This document analyzes approaches to assessing the volatility of private market or direct real estate as an asset class. It finds that estimates of annualized volatility for direct real estate portfolios fall in a range of 6.5-9.0% when accounting for the autocorrelation and lack of statistical independence between quarterly returns. Publicly traded REITs have a historical volatility of around 13% due to leverage, but blending REIT and bond returns estimates the underlying real estate asset volatility at around 9%. The analyses provide useful guidance for quantifying direct real estate risk in portfolio allocation.
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
This study explores performance persistence in mutual funds. The authors find:
1) Funds that perform relatively poorly compared to peers and benchmarks are more likely to disappear, indicating survivorship bias can be relevant in mutual fund studies.
2) Mutual fund performance persists from year to year on a risk-adjusted basis, though much of the persistence is due to repeated underperformance relative to benchmarks.
3) Persistence patterns vary dramatically between time periods, suggesting performance is correlated across managers due to common strategies not captured by risk adjustments. Poorly performing funds also persist instead of being fully eliminated by the market.
The document discusses strategies for creating an investment portfolio based on Nobel Prize-winning academic research. It recommends structuring portfolios to take advantage of factors like company size, relative price, and profitability that have been shown to increase returns. Specifically, it suggests investing more in small and value stocks, as both have higher returns than large or growth stocks over the long run. The document also provides examples of model portfolios that diversify across global stock and bond index funds targeting these factors.
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 .
The five steps in financial planning, forecasting internalexternal .pdfamrahlifestyle
The five steps in financial planning, forecasting internal/external finds is critical. With today\'s
economic and interest rate market conditions, along with the volitility of the captial markets,
what factors would you emphasize when you are preparing your forecasts?
Solution
Connect with Vanguard > vanguard.com Executive summary. Some say the long-run outlook for
U.S. stocks is poor (even “dead”) given the backdrop of muted economic growth, already-high
profit margins, elevated government debt levels, and low interest rates. Others take a rosier view,
citing attractive valuations and a wide spread between stock earnings yields and Treasury bond
yields as reason to anticipate U.S. stock returns of 8%–10% annually, close to the historical
average, over the next decade. Given such disparate views, which factors should investors
consider when formulating expectations for stock returns? And today, what do those factors
suggest is a reasonable range to expect for stock returns going forward? We expand on previous
Vanguard research in using U.S. stock returns since 1926 to assess the predictive power of more
than a dozen metrics that investors would know ahead of time. We find that many commonly
cited signals have had very weak and erratic correlations with actual subsequent returns, even at
long investment horizons. These poor Vanguard research October 2012 Forecasting stock
returns: What signals matter, and what do they say now? Authors Joseph Davis, Ph.D. Roger
Aliaga-Díaz, Ph.D. Charles J. Thomas, CFA 2 predictors include trailing values for dividend
yields and economic growth, the difference between the stock market’s earnings yield and
Treasury bond yields (the so-called Fed Model), profit margins, and past stock returns. We
confirm that valuation metrics such as price/earnings ratios, or P/Es, have had an inverse or
mean-reverting relationship with future stock market returns, although it has only been
meaningful at long horizons and, even then, P/E ratios have “explained” only about 40% of the
time variation in net-of-inflation returns. Our results are similar whether or not trailing earnings
are smoothed or cyclically adjusted (as is done in Robert Shiller’s popular P/E10 ratio). The
current level of a blend of valuation metrics contributes to Vanguard’s generally positive outlook
for the stock market over the next ten years (2012–2022). But the fact that even P/Es—the
strongest of the indicators we examined—leave a large portion of returns unexplained
underscores our belief that expected stock returns are best stated in a probabilistic framework,
not as a “point forecast,” and should not be forecast over short horizons. The variation of
expected returns Forming reasonable long-run return expectations for stocks and other asset
classes can be important in devising a strategic asset allocation. But what precisely are
“reasonable” expectations in the current environment, and how should they be formed? For
instance, should investors expect t.
The document summarizes research on value investing in emerging markets. It finds that:
1) A simple valuation model can identify emerging markets with higher expected returns compared to average emerging markets.
2) A portfolio of "undervalued" emerging markets identified by the model generates superior returns compared to benchmarks, with statistical significance.
3) Risk measures of the portfolio of undervalued emerging markets are close to risk measures of broader emerging market benchmarks, implying the higher returns are not compensated by significantly higher risk.
Lodging REIT Analysis - Keynote Presentation for Research Committee by Brad K...Brad Kuskin
Although numerous studies examine REIT performance over extended periods of time, many online and data-driven investment tools do not adequately provide existing and prospective investors with the tools necessary to extract business management risk out of lodging REIT returns. Given investors' current reliance on technology and graphic-oriented return analysis, it is critical that lodging REIT shareholders understand that not all equity REITs are equal. Typically, investors govern by a combination of return on capital and diversification. However, lodging REITs are inherently misleading due to their "equity REIT" classification.
As lodging REITs expand to encompass a vast portion of the hospitality industry, particularly marquis lodging assets in primary metropolitan markets, an accurate comprehension of inherent risks is critical for any investor considering deploying capital into a lodging REIT.
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.
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
This study explores performance persistence in mutual funds. The authors find:
1) Funds that perform relatively poorly compared to peers and benchmarks are more likely to disappear, indicating survivorship bias can be relevant in mutual fund studies.
2) Mutual fund performance persists from year to year on a risk-adjusted basis, though much of the persistence is due to repeated underperformance relative to benchmarks.
3) Persistence patterns vary dramatically between time periods, suggesting performance is correlated across managers due to common strategies not captured by risk adjustments. Poorly performing funds also persist instead of being fully eliminated by the market.
The document discusses strategies for creating an investment portfolio based on Nobel Prize-winning academic research. It recommends structuring portfolios to take advantage of factors like company size, relative price, and profitability that have been shown to increase returns. Specifically, it suggests investing more in small and value stocks, as both have higher returns than large or growth stocks over the long run. The document also provides examples of model portfolios that diversify across global stock and bond index funds targeting these factors.
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 .
The five steps in financial planning, forecasting internalexternal .pdfamrahlifestyle
The five steps in financial planning, forecasting internal/external finds is critical. With today\'s
economic and interest rate market conditions, along with the volitility of the captial markets,
what factors would you emphasize when you are preparing your forecasts?
Solution
Connect with Vanguard > vanguard.com Executive summary. Some say the long-run outlook for
U.S. stocks is poor (even “dead”) given the backdrop of muted economic growth, already-high
profit margins, elevated government debt levels, and low interest rates. Others take a rosier view,
citing attractive valuations and a wide spread between stock earnings yields and Treasury bond
yields as reason to anticipate U.S. stock returns of 8%–10% annually, close to the historical
average, over the next decade. Given such disparate views, which factors should investors
consider when formulating expectations for stock returns? And today, what do those factors
suggest is a reasonable range to expect for stock returns going forward? We expand on previous
Vanguard research in using U.S. stock returns since 1926 to assess the predictive power of more
than a dozen metrics that investors would know ahead of time. We find that many commonly
cited signals have had very weak and erratic correlations with actual subsequent returns, even at
long investment horizons. These poor Vanguard research October 2012 Forecasting stock
returns: What signals matter, and what do they say now? Authors Joseph Davis, Ph.D. Roger
Aliaga-Díaz, Ph.D. Charles J. Thomas, CFA 2 predictors include trailing values for dividend
yields and economic growth, the difference between the stock market’s earnings yield and
Treasury bond yields (the so-called Fed Model), profit margins, and past stock returns. We
confirm that valuation metrics such as price/earnings ratios, or P/Es, have had an inverse or
mean-reverting relationship with future stock market returns, although it has only been
meaningful at long horizons and, even then, P/E ratios have “explained” only about 40% of the
time variation in net-of-inflation returns. Our results are similar whether or not trailing earnings
are smoothed or cyclically adjusted (as is done in Robert Shiller’s popular P/E10 ratio). The
current level of a blend of valuation metrics contributes to Vanguard’s generally positive outlook
for the stock market over the next ten years (2012–2022). But the fact that even P/Es—the
strongest of the indicators we examined—leave a large portion of returns unexplained
underscores our belief that expected stock returns are best stated in a probabilistic framework,
not as a “point forecast,” and should not be forecast over short horizons. The variation of
expected returns Forming reasonable long-run return expectations for stocks and other asset
classes can be important in devising a strategic asset allocation. But what precisely are
“reasonable” expectations in the current environment, and how should they be formed? For
instance, should investors expect t.
The document summarizes research on value investing in emerging markets. It finds that:
1) A simple valuation model can identify emerging markets with higher expected returns compared to average emerging markets.
2) A portfolio of "undervalued" emerging markets identified by the model generates superior returns compared to benchmarks, with statistical significance.
3) Risk measures of the portfolio of undervalued emerging markets are close to risk measures of broader emerging market benchmarks, implying the higher returns are not compensated by significantly higher risk.
Lodging REIT Analysis - Keynote Presentation for Research Committee by Brad K...Brad Kuskin
Although numerous studies examine REIT performance over extended periods of time, many online and data-driven investment tools do not adequately provide existing and prospective investors with the tools necessary to extract business management risk out of lodging REIT returns. Given investors' current reliance on technology and graphic-oriented return analysis, it is critical that lodging REIT shareholders understand that not all equity REITs are equal. Typically, investors govern by a combination of return on capital and diversification. However, lodging REITs are inherently misleading due to their "equity REIT" classification.
As lodging REITs expand to encompass a vast portion of the hospitality industry, particularly marquis lodging assets in primary metropolitan markets, an accurate comprehension of inherent risks is critical for any investor considering deploying capital into a lodging REIT.
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.
Convertible bonds can provide diversification benefits and higher risk-adjusted returns than equities or bonds in a low-yield environment. NN Investment Partners examines the historical performance of convertible bonds globally and in Japan, where convertibles outperformed stocks and bonds for the past decade. The document describes a predictive model for convertible bond returns based on stochastic diffusion of key market factors. The model aims to assess how convertibles may contribute positively to asset allocation going forward in the current low-yield environment.
[LATAM EN] The use of convertible bonds in the asset allocation processNN Investment Partners
Convertible bonds can provide diversification benefits and higher risk-adjusted returns than other asset classes in a low-yield environment. The document examines historical performance of convertible bonds globally and in Japan, which experienced low yields for over a decade. A simulation model is described that predicts future convertible bond returns based on stochastic diffusion of key market factors. The document concludes convertible bonds deserve consideration for asset allocation given their ability to participate in equity upside while providing downside protection from bond floors.
The investment philosophy focuses on efficient market investing through portfolio design and implementation that targets dimensions of higher expected returns like value, size, and profitability. It believes prices reflect all available information and aims to add value not by forecasting but by pursuing risk premia in a low-cost, diversified portfolio. Traditional active management often relies on forecasting and generates higher costs without consistent outperformance, while index funds provide little flexibility.
Investment analysis and portfolio management quantitative methods of investme...Arif Hossain FCA
The objective of investment analysis and portfolio management study is to help entrepreneurs and practitioners to understand the investments field as it is currently understood and practiced for sound investment decisions making. Following this objective, key concepts are presented to provide an appreciation of the theory and practice of investments, focusing on investment portfolio formation and management issues. This study is designed to emphasize both theoretical and analytical aspects of investment decisions and deals with modern investment theoretical concepts and instruments. Both descriptive and quantitative materials on
Investment analysis and portfolio management quantitative methods of investme...Arif Hossain FCA
The objective of investment analysis and portfolio management study is to help entrepreneurs and practitioners to understand the investments field as it is currently understood and practiced for sound investment decisions making. Following this objective, key concepts are presented to provide an appreciation of the theory and practice of investments, focusing on investment portfolio formation and management issues. This study is designed to emphasize both theoretical and analytical aspects of investment decisions and deals with modern investment theoretical concepts and instruments. Both descriptive and quantitative materials on.............................
This document provides an analysis and updated expectations for long-term capital market returns. It estimates that U.S. stocks will provide a total return of 6-8% over the long-term and bonds will return 3-4%. These estimates are based on reasonable assumptions about inflation, dividend income, dividend growth, and valuation shifts. The document examines historical returns and factors to derive its projections, which are meant to provide a guide for long-term financial planning.
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.
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.
Liquidity Risk and Expected Stock Returns Lubos Pastor and Robert F- S.docxLucasmHKChapmant
Liquidity Risk and Expected Stock Returns Lubos Pastor and Robert F. Stambaugh NBER Working Paper No. 8462 September 2001 JEL No. G12 ABSTRACT This study investigates whether market-wide liquidity is a state variable important for asset pricing. We find that expected stock returns are related cross-sectionally to the sensitivities of returns to fluctuations in aggregate liquidity. Our monthly liquidity measure, an average of individual-stock measures estimated with daily data, relies on the principle that order flow induces greater return reversals when liquidity is lower. Over a 34-year period, the average retum on stocks with high sensitivities to liquidity exceeds that for stocks with low sensitivities by 7.5% annually, adjusted for exposures to the market return as well as size, value, and momentum factors. 1. Introduction In standard asset pricing theory, expected stock returns are related cross-sectionally to returns' senxitivities to state variables with pervasive effects on consumption and invertment opportunities. The basic intuition is that a security whose lowest returns tend to accompany unfavorable shifts in quantities afferting an imvestor's overall welfare must offer additional compensation to the investor for holding that security. Liquidity appears to be a good candidate for a priced state variable. It is often viewed as important for investment decisions, and recent studies find that fluctuations in various measures of liquidity are correlated acroos stocks." This empirical study investigates whether market-wide liquidity is indeed priced. That is, we ask whether cross-sectional differences in expected stock returns are rehated to the sensitivities of returns to fluctuations in aggregate liquidity. 2 Liquidity is a broad and elusive concept that generally denotes the ability to trade large quantities quickly, at low cost, and without moving the price. We focus on an aspect of liquidity associated with temporary price fluctuations induced by order flow. Our monthly aggregate liquidity measure is a cross-sectional average of individual-stock liquidity measures. Each stock's liquidity in a given month, etimated using that stock's within-month daily returns and volume, represents the average effect that a given volume on day d has on the return for day d + 1 , when the volume is given the same sign as the return on day d . The basic idea is that, if signed volume is viewed ronghly as "order flow," then lower liquidity is reflected in a greater tendency for order flow in a given direction on day d to be followed by a price change in the opposite direction on day d + 1 . Esentially, lower liquidity corresponds to stronger volume-related return reversals, and in this respect our liquidity measure follows the same line of reasoning as the model and empirical evidence presented by Campbell, Groseman, and Wang (1993). They find that sturns accompanied by high volume tend to be reversed more strongly, and they explain how this result i.
1. The document analyzes alternative benchmarks for evaluating the performance of mutual funds that invest in real estate investment trusts (REITs). It compares using a simple REIT index to using multiple-factor models that account for characteristics like firm size, book-to-market ratio, and property type.
2. The study finds that including additional factors like these improves the explanatory power of performance models and lowers estimates of abnormal returns for many REIT mutual funds. Including returns of non-REIT real estate firms like homebuilders and real estate operating companies also enhances the models.
3. While benchmark choice has a modest effect on measured performance of the overall REIT mutual fund market, individual fund performance can be more
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.
The document analyzes the risk-adjusted performance of various asset classes over one, three, and five year periods. It finds that over all periods, fixed income assets have generated the highest Sharpe ratios, a measure of risk-adjusted return. Risk parity has also performed well over longer periods. Equities and other assets have seen higher returns but rank lower on a risk-adjusted basis.
Not only are many factors becoming really expensive due to their popularity, the realized historical returns were only half as good as they looked on paper. Since smart beta is all the rage RAFI is doing important work.
Competition and Bias by Harrison Hong and Marcin KacperczykMichael-Paul James
Competition and Bias
Paper by Harrison Hong and Marcin Kacperczyk
Presentation by Michael-Paul James
Treatment effect: a decrease in analyst covering increases optimism bias one year after the merger relative to control.
-Evidence for competition reduction bias
-Larger bias impact for stocks with less coverage
Short Stories To Write Ideas - Pagspeed. Online assignment writing service.Andrew Parish
This document provides instructions for requesting writing assistance from HelpWriting.net. It outlines a 5-step process: 1) Create an account with a password and email. 2) Complete a 10-minute order form providing instructions, sources, and deadline. 3) Review bids from writers and choose one based on qualifications. 4) Receive the paper and authorize payment if pleased. 5) Request revisions to ensure satisfaction, with a refund option for plagiarized work. The service aims to provide original, high-quality content through a bidding system and revision process.
Jacksonville- Michele Norris Communications And The Media DietAndrew Parish
The document discusses the benefits of the minimum legal driving age. It begins by stating that the minimum legal driving age should not be increased to 18 because that would not help decrease the rate of teen accidents. While some argue accidents caused by teens justify a higher age, the document counters that maturity levels vary and delaying licensure could reduce overall experience and skills. Restricting mobility may also restrict opportunities for work, education and independence. Raising the age could even have unintended consequences of increased risk-taking.
Convertible bonds can provide diversification benefits and higher risk-adjusted returns than equities or bonds in a low-yield environment. NN Investment Partners examines the historical performance of convertible bonds globally and in Japan, where convertibles outperformed stocks and bonds for the past decade. The document describes a predictive model for convertible bond returns based on stochastic diffusion of key market factors. The model aims to assess how convertibles may contribute positively to asset allocation going forward in the current low-yield environment.
[LATAM EN] The use of convertible bonds in the asset allocation processNN Investment Partners
Convertible bonds can provide diversification benefits and higher risk-adjusted returns than other asset classes in a low-yield environment. The document examines historical performance of convertible bonds globally and in Japan, which experienced low yields for over a decade. A simulation model is described that predicts future convertible bond returns based on stochastic diffusion of key market factors. The document concludes convertible bonds deserve consideration for asset allocation given their ability to participate in equity upside while providing downside protection from bond floors.
The investment philosophy focuses on efficient market investing through portfolio design and implementation that targets dimensions of higher expected returns like value, size, and profitability. It believes prices reflect all available information and aims to add value not by forecasting but by pursuing risk premia in a low-cost, diversified portfolio. Traditional active management often relies on forecasting and generates higher costs without consistent outperformance, while index funds provide little flexibility.
Investment analysis and portfolio management quantitative methods of investme...Arif Hossain FCA
The objective of investment analysis and portfolio management study is to help entrepreneurs and practitioners to understand the investments field as it is currently understood and practiced for sound investment decisions making. Following this objective, key concepts are presented to provide an appreciation of the theory and practice of investments, focusing on investment portfolio formation and management issues. This study is designed to emphasize both theoretical and analytical aspects of investment decisions and deals with modern investment theoretical concepts and instruments. Both descriptive and quantitative materials on
Investment analysis and portfolio management quantitative methods of investme...Arif Hossain FCA
The objective of investment analysis and portfolio management study is to help entrepreneurs and practitioners to understand the investments field as it is currently understood and practiced for sound investment decisions making. Following this objective, key concepts are presented to provide an appreciation of the theory and practice of investments, focusing on investment portfolio formation and management issues. This study is designed to emphasize both theoretical and analytical aspects of investment decisions and deals with modern investment theoretical concepts and instruments. Both descriptive and quantitative materials on.............................
This document provides an analysis and updated expectations for long-term capital market returns. It estimates that U.S. stocks will provide a total return of 6-8% over the long-term and bonds will return 3-4%. These estimates are based on reasonable assumptions about inflation, dividend income, dividend growth, and valuation shifts. The document examines historical returns and factors to derive its projections, which are meant to provide a guide for long-term financial planning.
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.
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.
Liquidity Risk and Expected Stock Returns Lubos Pastor and Robert F- S.docxLucasmHKChapmant
Liquidity Risk and Expected Stock Returns Lubos Pastor and Robert F. Stambaugh NBER Working Paper No. 8462 September 2001 JEL No. G12 ABSTRACT This study investigates whether market-wide liquidity is a state variable important for asset pricing. We find that expected stock returns are related cross-sectionally to the sensitivities of returns to fluctuations in aggregate liquidity. Our monthly liquidity measure, an average of individual-stock measures estimated with daily data, relies on the principle that order flow induces greater return reversals when liquidity is lower. Over a 34-year period, the average retum on stocks with high sensitivities to liquidity exceeds that for stocks with low sensitivities by 7.5% annually, adjusted for exposures to the market return as well as size, value, and momentum factors. 1. Introduction In standard asset pricing theory, expected stock returns are related cross-sectionally to returns' senxitivities to state variables with pervasive effects on consumption and invertment opportunities. The basic intuition is that a security whose lowest returns tend to accompany unfavorable shifts in quantities afferting an imvestor's overall welfare must offer additional compensation to the investor for holding that security. Liquidity appears to be a good candidate for a priced state variable. It is often viewed as important for investment decisions, and recent studies find that fluctuations in various measures of liquidity are correlated acroos stocks." This empirical study investigates whether market-wide liquidity is indeed priced. That is, we ask whether cross-sectional differences in expected stock returns are rehated to the sensitivities of returns to fluctuations in aggregate liquidity. 2 Liquidity is a broad and elusive concept that generally denotes the ability to trade large quantities quickly, at low cost, and without moving the price. We focus on an aspect of liquidity associated with temporary price fluctuations induced by order flow. Our monthly aggregate liquidity measure is a cross-sectional average of individual-stock liquidity measures. Each stock's liquidity in a given month, etimated using that stock's within-month daily returns and volume, represents the average effect that a given volume on day d has on the return for day d + 1 , when the volume is given the same sign as the return on day d . The basic idea is that, if signed volume is viewed ronghly as "order flow," then lower liquidity is reflected in a greater tendency for order flow in a given direction on day d to be followed by a price change in the opposite direction on day d + 1 . Esentially, lower liquidity corresponds to stronger volume-related return reversals, and in this respect our liquidity measure follows the same line of reasoning as the model and empirical evidence presented by Campbell, Groseman, and Wang (1993). They find that sturns accompanied by high volume tend to be reversed more strongly, and they explain how this result i.
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Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
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3. Executive summary_______________________________ 3
Gauging the private real estate market_______________ 5
Autocorrelation and volatility_______________________ 5
Evidence from annual NCREIFreturns ________________ 6
Evidence from the REITmarket______________________ 7
Impact of real estate volatility assumptions
on asset allocation _______________________________ 8
Conclusion ______________________________________ 9
Real Estate Investment Group
The Real Estate Investment Group of JPMorgan
Fleming Asset Management, with 30 years of experience
in the private and public real estate markets, is well posi-
tioned to meet the increasing demands of institutional
real estate investors amidst a rapidly changing market
environment. The group comprises over 90 professionals,
organized within the key functional areas of portfolio
management: research, acquisitions, asset management,
finance, legal, valuation, client services, product develop-
ment, real estate securities, structured capital and fiduci-
ary services. We also have broad-based experience across
all four major property sectors (retail, offices, multifami-
ly and industrial) throughout the United States.
T
he J
PMorgan F
leming Asset Management Investment
Insight series conveys analysis and perspectives devel-
oped by J
PMorgan F
leming’s portfolio management and
research professionals. T
he series focuses on investment
topics and issues aimed at offering unique and useful
insights to ourclients.
Author:
Michael Giliberto, Ph.D., managing director, is Director
of Portfolio Strategy in the Real Estate Investment
Group. A firm employee since 1996, he focuses on port-
folio strategy and management, product design and risk
management. Previously
, he held senior research posi-
tions at Lehman Brothers and Salomon Brothers. Michael
holds a Ph.D. in finance from the University of
Washington, an M.A. in business economics from the
University of Hartford and an undergraduate degree
from Harvard University
.
Telephone: (212) 837-1693
michael.giliberto@jpmorganfleming.com
TABLE OF CONTENTS
As s e s s i n g r e a l e s t a t e vo la t i l i t y
4.
5. • The following research illustrates several
approaches to assessing the volatility of private
market or direct real estate as an asset class.
These approaches yield reasonably consistent esti-
mates, falling in a range of 6.5%–9.0% annualized
standard deviation of total return for a diversified
portfolio of unleveraged, core properties.
• Our estimates take into account the lack of statis-
tical independence from quarter to quarter that is
characteristic of private market real estate
returns. Failure to adjust for this phenomenon can
lead to understating risk.
• We believe our estimates provide useful guid-
ance for quantifying the risk of direct real estate
for purposes of evaluating real estate’s potential
role in a multi-asset class portfolio. In addition,
our empirical estimates have intuitive appeal:
they lie between investment-grade bonds and
large-cap stocks.
Executive summary
INVES TMENT INS IGHT
As s e s s i n g r e a l e s t a t e vo la t i l i t y
3
6.
7. Gauging the private real estate market
Diversification is a central principle of investing. Tools
such as mean-variance analysis provide insight into how
asset classes can be blended to produce portfolio alloca-
tions that maximize a portfolio’s expected return for a
specified level of risk; alternatively
, the allocation process
seeks to minimize risk for a target rate of return. Clearly
,
assessing risk is essential if one wishes to use such quan-
titative methods to help design portfolios.
In the private real estate market, as, say
, compared with
large-capitalization stocks, properties trade infrequently
,
transactions do not take place in continuously open, auc-
tion markets and transaction costs are high.
Consequently
, it seems unreasonable to expect data on
returns from private market investments in real estate to
have similar characteristics to liquid financial assets.
Some argue that private market real estate data exhibit
artificially lowvolatility and do not respond contempora-
neously to changes in market conditions. There is often
an implication that data are, therefore, worthless. We do
not think these concerns are unique to real estate. For
example, these concerns could potentially affect other
“alternative” investments, such as private equity
, some
categories of hedge funds and less-liquid securities.
Autocorrelation and volatility
As of December 31, 2002, the NCREIF Property Index
— which is used to provide the historical investment
risk/
return performance of private real estate — provided
100 quarters of performance data. The standard deviation
of quarterly total return is 1.7%. What is the annualized
standard deviation or volatility? The conventional answer
is 3.4%, which one gets by multiplying quarterly stan-
dard deviation by two to obtain an annualized value.
Of course, many look askance at 3.4%, saying it does not
make sense because it is too low
. The Lehman Aggregate
Bond Index, for example, has a 7.5% historical volatility
over the same 100 quarters. Howcan equity real estate
exhibit less risk?
The answer stems from the formula used to extend from
quarterly to annual measurement. The standard formula
applies when returns are statistically independent from
period to period.1
A consequence of independence is that
time series of returns will showno serial dependence or
correlation. That is, return in one period is uncorrelated
with return in another period. Many time series of finan-
cial market returns have little serial correlation, at least
when observations are made at intervals of a month or
longer. For example, monthly total returns on the
Wilshire 5000 stock index have a 0.03 correlation with
returns lagged one month. In contrast, NCREIF quarter-
ly returns have a 0.68 serial correlation. Wilshire’s 0.03
correlation is small enough to ignore when deriving
annualized standard deviation; NCREIF’s is not.
Over time, positive autocorrelation, such as that exhibit-
ed by NCREIF, causes the dispersion of return outcomes
over multi-period horizons to be greater than would be
the case with serially uncorrelated returns. This finding
has implications for risk, as it suggests higher volatility
for private market real estate returns than the reported
historical volatility
.
To illustrate these concepts, we conducted a simulation.
In the simulation, we randomly generated 20,000 obser-
vations drawn from a normal distribution with a zero
mean and standard deviation of one, which is abbreviat-
ed as N(0,1). These observations were transformed into
500 “time series” of returns, with each time series con-
taining 40 data points. Think of these as 500 samples of
“quarterly returns” over 10-year periods.
For the first batch of 500 time series, we transformed the
N(0,1) data into quarterly returns with a mean of 2.3%
and a quarterly standard deviation of 1.7%, correspon-
ding approximately to historical NCREIF data. We
assumed these quarterly returns had no serial correlation.
We then usedthe same N(0,1) datato create another
group of quarterlyreturns with serial correlation equal to
5
INVES TMENT INS IGHT
As s e s s i n g r e a l e s t a t e vo la t i l i t y
8. 6
0.68 to match NCREIF. We were careful to keep the mean
andstandarddeviation of returns within each time period
approximatelythe same for the uncorrelatedandautocor-
relatedoutcomes (see Charts 1 and2). Formal tests con-
firmedthat no differences were statisticallysignificant.
We then calculated the compound average annual return
(geometric mean) for each scenario. The distributions of
these returns clearly showthat outcomes are more dis-
persed when autocorrelation is present (see Chart 3), even
though the average return is about 9%. Simply put,
while quarterly standard deviations of returns are the
same, “risk” over a multi-period horizon is not.
This point is emphasized by transforming the distribu-
tions in Chart 3 to shortfall probabilities (see Chart 4).
For example, the probability that the cumulative return
over the investment horizon will average less than 7%
per annum, or about 2% belowthe expected value, is
about 2% for uncorrelated returns and more than 18%
for autocorrelated returns.
This section has demonstrated that the widely held view
that 3%–4% volatility for real estate is too low“no mat-
ter what the data say” is correct. This is not, however,
necessarily a shortcoming of NCREIF data or any other
return series that are based, at least in part, on infre-
quently observed private market valuations. It is a conse-
quence of the high autocorrelation that likely exists in
such series. This can be dealt with empirically
.
Evidence from annual NCREIFreturns
One technique toreduce autocorrelation’s influence on
volatilityestimates is touse annual returns (i.e., quarterly
returns compoundedover four quarters). As of December
31, 2002, NCREIF dataextendedfor 100 quarters. From
these datawe constructedfour sets of observations, one set
each for one-year periods ending in March, June,
September andDecember. Each set consists of non-overlap-
ping observations. Of course, the sets are not independent
because nearlythe same underlying dataare includedin
everyset. Table 1 presents astatistical summaryof these
annual returns.
1 4 7 10 13 16 19 22 25 28 31 34 37 40
Uncorrelated Autocorrelated
Quarter
0.025
0.024
0.023
0.022
0.021
0.020
0.018
0.019
Chart 1
Simulation model: Mean return
Source: J
PMorgan F
leming Asset Management
1 4 7 10 13 16 19 22 25 28 31 34 37 40
Uncorrelated Autocorrelated
Quarter
0.0190
0.0185
0.0180
0.0175
0.0170
0.0165
0.0160
0.0150
0.0155
Chart 2
Simulation model: Cross-sectional standard deviation
Source: J
PMorgan F
leming Asset Management
%
of
Cases
0
2% 3% 4% 5% 6% 7% 8% 10%
9% 11%12% 14%
13% 15%16%17%
5
10
15
20
25
30
35
40
Annualized return
Uncorrelated
Autocorrelated
Chart 3
Dispersion of autocorrelated returns
Source: J
PMorgan F
leming Asset Management
Return target
Probability
of
return
less
than
target
(shortfall)
120
140%
100
80
60
40
20
0
2% 3% 4% 5% 6% 7% 8% 10%
9% 11%12% 14%
13% 15%16% 17%
Uncorrelated
Autocorrelated
Chart 4
Shortfall probabilities
Source: J
PMorgan F
leming Asset Management
9. 7
The volatility of annual returns evidenced above is
clearly higher than the 3.4% volatility obtained by
annualizing quarterly standard deviations of return. As
demonstrated in the last section, this result is driven by
the significant autocorrelation that exists in NCREIF
quarterly returns.
However, using annual returns may not eliminate auto-
correlation’s effect. The annual total returns exhibited
first-order autocorrelation ranging from 0.77–0.82. The
approximate standard error for the autocorrelation in
these observations is 0.2, so the remaining autocorrela-
tion is statistically significant. We conclude that the
volatility estimated from annual NCREIF returns may
retain a downward bias relative to the “true” volatility
.
(One could, in theory
, use periods longer than one year to
further lower the effect of autocorrelation, but the num-
ber of available non-overlapping observations declines.)
Evidence from the REITmarket
Publicly traded equity real estate investment trusts
(REITs) provide an intriguing source of data for assessing
real estate risk. Giliberto and Mengden (1996) showthat
the aggregate income from private market real estate
(NCREIF data) is highly correlated with implied REIT
income.2
This result was updated and confirmed in
Giliberto (1999).3
A reasonable interpretation of these
findings is that properties held by REITs and those in
the NCREIF database are, by and large, exposed to the
same fundamental factors of supply and demand, which
drive market rents, vacancy rates and thereby cash flows.
Giliberto and Mengden attributed some of the apparent
“disconnect” between REIT returns and NCREIF per-
formance to differing valuation regimes.
The NCREIF database is constructed to be unleveraged,
whereas, REITs, in contrast, typically use leverage. As
leveraging an equity investment generally leads to
higher volatility, it, therefore, comes as no surprise that
the volatility of REIT total returns exceeds that of
NCREIF returns.
The historical volatility of equity REIT total returns is
13.5%, using monthly National Association of Real
Estate Investment Trusts (NAREIT) data from January
1972 through December 2002. The volatility is slightly
lower (12.8%) for the January 1978 through December
2002 time period, which corresponds to the NCREIF
database’s coverage. As previously mentioned, the
volatility of REIT returns reflects, in part, REITs’ use of
leverage. It is probable that the percentage of leverage
has varied over time, therefore, measured volatility picks
up “average” leverage.
To use REIT data to gain insight into the (unleveraged)
volatility of real estate assets held by REITs, we took a
page from the classic Miller-Modigliani (MM) analysis of
a firm’s capital structure. MM opined that shareholders
could undo corporate leverage structure by simply buying
a firm’s debt. (Or they could leverage up by borrowing to
buy equity shares.) A simple, macro approach to delever-
aging REITs is to blend REIT equity performance with
the performance of publicly traded corporate bonds. For
example, if a REIT’s (highly simplified) balance sheet is:
then a blend of 50% REIT (equity) share performance
and 50% debt performance should, in principle, approxi-
mate the performance of underlying real estate assets.4
We used Lehman Brothers’ data on bond performance,
and since many REITs currently have Baa unsecured debt
ratings, we used that component. REITs’ typical debt
issues are intermediate term (10 years and under).
Lehman provides data back to 1973 on the performance
of Baa intermediate corporate bonds. Starting in 1997,
returns are available for REITs as a sector within the
credit universe.
Since we did not have access to data on the changing
amounts of leverage on REIT balance sheets, we decided
T
able I. Mean and volatility of annual NCREIFtotal returns
Numberof Average
Y
earended observations return V
olatility
March 24 9.6% 6.4%
June 24 9.7% 6.4%
September 24 9.6% 6.3%
December 25 9.7% 6.2%
Sources: NCR
E
IF
, J
PMorgan F
leming Asset Management
Data constructed from quarterly total returns overthe period
1Q1978–4Q2002.
Assets Liabilities
Real estate 100
Debt 50
Shareholder equity 50
10. to examine several debt-equity blends. We believe this
provides a plausible range of volatility estimates. We cre-
ated two series of debt results. The first uses the overall
Lehman Baa intermediate corporate index, of which
REITs are a small component, from January 1973
through December 2002. The second debt series uses the
overall corporate bond data from 1973 through 1996 and
uses the REIT-specific bond index from 1997 onward.
Results were virtually identical for both bond series.
Chart 5 presents the results for the January 1978–
December 2002 period using the spliced bond data.
To understand these findings, let’s examine the 35%
leverage case. Suppose one bought $100 of real estate,
using $35 of borrowed money and $65 of equity
. The
volatilities of and correlation between total returns on
publicly traded REIT equity and debt are fixed by the
historical data. Specifically
, REIT volatility was 12.8%,
Baa bond volatility was 5.5% and correlation was 0.29.
Using these data, we calculatedthat the same real estate, if
financed 100% with equity
, would have had a volatility
of 9.1%. Put another way
, taking real estate that has 9.1%
volatilityandusing 35% debt financing causes the volatil-
ity of the now-leveraged equity position to rise to 12.8%.
Our belief is that over time REIT leverage probably has
been within the 35%–65% range. Reviewing the results
in Chart 5, REIT data suggest that underlying real estate
volatility lies within the 6.5%–9.1% range.
Interestingly
, when we used annual NCREIF returns to
adjust for serial correlation, volatility was approximately
6.4%. As we argued above, this estimate may still have
some downward bias because the serial correlation is quite
high and its effect may not be washed out within one
year. Additionally
, we might argue that the REIT estimates
could be biased upward since REITs’ assets include some
amount of riskier, value-added real estate. In addition,
REITs might perhaps be subject to “excess volatility” due
to being traded in the public equity market.5
Impact of real estate volatility assumptions on
asset allocation
Not surprisingly
, different numerical assumptions about
real estate volatility alter the allocation given to real
estate within a multi-asset class portfolio. To illustrate
this, we ran three portfolio optimizations. We picked a
different level of real estate volatility for each optimiza-
tion. All other factors, including expected returns,
volatilities for asset classes other than real estate, and
correlations, were held constant.6
As representative volatilities for real estate, we used (1)
3.4%, which is the annualized NCREIF volatility uncor-
rected for autocorrelation; (2) 7.6%, which is within the
range that we think is indicated by both adjusted
NCREIF and REIT data; and (3) 15%, which is more
akin to the volatility of equities. (We point out that
companies that are publicly traded often use debt financ-
ing. As a result, equity volatility assumptions reflect
financial leverage. While leverage frequently is used with
real estate, our volatility estimates and asset allocation
parameters reflect unleveraged core real estate.)
Optimizations generate ranges of portfolios along an
“efficient frontier.” We selected representative portfolios
fromeach efficient frontier using asimple rule. We picked,
in each of the three optimizations, the portfolio on the
efficient frontier that hadthe highest Sharpe ratio.7
This is
not necessarilyan appropriate guide tochoosing aportfolio
along the efficient frontier. The advantage for our exer-
cise was that this selection criterion could be applied
mechanically
. Chart 6 illustrates the outcomes. Given the
mechanical nature of the exercise, none of the allocations
in Chart 6 should be viewed as recommendations.
The results are as expected: the higher the assumed
volatility of real estate, the lower its allocation within a
portfolio. Importantly
, the influence of different assump-
tions can be significant. For example, we posit that 7.6%
8
Implied
real
estate
volatility
0
2
4
6
8
10
12%
Debt Equity
75%
9.1%
7.7%
6.5%
5.9%
25%
65%
35%
50%
50%
35%
65%
25%
75%
10.1%
Chart 5
Effects of varying debt-equity blends on implied volatility
Source: J
PMorgan F
leming Asset Management
11. is a reasonable estimate of volatility
. Doubling the
volatility to 15% causes the allocation to shrink by
almost 60% in this case. The variability of the output
(allocation) to the assumed input points out the need to
use careful sensitivity analysis in conjunction with alloca-
tion models.
Conclusion
In conclusion, our research suggests that the volatility of
a diversified portfolio of unleveraged direct, core real
estate is probably within the 6.5%–9% range. This
range estimate comes from (1) the private market, mak-
ing allowances for serial correlation and (2) the public
market, adjusting for leverage. When a narrower range is
called for, we recommend 7%–8%.
This does appear somewhat lowcompared with the 7.5%
historical volatility for the Lehman Aggregate Bond
Index. But historical bond returns include a period of
high inflation that boosted volatility
. Going forward,
bond volatility is expected to be moderate. In fact, over
the 10-year period ending December 31, 2002, the
Lehman Aggregate posted 4.2% volatility
. As a result,
bond volatility projections of 4%–5% are commonly
used today
. In that context, our recommended real estate
volatility is nearly double the bond projection, and it
will increase if the real estate is leveraged. However, we
believe that it is generally preferable to use unleveraged
real estate when examining strategic asset allocation.
Why? Because it provides the clearest perspective on
howreal estate interacts with other asset classes. And
that should lead, in turn, to more-informed portfolio
construction, which was, after all, the initial motivation
for undertaking this research.
Notes
1. The formula is annualized standard deviation equals
standard deviation of periodic return times the
square root of the number of periods in a year. For
quarterly data, this works out to multiplying by
two; for monthly data, one would multiply the
monthly standard deviation by the square root of 12.
2. Michael Giliberto and Anne Mengden, “REITs and
Real Estate: Two Markets Re-examined,” Real
Estate Finance, Volume 13, Number I.
(Spring 1996).
3. Michael Giliberto, “Public Portfolios of Private
Properties,”presentation to Association for
Investment Management andResearch conference
“ANewErafor Real Estate Investing,”
(November 1999).
4. A similar approach was used by Geltner, O’Connor
and Rodriguez. See “The Similar Genetics of Public
and Private Real Estate and the Optimal Long-
Horizon Portfolio Mix,” Real Estate Finance,
Volume 12, Number 3. (Fall 1995).
5. For an introduction to this concept in a real estate
context, see page 280 in Geltner and Miller,
Commercial Real Estate Analysis and Investments,
NewJersey: Prentice Hall, 2001.
6. We used assumptions developed by JPMorgan
Fleming’s Strategic Investment Advisory Group
(SIAG) for U.S. aggregate bonds, U.S. high yield,
U.S. large cap, U.S. small cap and international
(unhedged). The expected return and correlation
assumptions for U.S. real estate also were those used
by SIAG. This information is available upon
request.
7. The Sharpe ratio is the expected portfolio return
minus the risk-free interest rate divided by portfolio
volatility
.
9
%
of
portfolio
in
real
estate
Assumed real estate volatility
0% 2% 4% 6% 8% 10% 12% 14% 16%
0
10
20
30
40
50
60
70
80%
Chart 6
The impact of real estate volatility assumptions on asset allocation
Source: J
PMorgan F
leming Asset Management
13. ■
■ Assessing real estate volatility
■
■ Managing corporate portfolios over the credit cycle
■
■ Reviewand outlook 2003:
U.S. corporate pension financial performance
■
■ Asset allocation and sustainable payouts for endow-
ments and foundations
■
■ A look at the U.S. equity trading market:
Historical overview
, current trends and future
prospects
■
■ The impact of large assets on real estate portfolio
returns
■
■ The JPMorgan Fleming Asset Management
Fixed Income Portfolio Risk Model
■
■ A 10-step implementation plan for Currency
Overlay
■
■ An explanation of the discount/
premium puzzle in
currency markets
■
■ Characteristics of portfolio excess return
■
■ Characteristics of manager excess return
■
■ The portfolio impact of asymmetric correlations,
mean reversion and transaction costs
■
■ Determining tactical ranges
Torequest one of these titles, please e-mail your
request tojpmorganinvestment@jpmorganfleming.com
or
fax this form to (212) 837-1067
Please send to:
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