securities at time t;8
Dt = market value of debt at time t;
PSt = market value of preferred stock at time t;
rWACC = weighted average cost of capital;
rD = cost of debt;
rps = cost of preferred stock;
WD = proportion of debt in target capital structure;
WpS = proportion of preferred stock in target capital structure;
WE = proportion of equity in target capital structure; and
X = corporate tax rate.
The discounted abnormal earnings model is based on valuation
tech-
niques introduced by Preinreich [1938] and Edwards and Bell
[1961],
7The FCF measure specified in equation (2a) is similar to
Copeland, Koller, and Mur-
rin's [1994] specification except we omit the change in deferred
taxes because VL does
not forecast this item.
8Excess cash and marketable securities (ECMS) are the short-
term cash and invest-
ments that the company holds over and above its target cash
balances.
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18. 50 J. FRANCIS, P. OLSSON, AND D. R. OSWALD
and further developed by Ohlson [1995]. The AE model
assumes an ac-
counting identity-the clean surplus relation (3b) -to express
equity
values as a function of book values and abnormal earnings:9
T AEt
AE BE + (3)
t=1 (1 + rE)t
AEt = Xt- rEBt-I (3a)
Bt = Bt-I+XI-DVt (3b)
where:
VI'E = market value of equity at time F;
AEt = abnormal earnings in year t;
Bt = book value of equity at end of year t; and
Xt = earnings in year t.
2.2 PRIOR RESEARCH COMPARING ESTIMATES OF
INTRINSIC VALUES
Several studies investigate the ability of one or more of these
valua-
tion methods to generate reasonable estimates of market values.
19. Kaplan
and Ruback [1995] provide evidence on the ability of
discounted cash
flow estimates to explain transaction values for a sample of 51
firms en-
gaged in high leverage transactions.10 Their results indicate
that the me-
dian cash flow value estimate is within 10O% of the market
price, and that
cash flow estimates significantly outperform estimates based
on compa-
rables or multiples approaches. Frankel and Lee [1995; 1996]
find that
the AE value estimates explain a significantly larger portion of
the varia-
tion in security prices than value estimates based on earnings,
book val-
ues, or a combination of the two.
In addition to these horse races (which pit theoretically based
value
estimates against one or more atheoretically based, but perhaps
best prac-
tice, value estimates), there are at least two studies which
contrast the el-
ements of, or the value estimates from, the DIV FCF, and/or
20. AE models.
Bernard [1995] compares the ability of forecasted dividends
and fore-
casted abnormal earnings to explain variation in current
security prices.
Specifically, he regresses current stock price on current year,
one-year-
ahead, and the average of the three- to five-year-ahead
forecasted divi-
dends and contrasts the explanatory power of this model with
the ex-
planatory power of the regression of current stock price on
current book
value and current year, one-year-ahead and the average of
three- to five-
year-ahead abnormal earnings forecasts. He finds that
dividends explain
29% of the variation in stock prices, compared to 68% for the
combina-
tion of current book value and abnormal earnings forecasts.
Penman
and Sougiannis [1998] also compare dividend, cash flow, and
abnormal
9 Clean surplus requires that any change in book value must
flow through earnings. The
21. exception is dividends, which are defined net of capital
contributions.
10 Transaction value equals the sum of the market value of
common stock and preferred
stock, book value of debt not repaid as part of the transaction,
repayment value of debt for
debt repaid, and transaction fees; less cash balances and
marketable securities.
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COMPARING EARNINGS EQUITY VALUE ESTIMATES 51
earnings-based value estimates using infinite life assumptions.
Using re-
alizations of the payoff attributes as proxies for expected
values at the
valuation date, they estimate intrinsic values for horizons of T
= 1 to T =
10 years, accounting for the value of the firm after time Tusing
a termi-
nal value calculation. Regardless of the length of the horizon,
PS find
that AE value estimates have significantly smaller (in absolute
22. terms)
mean signed prediction errors than do FCF value estimates,
with DIV
value estimates falling in between.
Our study extends previous investigations by comparing
individual
securities' DI, FCF, and AE value estimates calculated using ex
ante data
for a large sample of publicly traded firms. In addition to
evaluating
value estimates in terms of their accuracy (absolute deviation
between
the value estimate and market price at the valuation date,
scaled by the
latter), we contrast their ability to explain cross-sectional
variation in
current market prices. Both metrics assume that forecasts
reflect all avail-
able information and that valuation date securities prices are
efficient
with respect to these forecasts. Under the accuracy metric,
value estimates
with the smallest absolute forecast errors are the most reliable.
The ex-
23. plainability tests-which compare value estimates in terms of
their abil-
ity to explain cross-sectional variation in current market
prices-control
for systematic over- or underestimation by the valuation
models."
3. Data and Model Specification
Our analyses require data on historical book values (from
Compustat),
market prices (from CRSP), and proxies for the market's
expectations of
the fundamental attributes (from VL). VL data are preferred to
other
analyst forecast sources (such as IIBIEIS or Zacks) because VL
reports
contain a broader set of variables forecast over longer horizons
than the
typical data provided by sell-side analysts. In particular, VL
reports divi-
dend, earnings, book value, revenue, operating margin, capital
expendi-
ture, working capital, and income tax rate forecasts for the
current year
(t = 0), the following year (t = 1), and "3-5 years ahead."''2
24. Because the
valuation models require projected attributes for each period in
the fore-
cast horizon, we assume that three- to five-year forecasts apply
to all
years in that interval (results are not sensitive to this
assumption). Also,
because VL does not- report two-year-ahead forecasts, we set
year 2 fore-
casts equal to the average of the one-year-ahead and the three-
year-
ahead forecast. We use data from third-quarter VL reports
because this
is the first time data are reported for the complete five-year
forecast
" In the OLS regression, bias is captured both by the inclusion
of an intercept and by
allowing the coefficient relating the value estimate to current
market price to deviate from
a theoretical value of one (bias which is correlated with the
value estimate itself). Rank
regressions implicitly control for bias by using the ranks of the
variables rather than the
values of the variables.
12 In contrast, IIBIEIS and Zacks contain, at most, analysts'
current-year and one-year-
25. ahead earnings forecasts (annual and quarterly) and an earnings
growth rate.
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52 J. FRANCIS, P. OLSSON, AND D. R. OSWALD
horizon; these reports have calendar dates ranging from F =
July 1 to
September 30 (incrementing weekly) for each sample year,
1989-93. Fi-
nally, we restrict our analysis to December year-end firms to
simplify
calculations.
VL publishes reports on about 1,700 firms every 13 weeks;
800-900 of
these firms have December year-ends. Because VL does not
forecast all
of the inputs to the three valuation models for all firms (e.g.,
they do
not forecast capital expenditures for retail firms), the sample is
reduced
to those firms with a complete set of forecasts. This
requirement ex-
26. cludes about 250-300 firms each year, leaving a pooled sample
of 3,085
firm-year observations (a firm appears at most once each year).
Missing
Compustat and CRSP data reduce the sample to 2,907 firm-year
observa-
tions, ranging from 554 to 607 firms annually. The sample
firms are
large, with a mean market capitalization of $2.6 billion and a
mean beta
of 0.97. Most of the sample firms are listed on either the NYSE
or the
AMEX (82%), with the remainder trading on the NASDAQ
For each valuation model, we discount the forecasted
fundamental
attributes to date F We adjust both for the horizon of the
forecast (e.g.,
three years for a three-year-ahead forecast) and for a part-year
factor,
f (f equals the number of days between F and December 31,
divided by
365), to bring the current-year estimate back to the forecast
date. We es-
timate discount rates using the following industry cost of
equity model:'3
rE = rf + P[E(rm) - rf] (4)
27. where:
rE = industry-specific discount rate;
rf = intermediate-term Treasury bond yield minus the historical
pre-
mium on Treasury bonds over Treasury bills (Ibbotson and
Sinquefield [1993]);
= estimate of the systematic risk for the industry to which firm
j
belongs. Industry betas are calculated by averaging the firm-
specific betas of all sample firms in each two-digit SIC code.
Firm-specific betas are calculated using daily returns over
fiscal
year t- 1;
E(rm) - rf = market risk premium = 6%.14
For a given firm and valuation date, we assume rE(rwAcc for
the FCF
model) is constant across the forecast horizon. The average
cost of equity
for the pooled sample is about 13%. The rwAcc calculation
requires esti-
mates of rD, rps, capital structure (WD, wps, and WE), and
ECMS. The cost
of debt is measured as the ratio of the VL reported interest on
long-term
28. 13Fama and French [1997] argue that industry costs of equity
are more precise than
firm-specific costs of equity. Results using firm-specific
discount rates yield similar infer-
ences and are not reported.
14 SiX percent is advocated by Stewart [1991] and is similar to
the 5-6% geometric
mean risk premium recommended by Copeland, Koller, and
Murrin [1994]. We obtain
qualitatively similar results using the arithmetic average
market risk premium.
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COMPARING EARNINGS EQUITY VALUE ESTIMATES 53
debt to the book value of long-term debt; the cost of preferred
stock is
proxied by the VL reported preferred dividends divided by the
book
value of preferred stock.'5 We set the pretax upper bound on
the cost of
debt and the cost of preferred stock equal to the industry cost
of equity,
and we set the pretax lower bound equal to the risk-free rate
(results
29. are not sensitive to these boundary conditions). Following
Copeland,
Koller, and Murrin [1994, pp. 241-42] we develop long-term
target cap-
ital weights for the rWAcc formula rather than use the weights
implied by
the capital structure at the valuation date.'6 For the pooled
sample, the
mean cost of debt is 9.3%, the mean cost of preferred stock is
10.3%, and
the mean weighted average cost of capital is 11.8%. Based on
Copeland,
Koller, and Murrin's [1994, p. 161] suggestion that short-term
cash and
investments above 0.5-2% of sales revenues are not necessary
to support
operations, we define ECMS as cash and marketable securities
in excess
of 2% of revenues.
We compute two terminal values for each valuation model,
TVFUND,
where FUND = DIV EU', or A. Both terminal values discount
into per-
petuity the stream of forecasted fundamentals after T = 5; the
first
specification assumes these fundamentals do not grow; the
30. second as-
sumes they grow at 4%.17 If the forecasted T = 5 fundamental
is nega-
tive, we set the terminal value to zero based on the assumption
that the
firm will not survive if it continues to generate negative cash
flows or
negative abnormal earnings (dividends cannot be less than
zero). (The
results are not sensitive to this assumption.) Because we draw
similar in-
ferences from the results based on the no growth and the 4%
growth as-
sumptions, we discuss only the latter but report both sets of
results in
the tables.18
15 VL reports book values of long-term debt and preferred
stock as of the end of quarter
1. The results are not affected if we use Compustat data on
book values of debt and pre-
ferred stock at the end of quarter 2. In theory, we should use
the market values of debt
and preferred stock, but these data are not available.
16 Specifically, we use Value Line's long-term (three- to five-
year-ahead) predictions to
infer the long-term capital structure. We use the long-term
31. price-earnings ratio multiplied
by the long-term earnings prediction to calculate the implied
market value of equity five
years hence. For debt, we use VL's long-term prediction of the
book value of debt. For pre-
ferred stock, we assume that it remains unchanged from the
valuation date. The equity
weight in the WACC formula, WE, is then given by WE =
implied equity value/ (implied equity
value + forecasted debt + current book value of preferred
stock). The debt and preferred
stock weights are calculated similarly.
17 The growth rate is often assumed to equal the rate of
inflation. Consistent with
Kaplan and Ruback [1995] and Penman and Sougiannis [1998],
we use a 4% growth rate.
We draw similar conclusions using growth rates of 2%, 6%,
8%, and 10%.
'8We also examine a terminal value equal to VL's long-term
price projection (equal to
the VL three- to five-year-ahead price-earnings ratio multiplied
by the three- to five-year-
ahead earnings forecast). All models perform extremely well
using the inferred price ter-
minal value, with absolute (signed) prediction errors of 16-24%
32. (5-14%) and adjusted
R2s of .77 to .91. Although the magnitudes of the differences
are smaller, we find that AE
value estimates dominate FCF value estimates and perform at
least as well as DIV value
estimates. Because long-term price forecasts are not available
for most firms, we focus on
the more common scenario where terminal values must be
calculated.
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54 J. FRANCIS, P. OLSSON, AND D. R. OSWALD
Discounted dividend model specification:
5
VDIV = (1 + rE)-f .5D1V0 + E (1 + rE)( tf )DIVt
t= 1
+ (1 + rE) (5 +f)TVDv (5)
For the pooled sample, the average forecasted dividends for the
sec-
ond half of the current year and the next five years are, on
average,
33. $0.36, $0.76, $0.91, $1.05, $1.05, and $1.05. The mean
terminal value
estimates for the pooled sample are $8.24 and $12.68 for the no
growth
and 4% growth specifications, respectively.'9
Discounted free cash flow specification:
5
VFF F = (1 + rWAcc) 7.5FCF0 + E (1 + rWACC) ')FCFit
t=1
+ ( 1 + rWACC<) - (5 +f ) TVFCF + ECMSO - Do - PSO. (6)
The mean estimates of free cash flows for the remaining half of
the
current year and the next five years for the pooled sample are
$0.57,
$1.56, $0.99, $1.80, $3.98, and $3.98.20 The average terminal
values for
the pooled sample are $34.59 (no growth) and $55.86 (4%
growth).
Discounted abnormal earnings model specification:2'
VF4E = BQ2 + (1 + rE) f 5 (XO- rE x BQ2)
5
+ I (1 + rE)(t+f) [Xt - rE x Bt+ (1 +, -(5+f) TVAE. (7)
t = 1E
For the pooled sample, the average forecasted abnormal
34. earnings for
the remainder of the current year and the next five years are $-
0.05,
$0.33, $0.87, $1.14, $0.66, and $0.66.22 The terminal value
estimates for
19 For the 564 (of 2,907) observations where the firm pays no
dividends, the value esti-
mates equal zero. We retain these observations in the analysis,
unless noted otherwise. Re-
sults excluding these 564 observations are similar to the full
sample and are not reported.
20The FCF estimate for year 3 is different from years 4 and 5.
For t = 3 the change in
working capital is based on the estimate of working capital in t
= 2. For t = 4 and t = 5, the
change in working capital is zero because working capital
forecasts are equal across t = 3,
t = 4, and t = 5 (recall that we assume that VL three- to five-
year forecasts apply to eaclh.
year in that interval). This causes the FCF forecasts for years 4
and 5 to exceed the FCF
forecast for year 3.
21 We measure book value of equity at the end of Q2, year 0,
BQ2. We obtain similar re-
sults using book value at the end of year -1.
22 The abnormal earnings estimate for year 3 is a function of
35. the estimated book value
at the end of year 2. Hence, the estimate of abnormal earnings
for year 3 differs from the
estimate of abnormal earnings for years 4 and 5 (which is a
function of the constant book
value estimate for years 3-5).
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COMPARING EARNINGS EQUITY VALUE ESTIMATES 55
TABLE 1
Pooled Sample Prediction Errors a
Panel A: Signed Prediction Errors (Bias))b
Mean % a-Level Median % a-Level
Mean Difference Difference = 0 Median Difference Difference
= 0
Current Share Price 31.27 n/a n/a 25.12 n/a n/a
Value Estimate
DIV(g= 0%) 7.84 -75.5% 0.00 5.78 -75.8% 0.00
FCF(g= 0%) 18.40 -31.5% 0.00 13.79 -42.7% 0.00
AE (g= 0%) 22.04 -20.0% 0.00 17.91 -28.2% 0.00
36. DIV(g= 4%) 10.21 -68.0% 0.00 7.44 -68.7% 0.00
FCF (g= 4%) 30.02 18.2% 0.00 22.93 -8.8% 0.07
AE (g = 4%) 24.16 -12.7% 0.00 19.37 -22.9% 0.00
Panel B: Absolute Prediction Errors (Accuracy)c
Central
Value Estimate Median versus FCF versus AE Tendency
DIV(g= 0%) 75.8% 0.00 0.00 0.9%
FCF (g= 0%) 48.5% 0.00 13.2%
AE (g= 0%) 33.1% 20.2%
DIV(g= 4%) 69.1% 0.00 0.00 1.7%
FCF (g= 4%) 41.0% 0.00 18.4%
AE (g= 4%) 30.3% 22.5%
aThe sample securities are for December year-end firms with
the following information available for any year
t = 1989-93: third-quarter Value Line forecasts of all
fundamental values; Comvustat data on the book value of com-
mon equity for year t - 1; and CRSP security prices. P F =
observed share price of security j on the Value Line fore-
cast date; VE-UND = security j's estimate of intrinsic value
based on FUND = dividends (DMV), free cash flows (FCF),
or abnormal earnings (AE). We calculate terminal values based
on a no growth assumption and a 4% growth
assumption.
37. bPanel A reports mean and median signed prediction errors,
equal to (ViUND - ,F)PF We also report the
significance level associated with the t-statistics (sign rank
statistic) of whether the mean (median) prediction
error equals zero.
cPanel B shows the median absolute prediction error, IV/UND -
PJFlIPjF, and the measure of central tendency
(the percentage of observations with value estimates within
15% of observed security pr-ice). The third and fourth
columns report the significance levels for Wilcoxon tests
comparing the pooled sample median absolute predic-
tion errors for the noted row-column combination.
the pooled sample are, on average, $6.87 (no growth) and
$10.74 (4%
growth).
4. Empirical Work
Panel A of table 1 reports mean and median security prices at
the
valuation date and value estimates for the pooled sample.23 For
all anal-
yses we set negative value estimates to zero, affecting 16 AE,
80 FCF, and
no DIVvalue estimates. We obtain similar results if we do not
set these
estimates to zero. For comparison with Penman and
Sougiannis's results,
38. panel A shows information on signed prediction errors,
(VFUND - P)IP
23 Results for individual years, and using prices five days after
the valuation date (to en-
sure investors have fully impounded the information in VL
analysts' forecasts made at time
F), are similar and are not reported.
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56 J. FRANCIS, P. OLSSON, AND D. R. OSWALD
Summary statistics show that all of the models tend to
underestimate
security prices, with mean (median) signed prediction errors of
-68%
(-69%) for VDIV 18% (-9%) for VFCF, and -13% (-23%) for
VAE. The
frequency and magnitude of the underestimation is most severe
for DIV
value estimates which are less than price 99% of the time (not
reported
in table 1). Tests of the accuracy of the value estimates,
reported in
panel B, show median absolute prediction errors along with a
measure
39. of the central tendency of the value estimate distribution.
Following
Kaplan and Ruback [1995] we define central tendency as the
percent-
age of observations where the value estimate is within 15% of
the ob-
served security price. The median accuracy of VAE of 30% is
significantly
(at the .00 level) smaller than the median accuracy of VFCF
(41%) and of
VDNV (69%). AE value estimates also show more central
tendency than
FCF estimates (.22 versus .18); both of these models
significantly out-
perform DIV estimates, where fewer than 2% of the
observations are
within 15% of observed price.
We also examine the ability of the value estimates to explain
cross-
sectional variation in securities prices. Panel A of table 2
reports R2s for
the OLS and rank univariate regressions of market price at time
F on
each value estimate,24 and panel B reports the multivariate
regressions
of price on VDIV VFCF, and VAE (the full model). The
explained vari-
ability of the rank univariate regressions is high for all three
40. valuation
models (between 77% and 90%); however, the OLS results
show greater
variation in R2s-between 35% and 71% -with FCFvalue
estimates per-
forming substantially worse than AE or DIV value estimates. In
particu-
lar, VFCF explains about one-half (two-thirds) of the variation
in price
explained by VAE(VDNV).25 Results in panel B calibrate the
incremental
importance of each value estimate by decomposing the
explanatory
power of the full model into the portion explained by each
value esti-
mate controlling for the other two.26 For example, the
incremental ex-
24 OLS regressions include an intercept; rank regressions do
not. We report OLS results
after deleting observations with studentized residuals in excess
of two; this rule eliminates
between 44 and 105 observations for each model. Results based
on the full sample are
similar to those reported with one exception: when all
observations are retained, the ex-
planatory power of DfVestimates is 22% (versus 51% when
outliers are deleted).
41. 25 If the value estimates are unbiased predictors of market
security prices, then the in-
tercept (X0) should equal zero and the coefficient relating
value estimate to price (X1)
should be one. In all cases, we reject the joint hypothesis that
X0 = 0 and XI = 1. Because
these rejections may arise from heteroscedasticity (for the
DIVand FCF estimates-but not
the AE estimates-White [1980] tests reject the hypothesis that
the variance of the distur-
bance term is constant across observations), we repeat all
analyses after transforming the
variables to eliminate the heteroscedasticity. The transformed
results (not reported) show
small changes in the parameter estimates; in all cases the
results are qualitatively similar to
the untransformed results reported in the tables.
26 For g = 4%, we are unable to reject the joint hypothesis that
the intercept equals zero
and the sum of the slope coefficients equals one. For g = 0%,
we reject at the .08 level.
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42. COMPARING EARNINGS EQUITY VALUE ESTIMATES 57
TABLE 2
Results of Pooled Sample Regressions of Contemporaneous
Stock Prices on Intrinsic Value Estimates a
Panel A: Univariate Regressions of Price on Value Estimateb
Growth Rate = 0% Growth Rate = 4%
DIV FCF AE DIV FCF AE
OLS Coefficient 1.75 0.76 1.23 1.30 0.46 1.09
OLS R2 0.54 0.40 0.73 0.51 0.35 0.71
Rank R2 0.84 0.77 0.90 0.84 0.77 0.90
Panel B: Multivariate Regressions of Price on Value Estimatesc
Growth Rate = 0% Growth Rate = 4%
DIV FCF AE DIV FCF AE
OLS Coefficient 0.16 0.10 1.04 0.04 -0.02 1.06
t-Statistic OLS Coefficient = 0 2.04 4.93 22.18 0.53 -1.14
22.20
t-Statistic Rank Coefficient = 0 10.99 9.67 34.00 11.19 3.95
33.87
Model OLSR2 0.73 0.71
Model Rank R2 0.91 0.91
43. Incremental OLSR2 0.01 0.00 0.12 0.00 0.00 0.14
Incremental Rank R2 0.00 0.00 0.04 0.00 0.00 0.04
aSee n. a to table 1 for the sample description and the
calculations of value estimates.
bPanel A reports results of estimating the following regression:
P F = 0 + X1 VfUND + ?j, where =
observed share price of security j on the Value Line forecast
date; VPIND = value estimate for security j
for FUND = dividends (DIV), free cash flows (FCF), or
abnormal earnings (AE).
cPanel B shows results of estimating the following regression:
Pj F= N + p1 VjDI + pt 2ifC + t 3VjAE +
?j The last two rows in panel B show the incremental adjusted
R2 provided by the noted value esti-
mate, beyond that provided by the other two value estimates.
For the OLS regression, we report White
[1980] adjusted t-statistics. The incremental adjusted R2 is the
difference between the adjusted R2 for
the OLS (rank) regression containing all three value estimates
and the adjusted R2 for the OLS (rank)
regression which excludes the value estimate in the noted
column.
planatory power of VDIV equals the adjusted R2 from the full
model
minus the adjusted R2 from the regression of price on VFCF
and VAE.
44. Controlling for VFCF and VDIV VAE adds 14% explanatory
power for the
OLS regressions and 4% for the rank regressions. In contrast,
neither
VFCF nor VDIV adds much (0-1% incremental adjusted R2) to
explaining
variation in security prices.
In summary, the results in tables 1 and 2 indicate that AE value
esti-
mates dominate DIV and FCF value estimates in terms of
accuracy and
explainability. One explanation for this superiority is that
differences in
reliability stem from the AE model containing both a stock
component
(BF) and a flow component (AE.), whereas the DIV and FCF
models are
pure flow-based models. AE value estimates will dominate DIV
and FCF
value estimates when biases in book values resulting from
accounting
procedures (such as expensing R&D) or accounting choices
(such as a
firm's accrual practices) are less severe than errors in
forecasting at-
45. tributes and errors in estimating discount rates and growth
rates. As an
indication of the potential severity of this issue, we note that
book value
of equity represents 72% of the sample mean VAE, and that the
terminal
value represents 21%, 65%, and 82% of the mean VAI, VDIV
and VFU, re-
spectively. Thus, biases in measuring book values may
substantially affect
AE value estimates (but have no effect on DIV or FCF value
estimates),
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58 J. FRANCIS, P. OLSSON, AND D. R. OSWALD
TABLE 3
Results of Pooled Sample Regressions of Contemporaneous
Stock Prices
on the Components of Value Estimates a
Panel A: DIVModelb
Growth Rate = 0% Growth Rate = 4%
PV DTV PV DTV
47. Panel C: AE Model d
Growth Rate = 0% Growth Rate = 4%
B PV DTV B PV DTV
OLS Coefficient 1.24 2.99 -0.48 1.24 3.05 -0.31
t-Statistic: OLSCoefficient= 1 12.36 19.96 -15.82 12.51 22.73 -
28.03
t-Statistic: Rank Coefficient = 0 66.84 22.64 -4.95 67.03 23.75
-5.52
Model OLSR2 0.74 0.74
Model Rank R2 0.91 0.91
Incremental OLSR2 0.45 0.11 0.00 0.45 0.14 0.00
Incremental Rank R2 0.15 0.02 0.00 0.15 0.02 0.00
aSee n. a to table 1 for a description of the sample and the
calculations of value estimates and ter-
minal values.
bPanel A reports coefficient estimates and White-adjusted t-
statistics for the following regression:
Pj, F = +OO + co I PVPff + co DTVD/v , where PjF = observed
share price of security j on the Value Line
forecast date; PVjPJ{ = the present value of the five-year
stream of forecasted dividends; DTVDII/ = dis
counted (to time F) value of the terminal value for the noted
specification.
48. cPanel B reports coefficient estimates and White-adjusted t-
statistics for the following regression:
Pi, F = Co + AFAf(f + 2PVFFf + 03DTVF7(f + ?j ,, where PjF
= observed share price of security j on the
Value Line forecast date; NFAjF = net financial assets at the
valuation date (excess cash and marketable
securities - debt - preferred stock); PVF'T the present value of
the five-year stream of forecasted free
cash flows; DTVjDWr^ = discounted (to time F) value of the
terminal value for the noted specification.
dPanel C reports coefficient estimates and White-adjusted t-
statistics for the following regression:
PjFx = CO + (oBjF + (02PVjMjF + o3DT' +DT j where P. E =
observed share price of security j on the Value
Line forecast date; By= book value of equity at the valuation
date; PV; - the present value of the five-
year stream of forecasted dividends; DTV'AE, discounted (to
time F) value of the terminal value for the
noted specification.
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COMPARING EARNINGS EQUITY VALUE ESTIMATES 59
while errors in forecasting flows and estimating discount rates
and growth
rates are likely to have a bigger effect on VDIV and VFcF than
49. on V I.
Table 3 provides indirect evidence on the stock versus flow
distinction
by examining the incremental explanatory power of the
components of
each value estimate:27
= + (0pVDIV + v2DTVJD; + ?, (8)
P' F = to + wo1FAA + o2PVCf + o3DTV;f + ?j, (9)
P. F = WO + WJBj + PV + 3DTV7F + (10)
where:
PVFUND = the present value of the five-year stream of the
fore-
I
casted attribute;
DTV;FUN = the discounted (to time F) value of the terminal
value;
and
NFAj = net financial assets at time F = ECMS - D - PS.
For each regression, we report White-adjusted t-statistics of
whether
the estimate differs from its theoretical value of one, the
adjusted R2 for
each equation and model, and the additional explanatory power
added by
50. each component of the model holding constant the other
component(s).
Turning first to the coefficient estimates, we note that for all
three mod-
els, we strongly reject the null hypothesis that the coefficient
relating
DTVFUND to price equals one, suggesting that none of the
terminal values
is well specified. For the AE model, the results also reject the
hypothesis
that the coefficient relating price to book value is one, although
in all
cases 0h is significantly positive. Comparing OLS [rank]
results across the
three panels, we note that the incremental explanatory power
provided
by PVDIV of 10% [1 %] and by PVFC-F of 5% [1 %] is
substantially less than
the 45% [15%] explanatory power provided by book value
alone in the
AE model. Book value also adds more than either PVAE-14%
[2%] or
DTVAE o% [1%]. Overall, these results suggest that, despite
conserva-
tism in its measurement, book value of equity explains a
significant por-
tion of the variation in observed prices.
51. Our second analysis of the stock versus flow explanation
focuses on
situations where we might expect accounting practices to result
in book
values that are biased estimates of market value. On the one
hand, we
expect that when the current book value of equity does a good
job of
recording the intrinsic value of the firm, AE value estimates
are more
27 The terminal value component equals the present value, at
the valuation date, of the
estimated terminal value five years hence. To be consistent
with the FCF model specified by
equation (2), we include net financial assets (NFA), equal to
excess cash and marketable
securities minus debt minus preferred stock, as a component in
the FCF model.
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60 J. FRANCIS, P. OLSSON, AND D. R. OSWALD
reliable-than FCF or DIV value estimates-because more of
intrinsic
52. value is included in the forecast horizon (and therefore less in
the ter-
minal value calculation). On the other hand, even if book
values exclude
value-relevant assets, the AE model's articulation of the
balance sheet
and the income statement will link lower book values today
with larger
abnormal earnings in future periods. For example, if the net
R&D payoff
component of earnings is stable through time (as we expect in
equilib-
rium), then the sum of current book value of equity and the
discounted
stream of abnormal earnings will result in the same estimate of
intrinsic
value if R&D investments were capitalized at their net present
values
(Bernard [1995, n. 9] makes a similar point with respect to
accounting
distortions which result in overstatements of book values).
We test whether the AE model performs differently for firms
with high
R&D spending than for firms with low or no R&D spending.
We iden-
53. tify a sample of High R&D firms by first ranking the sample
firms based
on the ratio of Compustat R&D spending in year t - 1 to total
assets at
the beginning of year t - 1. About 48% (1,390 firm-year
observations) of
the sample disclose no, or immaterial amounts of, R&D
expenditures
(the Low R&D sample); the top 25% of firms (the High R&D
sample)
have mean annual R&D spending of 7.2% of total assets. Table
4, panel
A reports the results of accuracy comparisons; table 5, panel A
shows the
results of the explainability tests. These findings show no
evidence that,
for the High R&D sample, AE value estimates are less reliable
than DIV
or FCF estimates; in fact, they are significantly more accurate
and explain
more of the variation in security prices. Within-model, across-
partition
comparisons of accuracy (far right column of table 4) show no
differ-
ence in the accuracy of AE value estimates for High versus
Low R&D
54. firms. Differences in R2s between the High and Low R&D
samples (panel
A, table 5) indicate that the AE model performs better, not
worse, for
High R&D firms.28
We also partition the sample based on the ability of firms to
affect the
flow component of the AE model. Unlike free cash flows and
dividends,
management can influence the timing of abnormal earnings by
exer-
cising more or less discretion in their accrual practices.
Whether such
discretion leads to AE value estimates being more or less
reliable mea-
sures of market prices depends on whether management uses
account-
ing discretion to clarify or obfuscate value-relevant
information. Because
we have no a priori reason for believing that one effect
dominates the
other in explaining the accrual behavior of the sample firms,
we do not
28 Our results concerning the accuracy of AE value estimates
for Highl versus Low R&D
firms may be sensitive to our sample of large, relatively stable
firms. For their broader sam-
55. ple, Sougiannis and Yaekura [1997] find that absolute
prediction errors for AE value esti-
mates increase with the amount of R&D spending, and Barth,
Kasznik, and McNichols
[forthcoming] report a significant negative relation between
R&D spending and signed pre-
diction errors (they define the prediction error as price minus
value estimate, scaled by price).
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TABLE 4
Comparison of the Accuracy of Value Estimates Across and
Within Sample Partitionsa
Panel A: R&D (as Percentage of Total Assets)
High R&D Sampleb Low R&D Sampleb
versus versus versus versus High versus Low
Value Estimate Median FCF AE Median FCF AE Difference
DIV(g= 0%) 77.5% 0.00 0.00 78.2% 0.00 0.00 0.01
FCF(g= 0%) 46.1% 0.00 49.2% 0.00 0.00
AE (g= 0%) 35.0% 33.9% 0.35
56. DJV(g= 4%) 71.8% 0.00 0.00 71.9% 0.00 0.00 0.01
FCF(g= 4%) 33.7% 0.00 45.7% 0.00 O?O?
AE (g= 4%) 30.9% 32.0% 0.36
Panel B: Accruals (as Percentage of Total Assets)
High Accrual Samplec Low Accrual Samplec
versus versus versus versus High versus Low
Value Estimate Median FCF AE Median FCF AE Difference
DJV(g= 0%) 78.7% 0.00 0.00 77.0% 0.00 0.00 0.09
FCF (g= 0%) 48.6% 0.00 46.8% 0.00 0.40
AE (g= 0%) 32.9% 34.9% 0.19
DIV(g= 4%) 72.7% 0.00 0.00 71.4% 0.00 0.00 0.34
FCF (g= 4%) 41.9% 0.00 38.3% 0.00 0.17
AE (g= 4%) 29.8% 32.0% 0.38
Panel C: Precision of Attribute
High Precision Sampled Low Precision Sampled
versus versus versus versus High versus Low
Value Estimate Median FCF AE Median FCF AE Difference
DJV(g= 0%) 100.0% 0.00 0.00 67.0% 0.00 0.00 0.00
57. FCF (g= 0%) 50.5% 0.00 49.3% 0.00 0.67
AE (g= 0%) 43.8% 23.8% 0.00
DIV1l(g= 4%) 100.0% 0.00 0.00 58.5% 0.00 0.00 0.00
FCF (g= 4%) 34.8% 0.36 61.2% 0.00 0.00
AE (g= 4%) 39.3% 24.4% 0.00
Panel D: Predictability of Attribute
High Predictability Samplee Low Predictability Samplee
versus versus versus versus High versus Low
Value Estimate Median FCF AE Median FCF AE Difference
DIV(g= 0%) 71.2% 0.00 0.00 77.1% .0.00 0.00 0.00
FCF (g= 0%) 48.0% 0.00 48.7% 0.00 0.28
AE (g= 0%) 37.4% 31.0% 0.00
DJV(g= 4%) 63.7% 0.00 0.00 72.0% 0.00 0.00 0.00
FCF(g= 4%) 42.0% 0.00 39.0% 0.00 0.40
AE (g= 4%) 32.4% 29.4% 0.08
aSee n. a to table 1 for a description of the sample and the
calculations of value estimates and terminal val-
ues. In the columns labeled "versus FCF(AE) " we report
significance levels comparing the median absolute pre-
dliction errors of the noted variables. The column labeled
"High versus Low Difference" shows the significance
level for the Wilcoxon test for whether the accuracy of the
noted High sample differs from the accuracy of the
Low sample.
58. bThe High R&D sample consists of the firms in the top quartile
of R&D expenses as a percentage of total
assets, measured in year t - 1; the Low R&D sample contains
all firms with no disclosed research and develop-
ment expenses in year t - 1.
cThe High (Low) Accrual sample consists of the top (bottom)
quartile of firms ranked on the absolute value
of total accruals as a percentage of total assets, measured in
year t- 1.
11Precision equals the absolute value of the difference between
the forecast attribute and its realization,
scaled by forecast attribute. The Low (High) Precision sample
for each FCF(AE) attribute consists of the top
(bottom) quartile of firms ranked on the average precision of
the current-year, one-year-ahead, and three-year-
ahead forecasts of that attribute.
ePredictability equals the standard deviation of percentage
yearly changes in the historical realized values of
the attribute valued by each model. The Low (High)
Predictability sample for each FCF(AE) attribute consists
of the top (bottom) quartile of firms ranked on the
predictability of that attribute.
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62 J. FRANCIS, P. OLSSON, AND D. R. OSWALD
TABLE 5
59. Comparisons of the Explainability of Value Estimates Across
and Within Sample Partitionsa
Panel A: R&D (as Percentage of Total Assets)
High R&D Sample Low R&D Sample
Growth Rate = 0% Growth Rate = 4% Growth rate = 0%
Growth Rate = 4%
DIV FCF AE DIV FCF AE DIV FCF AE DIV FCF AE
OLS Coefficient 2.13 1.02 1.26 1.69 0.64 1.15 1.34 0.59 1.34
0.90 0.34 1.06
OLS R2 0.75 0.58 0.79 0.74 0.51 0.81 0.32 0.29 0.71 0.28 0.22
0.62
Rank R2 0.87 0.87 0.94 0.87 0.86 0.94 0.81 0.72 0.88 0.81 0.71
0.88
Panel B: Accruals (as Percentage of Total Assets)
High Accrual Sample Low Accrual Sample
Growth Rate = 0% Growth Rate = 4% Growth Rate = 0%
Growth Rate = 4%
DIV FCd AE DIV FCF AE DIV FCF AE DIV FCF AE
OLSCoefficient 1.89 0.81 1.26 1.44 0.52 1.13 1.47 0.82 1.21
0.98 0.52 1.04
OLS R2 0.56 0.40 0.75 0.54 0.34 0.72 0.40 0.41 0.66 0.35 0.35
0.63
Rank R2 0.84 0.77 0.91 0.84 0.76 0.90 0.82 0.81 0.89 0.82 0.79
61. 0.91
aSee n. a to table 1 for a description of the sample and the
calculations of value estimates and terminal values. We
report regression results of observed price on the value
estimates for each sample partition. See table 4 for a descrip-
tion of the sample partitions.
predict whether AE value estimates perform better or worse for
firms
with high accounting discretion. We partition firms based on
the level of
accounting discretion available to firms, as proxied by the ratio
of the ab-
solute value of the ratio of total accruals to total assets (Healy
[1985]).29
Securities in the top (bottom) quartile of the ranked
distribution are
assigned to the High (Low) Accruals sample. The mean
(median) ratio of
accruals to assets for the High Accruals sample is 14% (12%);
for the
Low Accruals sample the mean and the median value is 1.5%.
Table 4,
panel B summarizes the accuracy of the value estimates for the
accruals
29 Total accruals equal change in current assets (Compustat
item #4) - change in cur-
rent liabilities (#5) - change in cash (#1) + change in short-
term debt (#34) - depre-
62. ciation (#16).
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COMPARING EARNINGS EQUITY VALUE ESTIMATES 63
subsamples; table 5, panel B shows similar comparisons for the
explain-
ability measure. Within the High Accruals sample, both
measures show
that VAE is superior to VDNv and VFCF Comparisons of AE
estimates be-
tween the High and Low Accrual samples show no evidence
that AE es-
timates perform worse for the High Accrual sample than for the
Low
Accrual sample.
In summary, we find no evidence that AE estimates are less
reliable
for firms where book values poorly reflect intrinsic values
(firms with
high R&D spending) or for firms where there is scope for
managing
63. earnings (firms with high accruals). If anything, the within-
sample and
across-sample tests indicate that high R&D spending and high
account-
ing discretion are associated with more reliable AE value
estimates.30
A second potential explanation for differences in the reliability
of VDJ'Y
VFCF, and VAIE is that the precision and predictability of the
fundamental
attributes themselves differ. We measure precision as the
absolute differ-
ence between the predicted value of an attribute and its
realization,
scaled by share price.31 We also examine the bias in the
fundamental at-
tributes, measured as the signed difference between the
predicted value
and its realization, scaled by share price. We define
predictability as the
ease with which market participants can forecast the attribute,
measured
as the standard deviation of historical year-to-year percentage
changes
in the attribute.32 Ceteris paribus, more precise and more
predictable
64. attributes should result in more accurate value estimates which
explain
a greater portion of the variation in observed prices.
We compute bias and precision statistics for each of the current
year,
one-year-ahead and three- to five-year-ahead forecasted
attributes;33 me-
dian values are reported in table 6, panel A. Bias measures
indicate that
30 We also partition the sample securities by capital
expenditure spending to investigate
whether FCFvalue estimates outperform AE and DJVvalue
estimates when forecasted cap-
ital expenditures are low. (We thank Peter Easton for
suggesting this analysis.) Results (not
reported) show no evidence that FCF value estimates are more
accurate or explain more
variation in prices than do AE value estimates for the LOW
capital expenditure sample.
Across-sample, within-model tests, however, show some
evidence that FCF value estimates
are more accurate for the LOW capital expenditure sample than
for the HIGH capital ex-
penditure sample.
31 We find similar results if we scale by the absolute value of
the predicted attribute.
65. 32 Results based on the coefficient of variation of yearly
changes are similar and are not
reported.
33We use the three-year-ahead realization to measure the bias
in the three- to five-year-
ahead forecast of each attribute. The realized dividend for year
t equals the total amount
of common stock dividends declared in year t (#121). The
realized free cash flow per
share in year t equals the net cash flow from operating
activities (#308) minus capital
expenditures (#128). The realized abnormal earnings for year t
equal earnings per share
after extraordinary items (#53) minus the estimated discount
rate multiplied by the book
value of common equity in year t - 1 (#60). To ensure
consistency across models, we scale
all variables by the number of shares used to calculate primary
earnings per share (#54),
and we delete observations with missing data for dividends,
free cash flow, or abnormal
earnings.
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66. 64 J. FRANCIS, P. OLSSON, AND D. R. OSWALD
TABLE 6
Comparison of Selected Properties of the Forecast Attributes a
Panel A: Bias and Precisionb
Bias Precision
Wilcoxon Testsb Wilcoxon Testsb
Median versus FCF versus AE Median versus FCF versus AE
Current-Year
DIV 0.00% 0.00 0.00 0.00% 0.00 0.00
FCF 0.85% 0.00 4.88% 0.00
AE 0.65% 1.32%
One-Year-Ahead
DIV 0.01% 0.00 0.00 0.09% 0.00 0.00
FCF 1.75% 0.00 5.93% 0.00
AE 2.22% 2.85%
Three-Year-Ahead
DIV 0.46% 0.00 0.00 0.57% 0.00 0.00
67. FCF 4.72% 0.00 7.76% 0.01
AE 4.54% 5.10%
Panel B: Predictabilityb
Wilcoxon Tests
Attribute Median versus FCF versus AE
DIV 0.22 0.00 0.00
FCF 7.72 0.00
AE 3.64
aSee n. a to table 1 for a description of the sample and the
calculations of value estimates and ter-
minal values.
bBias (precision) equals the signed (absolute value of the)
difference between the forecast attribute
and its realization, scaled by the share price. Predictability
equals the standard deviation of percent-
age yearly changes in the historical realized values of the
attribute valued by each model. We report
median bias, precision, and predictability measures as well as
the significance levels for Wilcoxon tests
comparing these statistics across models.
the median current-year AE (FCF) forecast overstates realized
abnormal
earnings by about 0.6% (0.8%) of security price, with current-
68. year DIV
forecasts showing no bias. For all attributes, forecast optimism
increases
with the forecast horizon: the median one-year-ahead AE (FCF)
forecast
overstates its realization by about 2.2% (1.8%) of price,
compared to 4.5
(4.7%) for the three-year-ahead AE (FCF) forecasts. More
importantly,
we find that for all horizons, AE forecasts are significantly
more accurate
than FCF forecasts, with AE prediction errors ranging from
roughly 25%
of FCFprediction errors for current-year forecasts (1.3% versus
4.9%) to
65% for three-year-ahead forecasts (5.1% versus 7.8%). The
finding that
DIV forecasts are the most precisely forecasted attribute is to
be ex-
pected given firms' reluctance to alter dividend policies.
For each fundamental attribute, we average the precision of
current-
year, one-year-ahead and three-year-ahead forecasts and
identify those
observations in the top quartile of average precision (i.e., those
with the
largest percentage differences between forecasts and
realizations) as the
69. Low Precision sample and those observations in the bottom
quartile of
average precision as the High Precision sample. Comparisons
of the accu-
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COMPARING EARNINGS EQUITY VALUE ESTIMATES 65
racy and explainability of the value estimates for precision
subsamples
are shown in panel C of tables 4 and 5. With the exception of
the accu-
racy of VFCF (4% growth rate), there is no evidence that more
precise
forecasts result in more reliable value estimates; if anything,
we find the
opposite. For the DIV model, this result is not unexpected,
since for a
large portion of the sample firms, VDNV understates intrinsic
values (as
shown in table 1);34 in these cases, the slight average optimism
in DTV
forecasts (documented in table 6) improves the accuracy of
VDIY Simi-
larly, the optimism in AE forecasts compensates for the
70. underestimation
by VAE observed in table 1, resulting in more reliable AE
value estimates
for the Low Precision partition than for the High Precision
partition.
We also partition the sample based on the standard deviation of
the
percentage changes in the attribute; for these calculations, we
require
a minimum of ten annual changes in realized dividends, free
cash flows,
and abnormal earnings. Table 6, panel B reports median values
of the
predictability measure for each model; we also report
comparisons of
predictability between each pair of models. Consistent with
firms making
few changes in dividend payments and policies, we find that
dividends
are highly predictable. Of more interest (we believe) is the
finding that
abnormal earnings are significantly (at the .00 level) more
predictable
than free cash flows. To assess the importance of predictability
on the re-
liability of the value estimates, we rank each set of value
estimates based
on the magnitude of the relevant predictability measure and
repeat our
71. tests on the bottom quartile (High Predictability sample) and on
the top
quartile (Low Predictability sample). The results in panel D of
tables 4
and 5 show no evidence that a more predictable AE or FCF
series leads
to significantly more reliable value estimates; however, we do
observe
this pattern for the DIV series.
Overall, the results in tables 4-6 provide mixed evidence on
whether
the precision and the predictability of the attribute valued by
each
model are important determinants of the reliability of the value
esti-
mates. Consistent with the AE model's relative superiority over
the FCF
model, we find that AE forecasts are generally more precise
and more
predictable than FCF forecasts. However, within-model tests
show no
consistent evidence that securities with the most precise or the
most
predictable forecasts have more reliable value estimates than
do securi-
ties with the least precise or the least predictable forecasts.
5. Comparison to Penman and Sougiannis [1998]
72. Penman and Sougiannis [1998] compare the signed prediction
errors
of DIV FCF, and AE value estimates calculated using realized
values of
34This is certainly true for the 20% of firms in our sample
which do not pay dividends.
Even for dividend-paying firms, DlVestimates likely understate
value because our terminal
value calculations do not include a liquidating dividend.
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66 J. FRANCIS, P. OLSSON, AND D. R. OSWALD
these attributes.35 Although both PS and we conclude that
VAE domi-
nates VDNv and VFCF, the studies differ in several respects:
PS's sample is
larger and more diversified than our sample of (median and
large) VL
firms; PS's approach uses realizations and a portfolio averaging
process,
while our design examines analysts' forecasts for individual
securities;
PS evaluate bias, while we focus on accuracy and
explainability. We link
73. the two studies by examining, for our sample, the effects of
using fore-
casts versus realizations, the portfolio methodology, and other
perfor-
mance metrics.
For each firm-year observation with available data, we collect
realized
values of DIV FCF, and AE from the 1997 Compustat tape
(which includes
fiscal years through 1996). Because we have a five-year
forecast horizon,
we are limited to analyzing years 1989, 1990, and 1991. For
each sample
year, we randomly assign the sample securities to ten portfolios
and cal-
culate the average portfolio value of each attribute for each
year of the
horizon. We then discount (at the average discount rate) the
mean val-
ues of the attributes to the average valuation date to arrive at a
mean
value estimate for each portfolio. We perform this analysis
using both
forecasts and realizations, so that we have both an ex ante and
74. an ex
post mean value estimate for each portfolio to compare to the
mean
portfolio price. We believe the calculation of the mean value
estimates
follows that of PS, except that we have fewer portfolios (30
versus 400)
and fewer firms per portfolio (50-60 versus about 200).
Panel A of table 7 shows the median value estimates and
median bias
for the 30 portfolios.36 We report the median signed prediction
error
for portfolio value estimates based on realizations ("ex post"
value esti-
mates), for portfolio value estimates based on forecasts ("ex
ante" value
estimates), and for these same securities' value estimates
calculated us-
ing forecasts and the individual security approach. A
comparison of the
ex post portfolio and ex ante portfolio results shows the effects
of using
realizations versus forecasts, holding constant the
methodology; a com-
75. parison of ex ante portfolio value estimates with ex ante
individual secu-
rity value estimates highlights the effects of the portfolio
methodology,
controlling for the use of forecast data. Panel B shows similar
compari-
sons for the accuracy metric.37
We draw the following conclusions from the results in table 7.
First, ex
post value estimates are considerably smaller than ex ante
value estimates,
35 For the specification closest to ours, PS report mean biases
of -17% (VDII'), -76%
(VF'C), and 6% (VAE); these compare to our sample mean bias
measures of -68% (VDV),
18% (VFCF), and -13% (VAE), reported in table 1.
36We report median statistics for consistency with tables 1 and
4. Results based on
means are similar.
37We do not examine the explainability metric for the portfolio
measures because the
point of the portfolio averaging process-to reduce the
variability in observed prices and
in value estimates-runs counter to the point of the
explainability tests-to assess the ex-
tent to which value estimates explain variation in observed
prices.
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COMPARING EARNINGS EQUITY VALUE ESTIMATES 67
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68 J. FRANCIS, P. OLSSON, AND D. R. OSWALD
regardless of whether the portfolio or individual security
approach is
used. This difference is particularly striking for FCFvalue
estimates where
the median VFC-based on realizations is zero, reflecting the
fact that most
of the sample portfolios (20 of 30, not reported) had negative
realized
mean free cash flows. The smaller ex post value estimates are
also con-
sistent with the results in table 6 which show that, for our
sample, real-
izations fell short of analysts' expectations by a wide margin.
Second,
comparisons of observed prices with both ex ante and ex post
value es-
timates indicate that ex ante value estimates dominate ex post
value es-
79. timates: for all models, ex ante values have significantly (at the
.00 level)
smaller bias and are more accurate than comparable ex post
values.
Third, using either smallest absolute bias or smallest absolute
prediction
error as the performance criterion, the realization-portfolio
results show
that AE value estimates dominate DIV and FCF value
estimates. In con-
trast, the forecast-portfolio results depend on the growth rate:
for g= 0%,
VAE dominates VDV and VFC-in terms of bias and accuracy,
but for g = 4%,
VFC-dominates.38 This disparity between the bias and the
accuracy results
observed for FCF value estimates (for g = 4%) highlights the
effect that
variation in the value estimates has on the performance metric.
When the
variability in value estimates is retained-as it is when
individual securi-
ties rather than portfolios are valued-the results (far right
columns of
table 7) show that AE value estimates consistently dominate
80. FCF and DIV
estimates in terms of accuracy.
We draw the following inferences from these results. The
conclusion
that AE value estimates dominate FCF or DIV value estimates
is fairly
robust to the level of aggregation (portfolio versus individual
securities),
the type of data (realizations versus forecasts), and the
performance
metric (bias versus accuracy). We find, however, that the levels
of bias
and accuracy are significantly (at the .00 level) smaller when
forecasts
rather than realizations are used to calculate value estimates.
The fore-
cast versus realization distinction is also important for
conclusions con-
cerning FCF and DfVvalue estimates. Consistent with PS, we
find that ex
post FCF value estimates are more biased than ex post DIV
value esti-
mates; however, ex ante FCF value estimates dominate ex ante
DIVvalue
estimates in terms of both bias and accuracy.
81. 6. Summary and Conclusions
This paper compares the reliability of value estimates from the
dis-
counted dividend model, the discounted free cash flow model,
and the
discounted abnormal earnings model. Using a sample of five-
year fore-
casts for nearly 3,000 firm-year observations over 1989-93, we
find that
38 See n. 6 for results based on other growth rates.
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COMPARING EARNINGS EQUITY VALUE ESTIMATES 69
the AE value estimates are more accurate and explain more of
the varia-
tion in security prices than do FCF or DfVvalue estimates. Our
explora-
tions of the sources of the relative superiority of the AE model
show that
the greater reliability of AE value estimates is likely driven by
the suffi-
ciency of book value of equity as a measure of intrinsic value,
82. and per-
haps by the greater precision and predictability of abnormal
earnings.
Our analysis of whether accounting discretion enhances or
detracts
from the performance of the AE model indicates no difference
in the
accuracy of AE estimates between firms exercising high versus
low ac-
counting discretion, although there is some evidence that AE
value esti-
mates explain more of the variation in current market prices for
high
discretion firms than for low discretion firms. We also find no
evidence
that AE value estimates are less reliable for firms with high
versus low
R&D expenditures. Together these findings indicate no
empirical basis
for concerns that accounting practices (such as immediate
expensing of
R&D or the flexibility afforded by accruals) result in inferior
estimates
of market equity value. Our results are more consistent with the
83. argu-
ment that the articulation of clean surplus financial statements
ensures
that value estimates are unaffected by conservatism or accrual
practices.
We conclude there is little to gain-and if anything something to
lose-from selecting dividends or free cash flows over abnormal
earn-
ings as the fundamental attribute to be valued. Together with
the fact
that earnings are by far the most consistently forecasted
attribute, our
results suggest little basis for manipulating accounting data
(for exam-
ple, to general estimates of free cash flows) when earnings
forecasts and
book values are available.
REFERENCES
BARTH, M.; R. KASZNIK; AND M. McNICHOLS. "Analyst
Coverage and Intangible Assets."
Journal of Accounting Research (forthcoming).
BERNARD, V "The Feltham-OhIson Framework: Implications
for Empiricists." Contemipo-
rary Accounting Research (Fall 1995): 733- 47.
84. COPELAND, T.; T. KOLLER; AND J. MURRIN. Valuation:
Measuring and Managing the Value of
Companies. 2d ed. New York: Wiley, 1994.
EDWARDS, E., AND P. BELL. The Theory and Measurement
of Business Income. Berkeley: Univer-
sity of California Press, 1961.
FAMA, E., AND K. FRENCH. "Industry Costs of Equity."
Journal of Financial Economics 43
(1997): 153-93.
FRANKEL, R., AND C. LEE. "Accounting Valuation, Market
Expectation, and the Book-to-
Market Effect." Working paper, University of Michigan and
Cornell University, 1995.
. "Accounting Diversity and International Valuation." Working
paper, University of
Michigan and Cornell University, 1996.
HEALY, P "The Effect of Bonus Schemes on Accounting
Decisions." Journal of Accounting
and Economics 7 (1985): 85-107.
IBBOTSON, R., AND R. SINQUEFIELD. Stocks, Bonds, Bills
and Inflation, 1926-1992. Charlottes-
ville, Va.: Financial Analysts Research Foundation, 1993.
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70 J. FRANCIS, P. OLSSON, AND D. R. OSWALD
KAPLAN, S., AND R. RUBACK. "The Valuation of Cash Flow
Forecasts: An Empirical Analy-
sis." Journal of Finance (September 1995): 1059-93.
OHLSON, J. "Earnings, Book Values, and Dividends in Equity
Valuation." Contem)porary
Accounting Research (Spring 1995): 661-87.
PENMAN, S., AND T. SOUGIANNIS. "A Comparison of
Dividend, Cash Flow, and Earnings
Approaches to Equity Valuation." Contemporary Accounting
Research (Fall 1998): 343-84.
PREINREICH, G. "Annual Survey of Economic Theory: The
Theory of Depreciation." Econo-
metrica (July 1938): 219- 41.
SOUGIANNIS, T, AND T. YAEKURA. "The Accuracy and
Bias of Equity Values Inferred from
Analysts' Earnings Forecasts." Working paper, University of
Illinois, 1997.
STEWART, G. The Quest for Value. New York: HarperCollins,
1991.
86. WHITE, H. "A Heteroscedasticity-Consistent Covariance
Matrix Estimator and a Direct Test
for Heteroscedasticity." Econometrica 48 (1980): 817-38.
WILLIAMS, J. The Theory of Investment Value. Cambridge,
Mass.: Harvard University Press,
1938.
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2016 22:17:51 UTC
All use subject to http://about.jstor.org/terms
Contentsimage 1image 2image 3image 4image 5image 6image
7image 8image 9image 10image 11image 12image 13image
14image 15image 16image 17image 18image 19image 20image
21image 22image 23image 24image 25image 26Issue Table of
ContentsJournal of Accounting Research, Vol. 38, No. 1,
Spring, 2000Front MatterCountry-Specific Factors Related to
Financial Reporting and the Value Relevance of Accounting
Data [pp. 1 - 21]The 1993 Tax Rate Increase and Deferred Tax
Adjustments: A Test of Functional Fixation [pp. 23 -
44]Comparing the Accuracy and Explainability of Dividend,
Free Cash Flow, and Abnormal Earnings Equity Value Estimates
[pp. 45 - 70]Stock Returns and Accounting Earnings [pp. 71 -
101]Research ReportsFinancial Packaging of IPO Firms in
China [pp. 103 - 126]Biases and Lags in Book Value and Their
Effects on the Ability of the Book-to-Market Ratio to Predict
Book Return on Equity [pp. 127 - 148]Do Stock Prices Fully
Reflect the Implications of Current Earnings for Future
Earnings for AR1 Firms? [pp. 149 - 164]Why Do Audits Fail?
Evidence from Lincoln Savings and Loan [pp. 165 -
194]Capsules and CommentsThe Effect of the External
Accountant's Review on the Timing of Adjustments to Quarterly
Earnings [pp. 195 - 207]The Incremental Information Content
87. of SAS No. 59 Going-Concern Opinions [pp. 209 - 219]Back
Matter
How to use this template:
To use this template, replace the instructions written in italic
font with your own discussion text. Be sure to proofread your
work and check it for completeness and accuracy. Delete any
extra text/instructions/references that do not apply to your post.
Then, copy your work and paste it into the discussion window in
class.Week 3, Discussion 2: Initial Post
Here is my concluding paragraph:
(Here, you should share your concluding paragraph in third-
person academic voice. Your conclusion should reiterate the
position of your paper by summarizing your main points and
rephrased thesis statement. In a final paragraph, share your
original thesis statement.)
My conclusion’s closing argument is … (Here, you should share
and discuss your closing argument in a more conversational
tone. Does your conclusion reemphasize the important points
that you have made in your essay? Have you summarized your
main points and rephrased your thesis statement? Do the final
lines of your conclusion leave your readers with something
interesting to consider?)
After reading my concluding paragraph and the course
materials, I have questions about… (Here, you should share any
questions you may have regarding your conclusion [e.g.,
rephrased thesis statement, summarized main points, etc.] so
your classmates can assist you.)
References
(If you reference the tutorials, textbook, instructor guidance, or
handouts – which you should – be sure to cite them in-text and
add the references to the end of your post. Be sure to use proper
APA style!)
How to use this template:
88. To use this template, replace the instructions written in italic
font with your own discussion text. Be sure to proofread your
work and check it for completeness and accuracy. Delete any
extra text/instructions/references that do not apply to your post.
Then, copy your work and paste it into the discussion window in
class.Week 3, Discussion 1: Initial Post
Thesis statement: Write your single-sentence thesis statement
here.
A. Claim: Write your first claim in a complete sentence here.
1. Evidence: Paraphrase or summarize your source and cite it
here (Sample, 2015).
2. Evidence: Paraphrase or summarize additional sources that
support this claim and cite them here as 2., 3., 4., and so on
(Sample, 2015).
3. Evidence: If you feel the need to use a quote, “add it to the
list with proper quotation marks and the appropriate in-text
citation containing the page, section, or paragraph number in
the original source” (Sample, 2015, p. 22).
B. Claim: Write your second claim in a complete sentence here.
1. Evidence: Paraphrase or summarize your source here
(Sample, 2015).
2. Evidence: Continue to paraphrase and summarize your
sources for each claim (Sample, 2015).
C. Claim: Continue to write your claims in complete sentences.
1. Evidence: Continue to paraphrase, summarize, and/or quote.
2. Evidence: Continue to paraphrase, summarize, and/or quote.
D. Claim: Continue to write your claims in complete sentences.
1. Evidence: Continue to paraphrase, summarize, and/or quote.
2. Evidence: Continue to paraphrase, summarize, and/or quote.
E. Claim: Continue to write your claims in complete sentences.
1. Evidence: Continue to paraphrase, summarize, and/or quote.
2. Evidence: Continue to paraphrase, summarize, and/or quote.
The fallacies I located in my work are...Describe any fallacies
you located and describe how you will remove those fallacies
this week. Ask questions, too, and your classmates can help you
89. out!
My claims logically support my thesis statement by...Describe
how each claim connects to your thesis statement. If you notice
that a claim doesn't quite fit, point it out. Describe the situation
and feel free to ask for advice from the class!
I would like some help with...End your post with questions or
concerns so we can work on them together.
References
List all references here. Your evidence should be cited in-text
and listed here, too.
1
Created in 2015
GUIDELINES FOR PARAPHRASING SOURCES
Paraphrasing
We have all watched a good television show or an interesting
news story that we wanted to tell others
about. When you are explaining the show or story, you most
likely tell your friends, your family, or your
coworkers what happened, how it happened, and why it
happened. In doing so, you describe things like
the plot, the main characters, the events, and the important
points using your own words. This skill is
paraphrasing–using your own words to express someone else's
message or ideas.
90. When you paraphrase in writing, the ideas and meaning of the
original source must be maintained; the
main ideas need to come through, but the wording has to be
your own. And, of course, credit needs to be
given to the author. You don’t want to over quote in your paper.
A great alternative to quoting is to
paraphrase information. However, paraphrasing takes a little
more skill than directly quoting information,
because, to paraphrase correctly, you need to understand what
the original quote or passage is about in
order to write about it in your words.
How Do You Paraphrase a Source?
it and its meaning.
own words. Say what the source
says, but no more, and try to reproduce the source's order of
ideas and emphasis.
exact sense in which the writer uses the
words.
original for accurate tone and meaning,
changing any words or phrases that match the original too
closely. If the wording of the
paraphrase is too close to the wording of the original, then it
can be considered plagiarism.
source, quote them in your
91. paraphrased version.
the original text. For example, if
the paragraph you are paraphrasing is five sentences long, try to
make your paraphrased
paragraph five sentences as well.
e a citation for the source of the information (including
the page numbers, if available) so
that you can cite the source accurately. Even when you
paraphrase, you must still give credit to
the original author.
When Is Paraphrasing Useful?
You should paraphrase when…
author’s language;
yourself;
nformation from charts, graphs, tables,
lectures, etc; or
92. 2
Created in 2015
Examples of Good Paraphrasing
Paraphrasing can be done with individual sentences or entire
paragraphs. Here are some examples:
Original sentence #1:
“Her life spanned years of incredible change for women”
(Smith, 2015, p.1).
Paraphrased version:
Mary lived through an era of liberating reform for women
(Smith, 2015, p.1).
Original sentence #2:
“Giraffes like Acacia leaves and hay, and they can consume 65
pounds of food a day” (“National
Geographic,” 2013, p.16).
Paraphrased version:
93. A giraffe can eat up to 65 pounds of Acacia leaves and hay
every day (“National Geographic,”
2013, p.16).
As you can see in the examples, the essence and meaning of the
paraphrased versions are similar to the
original sentences. The paraphrased sentences even used the
main keywords from the original source,
but the order and the structure of the sentence changed when the
author put the information in his own
words. You can apply these same tactics to paraphrasing longer
texts as well. Here is an example of how
to paraphrase a paragraph of information:
Original paragraph:
“The feminization of clerical work and teaching by the turn of
the century reflected the growth of
business and public education. It also reflected limited
opportunities elsewhere. Throughout the
nineteenth century, stereotyping of work by sex had restricted
women's employment. Job options were
limited; any field that admitted women attracted a surplus of
applicants willing to work for less pay than
men would have received. The entry of women into such
fields—whether grammar school teaching or
office work—drove down wages.”
Woloch, N. (2002). Women and the American experience: A
concise history. New York, NY: McGraw–Hill
Higher Education.
Paraphrased version:
According to Nancy Woloch (2002) in Women and the
94. American Experience: A Concise History,
the "feminization" of jobs in the nineteenth century had two
major effects: a lack of employment
opportunities for women and inadequate compensation for
positions that were available. Thus, while
clerical and teaching jobs indicated a boom in these sectors,
women were forced to apply for jobs that
would pay them less than male workers were paid (p. 170).
This version is properly paraphrased because…
ngth as the original passage;
structure;
quotation marks; and
1
Created in 2015
95. GUIDELINES FOR SUMMARIZING SOURCES
Summarizing
Another good skill to help you incorporate research into your
writing is summarizing. Summarizing is to
take larger selections of text and reduce them to their basic
essentials: the gist, the key ideas, the main
points that are worth noting and remembering. Think of a
summary as the "general idea in brief form"; it's
the distillation, condensation, or reduction of a larger work into
its primary notions and main ideas.
As with directly quoting and paraphrasing, summarizing
requires you to cite your sources properly to
avoid "accidental" plagiarism. Moreover, a summary should not
change the meaning of the original
source. A good summary should be a shortened version that
conveys the purpose and main points of the
original source.
Components of a Good Summary:
work.
o For example:
Death (1890),”
96. original text; it should be about 1/10
as long.
–3 main points of the text or work.
direct quote. If you must use the
author's key words or phrases, always enclose them in quotation
marks and cite.
summary. The purpose of writing a
summary is to accurately represent what the author wanted to
say, not to provide a critique.
When Is a Summary Useful?
You should summarize when…
than the original text;
uthority on the topic to support your ideas.
Examples of Good and Bad Summaries
Be careful when you summarize that you avoid stating your
97. opinion or putting a particular bias on what
you write. This point is important because the goal of a
summary is to be as factual as possible.
For example, here is an example of an inaccurate, opinion-laden
summary about Pixar’s popular movie
Finding Nemo:
So there's a film where a man's wife is brutally murdered by a
serial killer and his son is left
physically disabled. In a twist of events, the son is kidnaped
and kept in a tank while his father
https://awc.ashford.edu/cd-integrating-quotes.html
https://awc.ashford.edu/cd-guidelines-for-paraphrasing.html
2
Created in 2015
chases the kidnapper thousands of miles with the help of a
mentally challenged woman. Finding
Nemo is quite the thriller.
This example is a bad summary because it is very vague, and it
contains the writer’s opinion as well as
twists the events of the story into something it is not. Pixar’s
Finding Nemo is not a thriller or a horror story
like described above—it is an animated children’s movie about
fish.
Here is a better summary of Finding Nemo:
98. Pixar’s Finding Nemo (2003) is a story about Marlin, a
clownfish, who is overly cautious with his
son, Nemo, who has a damaged fin. When Nemo swims too
close to the surface to prove himself,
he is caught by a diver, and horrified Marlin must set out to find
him. A blue reef fish named Dory,
who has a really short memory, joins Marlin and together they
encounter sharks, jellyfish, and a
host of ocean dangers. Meanwhile, Nemo plots his escape from
a dentist's fish tank where he is
being held. In the end, Marlin and his son Nemo are reunited,
and they both learn about trust and
what it means to be a family. (Finding Nemo, 2003)
This paragraph is a better summary than the original one
because:
ucial details without giving too many
details; and
the meaning.
This summary is good because…
work;
1/10 the length of the original passage;
99. parenthetical citation in correct APA
format.
1
Created in 2015
IN-TEXT CITATION GUIDE
What are in-text citations?
An in-text citation is a citation within your writing to show
where you found your information, facts, quotes,
and research. APA in-text citation style uses the author's last
name and the year of publication, for
example: (Field, 2005). For direct quotations, include the page
number as well, for example: (Field, 2005,
p. 14). For sources such as websites and e-books that have no
page numbers, use a paragraph number
instead, for example: (Fields, 2015, para.3).
100. In-text citations follow any sentence in your writing that
contains a direct quote, or paraphrased or
summarized information from an outside source.
Each in-text citation in your writing must also have a
corresponding entry in your References list. There
are two exceptions to this rule: personal communications, like
interviews, emails, or classroom discussion
posts, and classic religious texts, like the Bible or the Koran.
These types of sources should be cited by
in-text citations only.
Always include in-text citations for:
All in-text citations require the same basic information:
h number (for direct quotes only)
Basic Examples of In-Text Citations
101. For a quote: “The systematic development of literacy and
schooling meant a new division in
society, between the educated and the uneducated” (Cook-
Gumperz, 1986, p. 27).
For paraphrased material: Some educational theorists suggest
that schooling and a focus on
teaching literacy divided society into educated and uneducated
classes (Cook-Gumperz, 1986).
For summarized material: Schooling and literacy contributed to
educational divisions in society
(Cook-Gumperz, 1986).
NOTE: If you mention the author and the year in your writing to
introduce the quote or paraphrased
material, then you need only include the page or paragraph
number in the in-text citation.
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For example:
According to Jenny Cook-Gumperz (1986), “The systematic
102. development of literacy and
schooling meant a new division in society, between the educated
and the uneducated” (p. 27).
Additional In-Text Citation Models
For online sources:
For a web page: The USDA is “taking steps to help farmers,
ranchers, and small businesses
wrestling with persistent drought” (United States Department of
Agriculture, 2015, “USDA Drought
Programs and Assistance,” para. 1).
Format: (Website Author, Year, “Web Page Title,” paragraph
number).
For an online article: The F.B.I. “warned the families not to talk
publicly” about the hostages
(Wright, 2015, para. 2).
Format: (Author’s Last Name, Year, paragraph number).
For an email communication: According to Dr. Edwards, “The
coming El Niño won’t do much to
alleviate California’s current drought” (personal
communication, April 26, 2015).
NOTE: Because most online sources do not contain page
numbers, use the paragraph number. While
103. many online sources may include numbers beside the
paragraphs, others may not, and you might have to
count them yourself. In the case of an extremely long article or
an online book, you may include the
section/chapter number and title and then the paragraph number,
like this:
(Smith, 2012, Chapter #, “Section Title,” para. 12).
Citing from a Secondary Source
Sometimes the quote you want to use is quoted by someone else
in another source, like your textbook.
You can still use that quote inside the textbook – this is called
citing from a secondary source. In this
case, the secondary source is your textbook and its author; the
primary source is the quote and its author.
So, in your writing, introduce the original author and the year
of publication, and then in the in-text citation
you’ll include the secondary source information. For instance,
you might want to include a quote by Sarah
Vowell that is cited in your textbook by Ryan Smith. You would
write this:
According to Sarah Vowell (2008), “The only thing more
dangerous than an idea is a belief” (as quoted in
Smith, 2012, Section #, “Section Title,” para. #).
104. NOTE: When citing from a secondary source, only the
secondary source information appears in the
references list. The primary source author and original date of
publication only appears in your writing.
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Moving the Citation Information Around
In-text citations contain three pieces of information: author,
publication date, and page/paragraph
location. However, if in your writing you place this information
elsewhere, like in the introductory phrase
before the quote, you do not need to repeat it in the citation.
Use the citation to “catch” anything you
haven’t already included.
Here are three examples where the citation information is
placed in different locations around the quote:
“The systematic development of literacy and schooling meant a
new division in society, between
105. the educated and the uneducated” (Cook-Gumperz, 1986, p. 27).
According to Jenny Cook-Gumperz (1986), “The systematic
development of literacy and
schooling meant a new division in society, between the educated
and the uneducated” (p. 27).
According to Cook-Gumperz, “The systematic development of
literacy and schooling meant a
new division in society, between the educated and the
uneducated” (1986, p. 27).
NOTE: Parentheses that contain citation information come after
the closing quote mark but before the
punctuation ending the entire sentence. Block quotes are the
exception, where the parenthetical citation
comes after the period at the end of the quote.
For a comprehensive overview of crediting sources, consult
Chapter 6 of the Publication Manual of the
American Psychological Association.
http://www.apastyle.org/
MBA 520 Module Six Forecasting Model Questions
The questions that follow and the article Comparing the
Accuracy and Explainability of Dividend, Free Cash Flow, and
Abnormal Earnings Equity Value Estimates will inform your
106. completion of Milestone Three. An understanding of the models
in this assignment will assist you in hypothesizing the
incremental impact of a new investment project for the
company. The understanding of these models will contribute to
your ability to look toward the future when considering the
direction of an organization. This activity is worth a total of 75
points. See the distribution of points listed before each
question.
Prompt: Once you have read the article “Comparing the
Accuracy and Explainability of Dividend, Free Cash Flow, and
Abnormal Earnings Equity Value Estimates” and Chapters 6 and
7 of your text, review and complete the questions below. Use
the article and your text to inform your responses to the
questions below.
Assignment Questions:
1. (20 points) For models 2, 2a, and 2b:
· What is the best way to minimize the weighted average cost of
capital?
· What is the effect of the weighted average cost of capital on
the market value?
2. (20 points) For models 3, 3a, and 3b:
· What is the relationship between book value of equity and
time t-1 and the market value of the equity?
3. (20 points) Discuss model 4 and expand on the importance
107. and the meaning of the market risk premium.
4. (15 points) In your own words, what are the main conclusions
for this article, and what could be improved upon in its
analysis?