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290
Common Risk Factors in the Returns On Stocks And Bonds
 Journal of Financial Economics
, 1993
"... This paper identities five common risk factors in the returns on stocks and bonds. There are three stockmarket factors: an overall market factor and factors related to firm size and booktomarket equity. There are two bondmarket factors. related to maturity and default risks. Stock returns have s ..."
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Cited by 955 (24 self)
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This paper identities five common risk factors in the returns on stocks and bonds. There are three stockmarket factors: an overall market factor and factors related to firm size and booktomarket equity. There are two bondmarket factors. related to maturity and default risks. Stock returns have shared variation due to the stockmarket factors, and they are linked to bond returns through shared variation in the bondmarket factors. Except for lowgrade corporates. the bondmarket factors capture the common variation in bond returns. Most important. the five factors seem to explain average returns on stocks and bonds. 1.
Risks for the long run: A potential resolution of asset pricing puzzles
 JOURNAL OF FINANCE
, 1994
"... We model consumption and dividend growth rates as containing (i) a small longrun predictable component and (ii) fluctuating economic uncertainty (consumption volatility). These dynamics, for which we provide empirical support, in conjunction with Epstein and Zin’s (1989) preferences, can explain ke ..."
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Cited by 350 (30 self)
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We model consumption and dividend growth rates as containing (i) a small longrun predictable component and (ii) fluctuating economic uncertainty (consumption volatility). These dynamics, for which we provide empirical support, in conjunction with Epstein and Zin’s (1989) preferences, can explain key asset markets phenomena. In our economy, financial markets dislike economic uncertainty and better longrun growth prospects raise equity prices. The model can justify the equity premium, the riskfree rate, and the volatility of the market return, riskfree rate, and the pricedividend ratio. As in the data, dividend yields predict returns and the volatility of returns is timevarying.
Illiquidity and Stock Returns: Crosssection and Timeseries Effects
 Journal of Financial Markets
, 2002
"... This paper shows that over time, expected market illiquidity positively affects ex ante stock excess return, suggesting that expected stock excess return partly represents an illiquidity premium. This complements the crosssectional positive return–illiquidity relationship. Also, stock returns are n ..."
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Cited by 297 (3 self)
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This paper shows that over time, expected market illiquidity positively affects ex ante stock excess return, suggesting that expected stock excess return partly represents an illiquidity premium. This complements the crosssectional positive return–illiquidity relationship. Also, stock returns are negatively related over time to contemporaneous unexpected illiquidity. The illiquidity measure here is the average across stocks of the daily ratio of absolute stock return to dollar volume, which is easily obtained from daily stock data for long time series in most stock markets. Illiquidity affects more strongly small firm stocks, thus explaining time series variations in their premiums over
Investing for the long run when returns are predictable
 Journal of Finance
, 2000
"... We examine how the evidence of predictability in asset returns affects optimal portfolio choice for investors with long horizons. Particular attention is paid to estimation risk, or uncertainty about the true values of model parameters. We find that even after incorporating parameter uncertainty, th ..."
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Cited by 283 (0 self)
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We examine how the evidence of predictability in asset returns affects optimal portfolio choice for investors with long horizons. Particular attention is paid to estimation risk, or uncertainty about the true values of model parameters. We find that even after incorporating parameter uncertainty, there is enough predictability in returns to make investors allocate substantially more to stocks, the longer their horizon. Moreover, the weak statistical significance of the evidence for predictability makes it important to take estimation risk into account; a longhorizon investor who ignores it may overallocate to stocks by a sizeable amount. ONE OF THE MORE STRIKING EMPIRICAL FINDINGS in recent financial research is the evidence of predictability in asset returns. 1 In this paper we examine the implications of this predictability for an investor seeking to make sensible portfolio allocation decisions. We approach this question from the perspective of horizon effects: Given the evidence of predictability in returns, should a longhorizon investor allocate his wealth differently from a shorthorizon investor? The motivation for thinking about the problem in these terms is the classic work of Samuelson ~1969! and Merton ~1969!. They show that if asset returns are i.i.d., an investor with power utility who rebalances his portfolio optimally should choose the same asset allocation, regardless of investment horizon. In light of the growing body of evidence that returns are predictable, the investor’s horizon may no longer be irrelevant. The extent to which the horizon does play a role serves as an interesting and convenient way of thinking about how predictability affects portfolio choice. Moreover, the results may shed light on the common but controversial advice that investors with long horizons should allocate more heavily to stocks. 2
Term Premia and Interest Rate Forecasts in Affine Models
, 2001
"... I find that the standard class of a#ne models produces poor forecasts of future changes in Treasury yields. Better forecasts are generated by assuming that yields follow random walks. The failure of these models is driven by one of their key features: The compensation that investors receive for faci ..."
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Cited by 250 (8 self)
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I find that the standard class of a#ne models produces poor forecasts of future changes in Treasury yields. Better forecasts are generated by assuming that yields follow random walks. The failure of these models is driven by one of their key features: The compensation that investors receive for facing risk is a multiple of the variance of the risk. This means that risk compensation cannot vary independently of interest rate volatility. I also describe and empirically estimate a class of models that is broader than the standard a#ne class. These "essentially a#ne" models retain the tractability of the usual models, but allow the compensation for interest rate risk to vary independently of interest rate volatility. This additional flexibility proves useful in forming accurate forecasts of future yields. Address correspondence to the University of California, Haas School of Business, 545 Student Services Building #1900, Berkeley, CA 94720. Phone: 5106421435. Email address: du#ee@haas.b...
The Determinants of Credit Spread Changes
, 2001
"... Using dealer’s quotes and transactions prices on straight industrial bonds, we investigate the determinants of credit spread changes. Variables that should in theory determine credit spread changes have rather limited explanatory power. Further, the residuals from this regression are highly crossco ..."
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Cited by 224 (2 self)
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Using dealer’s quotes and transactions prices on straight industrial bonds, we investigate the determinants of credit spread changes. Variables that should in theory determine credit spread changes have rather limited explanatory power. Further, the residuals from this regression are highly crosscorrelated, and principal components analysis implies they are mostly driven by a single common factor. Although we consider several macroeconomic and financial variables as candidate proxies, we cannot explain this common systematic component. Our results suggest that monthly credit spread changes are principally driven by local supply0 demand shocks that are independent of both creditrisk factors and standard proxies for liquidity.
Conditional skewness in asset pricing tests
 Journal of Finance
, 2000
"... If asset returns have systematic skewness, expected returns should include rewards for accepting this risk. We formalize this intuition with an asset pricing model that incorporates conditional skewness. Our results show that conditional skewness helps explain the crosssectional variation of expect ..."
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Cited by 150 (6 self)
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If asset returns have systematic skewness, expected returns should include rewards for accepting this risk. We formalize this intuition with an asset pricing model that incorporates conditional skewness. Our results show that conditional skewness helps explain the crosssectional variation of expected returns across assets and is significant even when factors based on size and booktomarket are included. Systematic skewness is economically important and commands a risk premium, on average, of 3.60 percent per year. Our results suggest that the momentum effect is related to systematic skewness. The low expected return momentum portfolios have higher skewness than high expected return portfolios. THE SINGLE FACTOR CAPITAL ASSET PRICING MODEL ~CAPM! of Sharpe ~1964! and Lintner ~1965! has come under recent scrutiny. Tests indicate that the crossasset variation in expected returns cannot be explained by the market beta alone. For example, a growing number of studies show that “fundamental” variables such as size, booktomarket value, and price to earnings ratios
Consumption, Aggregate Wealth, and Expected Stock Returns
 THE JOURNAL OF FINANCE • VOL. LVI, NO. 3 • JUNE 2001
, 2001
"... This paper studies the role of fluctuations in the aggregate consumption–wealth ratio for predicting stock returns. Using U.S. quarterly stock market data, we find that these fluctuations in the consumption–wealth ratio are strong predictors of both real stock returns and excess returns over a Treas ..."
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Cited by 150 (18 self)
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This paper studies the role of fluctuations in the aggregate consumption–wealth ratio for predicting stock returns. Using U.S. quarterly stock market data, we find that these fluctuations in the consumption–wealth ratio are strong predictors of both real stock returns and excess returns over a Treasury bill rate. We also find that this variable is a better forecaster of future returns at short and intermediate horizons than is the dividend yield, the dividend payout ratio, and several other popular forecasting variables. Why should the consumption–wealth ratio forecast asset returns? We show that a wide class of optimal models of consumer behavior imply that the log consumption–aggregate wealth ~human capital plus asset holdings! ratio summarizes expected returns on aggregate wealth, or the market portfolio. Although this ratio is not observable, we provide assumptions under which its important predictive components for future asset returns may be expressed in terms of observable variables, namely in terms of consumption, asset holdings and labor income. The framework implies that these variables are cointegrated, and
A Comprehensive Look at the Empirical Performance of Equity Premium Prediction,” working paper
, 2004
"... Given the historically high equity premium, is it now a good time to invest in the stock market? Economists have suggested a whole range of variables that investors could or should use to predict: dividend price ratios, dividend yields, earningsprice ratios, dividend payout ratios, net issuing rati ..."
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Cited by 125 (4 self)
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Given the historically high equity premium, is it now a good time to invest in the stock market? Economists have suggested a whole range of variables that investors could or should use to predict: dividend price ratios, dividend yields, earningsprice ratios, dividend payout ratios, net issuing ratios, bookmarket ratios, interest rates (in various guises), and consumptionbased macroeconomic ratios (cay). The typical paper reports that the variable predicted well in an insample regression, implying forecasting ability. Our paper explores the outofsample performance of these variables, and finds that not a single one would have helped a realworld investor outpredicting the thenprevailing historical equity premium mean. Most would have outright hurt. Therefore, we find that, for all practical purposes, the equity premium has not been predictable, and any belief about whether the stock market is now too high or too low has to be based on theoretical prior, not on the empirically variables we have explored.
Asset pricing at the millennium
 Journal of Finance
"... This paper surveys the field of asset pricing. The emphasis is on the interplay between theory and empirical work and on the tradeoff between risk and return. Modern research seeks to understand the behavior of the stochastic discount factor ~SDF! that prices all assets in the economy. The behavior ..."
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Cited by 123 (3 self)
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This paper surveys the field of asset pricing. The emphasis is on the interplay between theory and empirical work and on the tradeoff between risk and return. Modern research seeks to understand the behavior of the stochastic discount factor ~SDF! that prices all assets in the economy. The behavior of the term structure of real interest rates restricts the conditional mean of the SDF, whereas patterns of risk premia restrict its conditional volatility and factor structure. Stylized facts about interest rates, aggregate stock prices, and crosssectional patterns in stock returns have stimulated new research on optimal portfolio choice, intertemporal equilibrium models, and behavioral finance. This paper surveys the field of asset pricing. The emphasis is on the interplay between theory and empirical work. Theorists develop models with testable predictions; empirical researchers document “puzzles”—stylized facts that fail to fit established theories—and this stimulates the development of new theories. Such a process is part of the normal development of any science. Asset pricing, like the rest of economics, faces the special challenge that data are generated naturally rather than experimentally, and so researchers cannot control the quantity of data or the random shocks that affect the data. A particularly interesting characteristic of the asset pricing field is that these random shocks are also the subject matter of the theory. As Campbell, Lo, and MacKinlay ~1997, Chap. 1, p. 3! put it: What distinguishes financial economics is the central role that uncertainty plays in both financial theory and its empirical implementation. The starting point for every financial model is the uncertainty facing investors, and the substance of every financial model involves the impact of uncertainty on the behavior of investors and, ultimately, on mar* Department of Economics, Harvard University, Cambridge, Massachusetts