Predicting U.S. business cycles: an analysis based on credit spreads and mark...Gabriel Koh
Our paper aims to empirically test the significance of the credit spreads and excess returns of the market portfolio in predicting the U.S. business cycles. We adopt the probit model to estimate the partial effects of the variables using data from the Federal Reserve Economic Data – St. Louis Fed (FRED) and the National Bureau of Economic Research (NBER) from 1993:12 to 2014:08. Results show that the contemporaneous regression model is not significant while the predictive regression model is significant. Our tests show that only the credit spread variable lagged by one period is significant and that the lagged variables of the excess returns of the market portfolio is also significant. Therefore, we can conclude that credit spreads and excess returns of the market portfolio can predict U.S. business cycles to a certain extent.
Testing and extending the capital asset pricing modelGabriel Koh
This paper attempts to prove whether the conventional Capital Asset Pricing Model (CAPM) holds with respect to a set of asset returns. Starting with the Fama-Macbeth cross-sectional regression, we prove through the significance of pricing errors that the CAPM does not hold. Hence, we expand the original CAPM by including risk factors and factor-mimicking portfolios built on firm-specific characteristics and test for their significance in the model. Ultimately, by adding significant factors, we find that the model helps to better explain asset returns, but does still not entirely capture pricing errors.
Determinants of equity share prices of the listed company in dhaka stock exch...MD. Walid Hossain
This is the finance academic project report.This report prepare by MD. WALID HOSSAIN, Patuakhali science and technology University, Faculty of business administration and management. i think that is helpful for business studies students.
Predicting U.S. business cycles: an analysis based on credit spreads and mark...Gabriel Koh
Our paper aims to empirically test the significance of the credit spreads and excess returns of the market portfolio in predicting the U.S. business cycles. We adopt the probit model to estimate the partial effects of the variables using data from the Federal Reserve Economic Data – St. Louis Fed (FRED) and the National Bureau of Economic Research (NBER) from 1993:12 to 2014:08. Results show that the contemporaneous regression model is not significant while the predictive regression model is significant. Our tests show that only the credit spread variable lagged by one period is significant and that the lagged variables of the excess returns of the market portfolio is also significant. Therefore, we can conclude that credit spreads and excess returns of the market portfolio can predict U.S. business cycles to a certain extent.
Testing and extending the capital asset pricing modelGabriel Koh
This paper attempts to prove whether the conventional Capital Asset Pricing Model (CAPM) holds with respect to a set of asset returns. Starting with the Fama-Macbeth cross-sectional regression, we prove through the significance of pricing errors that the CAPM does not hold. Hence, we expand the original CAPM by including risk factors and factor-mimicking portfolios built on firm-specific characteristics and test for their significance in the model. Ultimately, by adding significant factors, we find that the model helps to better explain asset returns, but does still not entirely capture pricing errors.
Determinants of equity share prices of the listed company in dhaka stock exch...MD. Walid Hossain
This is the finance academic project report.This report prepare by MD. WALID HOSSAIN, Patuakhali science and technology University, Faculty of business administration and management. i think that is helpful for business studies students.
This case study utilizes a large database (2000 stores, 6-years of scanner data) to study pricing strategies for brands. Methods include Advanced regression, PCA and Clustering algorithms.
STOCK TREND PREDICTION USING NEWS SENTIMENT ANALYSISijcsit
Efficient Market Hypothesis is the popular theory about stock prediction. With its failure much research
has been carried in the area of prediction of stocks. This project is about taking non quantifiable data such
as financial news articles about a company and predicting its future stock trend with news sentiment
classification. Assuming that news articles have impact on stock market, this is an attempt to study
relationship between news and stock trend. To show this, we created three different classification models
which depict polarity of news articles being positive or negative. Observations show that RF and SVM
perform well in all types of testing. Naïve Bayes gives good result but not compared to the other two.
Experiments are conducted to evaluate various aspects of the proposed model and encouraging results are
obtained in all of the experiments. The accuracy of the prediction model is more than 80% and in
comparison with news random labelling with 50% of accuracy; the model has increased the accuracy by
30%.
MODELING THE AUTOREGRESSIVE CAPITAL ASSET PRICING MODEL FOR TOP 10 SELECTED...IAEME Publication
Systematic risk is the uncertainty inherent to the entire market or entire market segment and Unsystematic risk is the type of uncertainty that comes with the company or industry we invest. It can be reduced through diversification. The study generalized for selecting of non -linear capital asset pricing model for top securities in BSE and made an attempt to identify the marketable and non-marketable risk of investors of top companies. The analysis was conducted at different stages. They are Vector auto regression of systematic and unsystematic risk.
Multiple Linear Regression Applications in Real Estate Pricinginventionjournals
In this paper, we attempt to predict the price of a real estate individual homes sold in North West Indiana based on the individual homes sold in 2014. The data/information is collected from realtor.com. The purpose of this paper is to predict the price of individual homes sold based on multiple regression model and also utilize SAS forecasting model and software. We also determine the factors influencing housing prices and to what extent they affect the price. Independent variables such square footage, number of bathrooms, and whether there is a finished basement,. and whether there is brick front or not and the type of home: Colonial, Cotemporary or Tudor. How much does each type of home (Colonial, Contemporary, Tudor) add to the price of the real estate
This case study utilizes a large database (2000 stores, 6-years of scanner data) to study pricing strategies for brands. Methods include Advanced regression, PCA and Clustering algorithms.
STOCK TREND PREDICTION USING NEWS SENTIMENT ANALYSISijcsit
Efficient Market Hypothesis is the popular theory about stock prediction. With its failure much research
has been carried in the area of prediction of stocks. This project is about taking non quantifiable data such
as financial news articles about a company and predicting its future stock trend with news sentiment
classification. Assuming that news articles have impact on stock market, this is an attempt to study
relationship between news and stock trend. To show this, we created three different classification models
which depict polarity of news articles being positive or negative. Observations show that RF and SVM
perform well in all types of testing. Naïve Bayes gives good result but not compared to the other two.
Experiments are conducted to evaluate various aspects of the proposed model and encouraging results are
obtained in all of the experiments. The accuracy of the prediction model is more than 80% and in
comparison with news random labelling with 50% of accuracy; the model has increased the accuracy by
30%.
MODELING THE AUTOREGRESSIVE CAPITAL ASSET PRICING MODEL FOR TOP 10 SELECTED...IAEME Publication
Systematic risk is the uncertainty inherent to the entire market or entire market segment and Unsystematic risk is the type of uncertainty that comes with the company or industry we invest. It can be reduced through diversification. The study generalized for selecting of non -linear capital asset pricing model for top securities in BSE and made an attempt to identify the marketable and non-marketable risk of investors of top companies. The analysis was conducted at different stages. They are Vector auto regression of systematic and unsystematic risk.
Multiple Linear Regression Applications in Real Estate Pricinginventionjournals
In this paper, we attempt to predict the price of a real estate individual homes sold in North West Indiana based on the individual homes sold in 2014. The data/information is collected from realtor.com. The purpose of this paper is to predict the price of individual homes sold based on multiple regression model and also utilize SAS forecasting model and software. We also determine the factors influencing housing prices and to what extent they affect the price. Independent variables such square footage, number of bathrooms, and whether there is a finished basement,. and whether there is brick front or not and the type of home: Colonial, Cotemporary or Tudor. How much does each type of home (Colonial, Contemporary, Tudor) add to the price of the real estate
Clay Staires | Leadership Development | www.claystaires.com Why am I so tired? Why am I not fulfilled by my leadership position? These are common questions asked by leaders and Clay explains the two reasons why leaders fail to reach their full capacity and impact.
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 ...
Forecasting Economic Activity using Asset PricesPanos Kouvelis
This dissertation evaluates how well the asset prices and, in particular the term spread, the short rate and the real stock returns, forecast the GDP growth and the Industrial Production. The study is applied with data of seven countries (Canada, France, Germany, Italy, Japan, United Kingdom and United States) and it covers a period of time between 1966 until now. The research finds that the asset prices have forecasting power for one quarter/month but they lose their power when the forecasting horizon increases. Moreover, the paper evaluates that the real stock return is the best predictor of the GDP growth and that the short rate has more predictive content than the term spread.
Keywords: Term spread, short rate, stock returns, output growth, forecasting horizon, out-of-sample statistics
This paper investigates the link between forecast disparity and macroeconomic instability that results from the data revision of GDP and inflation in Japan. The recent Japanese case, which reflects the unconventional monetary policy conducted since 2013, is also examined. The empirical results show that such disparities do not cause economic instability; however, they have have done so after the unconventional and drastic monetary policy was conducted. On the other hand, exchange rates impacted economic stability for the total period. For the first part of the period under study (from 2000 to 2012), currency appreciation caused instability; however, for the more recent period, depreciation has caused such instability. Recently, macroeconomic instability has been linked with exchange rate movements.
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 ...
Accounting Research Center, Booth School of Business, Universi.docxnettletondevon
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
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted
digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about
JSTOR, please contact [email protected]
Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at
http://about.jstor.org/terms
Accounting Research Center, Booth School of Business, University of Chicago, Wiley
are collaborating with JSTOR to digitize, preserve and extend access to Journal of Accounting Research
This content downloaded from 198.246.186.26 on Sun, 11 Sep 2016 22:17:51 UTC
All use subject to http://about.jstor.org/terms
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. Journal of Accounting and Economics
Reviewer’s Report B
Title of Manuscript: Liquidity risk and accounting information
Reference #: Vol. 52 No. 1, March 20
Report Due Date: December 9, 2015
Report Date: December 9, 2015
************************************************************************
Ruoqing Li
2. Review Report
This paper based on Lang and Maffett’s study and Ng’s study find that the accounting
information impact the liquidity risk. Lang and Maffet find that transparency decrease firm level
liquidity uncertainty. Ng studies that the information quality has a negative relation with liquidity
risk. This study found that the accounting variables can affect firm valuation and cost of capital.
In this study, Dr. Ronnie issues that the accounting information have a significant role in liquidity
happening. I think this paper’s advantage is that Dr. Ronnie collects that prior study findings.
Before early 1980s. liquidity was connected with net profitability. The liquidity helped to establish
the standard asset pricing models. In early 2000s, a lot of studies found that liquidity not only can
significantly affect net profitability, but it can also serve as an investment signal as it can expect
future performance. Recent studies focus on the liquidity risk rather than risk levels. For example,
the liquidity risk is priced in the cross-section of stock return even after controlling for firm
liquidity level (Korajczyk and Sadka 2008). In sum, the previous studies show liquidity and asset
pricing has explained that net profitability of trading stratagem and liquidity level and liquidity
risk. This article examines the Lang and Maffett’s and Ng’s study to find some new result and
evidence. In section 2, the author briefly introduces the liquidity and accounting’s development.
This paper mostly focuses on recent works that the relation between four different variables: firm
return, firm liquidity, market return and market liquidity. The author set up a covariance matrix to
show these recent study’s relations. I think it is a good example for me, which the author clearly
explain what should be discover in future and recent. In the Matrix, the Acharya and Pedersen
3. study on the Firm level and Market level, the Lang and Maffet focus on firm level, and the others
(include Ng) focus on the Firm return and Market liquidity. In recent studies, the Lang and Maffet
show three significant outcomes. The first result appears that more transparency due to lower
liquidity uncertainty. The second one is that during liquidity crises, the results own higher index
rather than normal period’s. The last one is Finally, measures of liquidity uncertainty are negatively
related to firm valuation as measured by Tobin’s Q. The Ng’s find shows the Information quality
lead to lower liquidity beta which is measured liquidity risk. In this section, author join other factor
to build a new measurement, the author’s result support prior literatures result. In the final section,
base on the author’s analysis, the result of Ronnie Sadka shows high information quality can help
investor to keep away the losses in liquidity period.
I think this paper own some lack in literature reviews. To compare with Ng’s paper, I found
Ng reference a lot of paper from 2000 to 2009, but this paper has same range which is focus on
2002 to 2005. Ng’s paper wrote earlier than the Ronnie’s, so that the Ng’s data collection is shorter
than Ronnie’s. In my mind, the Ng’s contribution is bigger than Ronnie’s, because Ng find the
higher information quality lower liquidity risk, Ronnie’s main contribution extend to explain Ng
and other’s finding can be supported by recently evidence. In this study, Dr. Ronnie summary Ng
and Lang and Maffet’s paper. This part is very clearly explaining the development of research
accounting information and liquidity. Dr. Ronnie base on the prior studies and build a new cross-
sectional regression that mix previous studies’ factor. This creative support Ng and Lang’s research,
so that further research will have new way to developing the accounting information research.
To understand this paper, I have to reference Lang and Maffet, Ng, and Pastor and
4. Stambaugh’s research, because this paper is a comment for these papers. This paper doesn’t
introduce lots of new factor, the author base on prior study factor and compare them in Ng’s
regression analysis. The author finds that prior study was supported by new measurement. The
author first changes the measurement that information quality measure as conditioning variables
for liquidity risk and market risk. The author find that new results are same with Ng’s test, but new
results’ regression coefficient are stable over the prior result. To build perfect effect, the author use
Pastor and Stambaugh (2003) liquidity factor and Sadka (2006). This test find information quality
reduce information asymmetry and then it impacts the information component of liquidity.
Although the author use this approach is not bad, he doesn’t give us some new ideas. He should
consider introduce some new factor that impact information quality. If I write this paper, I consider
some employee or manager salary ratio or ESOP. Because employee and manager understand some
insider information, the insider information disclosure will impact information quality. While I
find Ng and Pastor’s paper own a similar Fig that explain the market liquidity 1962 to 2008. The
stock market crisis happened in 1987. The market liquidity reduced to the lowest in the world.
Almost stock changed to illiquidity. So that it gives us some evidence for the liquidity risk impact
the stock price. The Ng’s research focus on United States Stock market, Lang and Maffet’s research
focus on the global accounting information data, but we can find some similar result. For example,
Loss Freq own similar trend in two test. As a good writer, he should find this point. Loss for
investor is bad news. Although we find the loss have relationship with liquidity risk, but the author
doesn't consider some solution, I think solution to reduce the liquidity risk is easy such as the
company can enhance the operating information disclosure when the company developing well.
5. The investor like good news rather than the bad news. Good news will help to solute the liquidity
risk.
The second lack is the author explain Ng and Lang research, but he doesn't connect two paper
together. Although two paper focus on different factor, Lang study the firm level, Ng study the
market level. The two level should be connected by firm liquidity risk and market liquidity risk,
as the Fig.1 in this paper. When I see the Fig.1, I have an exception that the author will show a
whole picture on the liquidity, but the discussion doesn't meet the exception. In addition, prior
researches don’t discover liquidity risk in firm level and firm return. I think this part also is very
important in research field. Liquidity risk directly impact the market return. High liquidity can
help investor quickly trading, but the stock return will lower. Some stocks own high returns, but
the stock will be illiquidity. Liquidity risk in firm level hardly measure as an individual variable.
In firm level, the liquidity can be measured by cost of capital. Cost of capital can impact firm
return.
The last lack, the author doesn’t build a new model for this research. Although the author
discusses a model, the model only explain information quality reduce liquidity risk which in turn
reduces the risk premium. I think a good paper that should have good model. It should have some
different measurement in its empirical test.
This research’s contribution also doesn't ignore. This paper’s Fig.1 is good point. It is clearly
explaining the prior literature’s relationship. From this Fig, I understand market return and market
liquidity is future research way. I think market liquidity and market return’s relation is easy to
seem, but we can research detail factors relationship in this system. The paper is some creativity
6. that it brings some old factor in new model. I look at such a research first time. It is not only doing
literature review but also getting a new result. For example, the table 1 is good. In Ng’s paper, Ng
use information quality as dependent variable, liquidity risk as independent variable. This paper
information quality is independent variable; the author uses the liquidity risk model as dependent
variable. While the research data from prior researches, so that it tests result is effective by
developing of accounting research. When I see the table 3, I feel the author give us a detail
information about information quality and liquidity risk in financial crisis period. The liquidity
ratio in sep-08 is -0.0815 to Feb-09 is -0.2788. Why the liquidity shows this influence? Because
when the beginning of the financial crisis, a lot of people consider the market return should be go
down, the stock in market will sale. But when the financial crisis will finish, the stockholder will
buy stock. So that in Dec-09 the liquidity ratio reduces to -0.1110. The information quality shows
a different trend. When the financial crisis period, the company disclosure some good news to keep
the company stock price. So that information quality will increase. I think this table is very good
for explaining the information quality and liquidity risk effect in financial crisis period. Although
Lang and Maffet do similar explains is their research, they test too complex to easy get point.
Above all, this paper is lack of creativity, but it builds a good contribution in limitation. The
first it supports information quality can help investor to keep away liquidity risk. This find is only
finding in this paper. Although the finding has limitation in accounting research, it tells investors
should look for market information and firm information in their investment. If the firm have risk,
but it will take some gain in future. As an investor, you don't give up investment for worry about
the liquidity risk or illiquidity risk. If you consider higher information quality, it is enough to keep
7. away the loss. You don't need to give up investment. In China, the Chinese stock is experiencing
stock problem. In my pinion, if Chinese stock market increase the transparency and information
quality, the stock price should be increased later. I believe this paper is not only research, but also
give some advance in future market management and accounting information.
8. References
Acharya, V.V., Pedersen, L.H., 2005. Asset pricing with liquidity risk. Journal of Financial
Economics 77, 375–410.
Amid, Y., 2002. Illiquidity and stock returns: cross-section and time-series effects. Journal
of Financial Markets 5, 31–56.
Brown, S., Hillegeist, S.A., 2007. How disclosure quality affects the level of information
asymmetry. Review of Accounting Studies 12, 443–477.
Chordia, T., Goyal, A., Sadka, G., Sadka, R., Shivakumar, L., 2009. Liquidity and the
post-earnings-announcement drift. Financial Analysts Journal 65, 18–32.
Chordia, T., Roll, R., Subrahmanyam, A., 2000. Commonality in liquidity. Journal of
Financial Economics 56, 3–28.
Diamond, D.W., Verrecchia, R.E., 1991. Disclosure, liquidity, and the cost of capital.
Journal of Finance 46, 1325–1359.
Easley, D., Hvidkjaer, S., O’Hara, M., 2002. Is information risk a determinant of asset
returns? The Journal of Finance 58, 2185–2210. 152
Fama, E.F., French, K.R., 1993. Common risk factors in the returns on stocks and bonds.
Journal of Financial Economics 33, 3–56.
9. Francis, J., Lafond, R., Olsson, P., Schipper, K., 2007. Information uncertainty and post-
earnings-announcement-drift. Journal of Business, Finance and Accounting 34, 403–433.
Hameed, A., Kang, W., Viswanathan, W., 2010. Stock market declines and liquidity.
Journal of Finance 65, 257–293.
Heaton, J., Lucas, D.J., 1996. Evaluating the effects of incomplete markets on risk sharing
and asset pricing. Journal of Political Economy 104, 443–487.
Knez, P.J., Ready, M.J., 1996. Estimating the profits from trading strategies. Review of
Financial Studies 9, 1121–1164.
Korajczyk, R.A., Sadka, R., 2004. Are momentum profits robust to trading costs? Journal of
Finance 59, 1039–1082.
Korajczyk, R.A., Sadka, R., 2008. Pricing the commonality across alternative measures of
liquidity. Journal of Financial Economics 87, 45–72.
Lang, M., Maffett, M. Transparency and liquidity uncertainty in crisis periods. Journal of
Accounting and Economics, this issue. doi:10.1016/j.jacceco.2011. 07.001.
Lesmond, D.A., Schill, M.J., Zhou, C., 2004. The illusory nature of momentum profits.
Journal of Financial Economics 71, 349–380.
10. Lou, X., Sadka, R., 2011. Liquidity level or liquidity risk? Evidence from the financial
crisis. Financial Analysts Journal 67, 51–62.
Mendenhall, R., 2004. Arbitrage risk and post-earnings-announcement drift. Journal of
Business 77, 875–894.
Mitchell, M., Pulvino, T., 2001. Characteristics of risk and return in risk arbitrage. Journal
of Finance 56, 2135–2175.
Ng, J. The effect of information quality on liquidity risk. Journal of Accounting and
Economics, this issue. doi:10.1016/j.jacceco.2011.03.004.
Pastor, L., Stambaugh, R.F., 2003. Liquidity risk and expected stock returns. Journal of
Political Economy 111, 642–685.
Sadka, G., Sadka, R., 2009. Predictability and the earnings-returns relation. Journal of
Financial Economics 94, 87–106.
Sadka, R., 2006. Momentum and post-earnings-announcement drift anomalies: the role of
liquidity risk. Journal of Financial Economics 80, 309–349.
Vega, C., 2006. Stock price reaction to public and private information. Journal of Financial
Economics 82, 103–133.
11. Welker, M., 1995. Disclosure policy, information asymmetry, and liquidity in equity
markets. Contemporary Accounting Research 11, 801–827.
