The Cross-Section of Expected Stock Returns

Two easily measured variables, size and book-to-market equity, combine to capture the cross-sectional variation in average stock returns associated with market β, size, leverage, book-to-market equity, and earnings-price ratios. Moreover, when the tests allow for variation in β that is unrelated to size, the relation between market β and average return is flat, even when β is the only explanatory variable.     

Stock Returns,Expected Returns,and Real Activity

Measuring the total return variation explained by shocks to expected cash flows, time-varying expected returns, and shocks to expected returns is one way to judge the rationality of stock prices. Variables that proxy for expected returns and expected-return shocks capture 30% of the variance of annual NYSE value-weighted returns. Growth rates of production, used to proxy for shocks to expected cash flows, explain 43% of the return variance. Whether the combined explanatory power of the variables—about 58% of the variance of annual returns—is good or bad news about market efficiency is left for the reader to judge.     

Idiosyncratic Volatility Covariance and Expected Stock Returns

Given that the idiosyncratic volatility (IDVOL) of individual stocks co‐varies, we develop a model to determine how aggregate idiosyncratic volatility (AIV) may affect the volatility of a portfolio with a finite number of stocks. In portfolio and cross‐sectional tests, we find that stocks whose returns are more correlated with AIV innovations have lower returns than those that are less correlated with AIV innovations. These results are robust to controlling for the stock's own IDVOL and market volatility. We conclude that risk‐averse investors pay a premium for stocks that pay well when AIV is high, consistent with our model.     

Ex Ante Skewness and Expected Stock Returns

We use option prices to estimate ex ante higher moments of the underlying individual securities’ risk‐neutral returns distribution. We find that individual securities’ risk‐neutral volatility, skewness, and kurtosis are strongly related to future returns. Specifically, we find a negative (positive) relation between ex ante volatility (kurtosis) and subsequent returns in the cross‐section, and more ex ante negatively (positively) skewed returns yield subsequent higher (lower) returns. We analyze the extent to which these returns relations represent compensation for risk and find evidence that, even after controlling for differences in co‐moments, individual securities’ skewness matters.     

Another Look at the Cross-section of Expected Stock Returns

Our examination of the cross-section of expected returns reveals economically and statistically significant compensation (about 6 to 9 percent per annum) for beta risk when betas are estimated from time-series regressions of annual portfolio returns on the annual return on the equally weighted market index. The relation between book-to-market equity and returns is weaker and less consistent than that in Fama and French (1992). We conjecture that past book-to-market results using COMPUS-TAT data are affected by a selection bias and provide indirect evidence.     

Monetary Policy and the Cross-Section of Expected Stock Returns

Ample evidence shows that size and book-to-market equity explain significant cross-sectional variation in stock returns, whereas beta explains little or none of the variation. Recent studies also demonstrate that proxies for monetary stringency increase the explained variation in stock returns. We reexamine a three-factor model that includes beta, size, and book-to-market equity, while allowing monetary conditions to influence the relations between these risk factors and average stock returns. We find that ex-ante proxies for monetary stringency significantly influence the relations between stock returns and all three risk factors. Additionally, all three variables are found to contribute significantly to explaining cross-sectional returns in a three-factor model that includes the monetary sector.     

Expected Inflation, Interest Rates, and Stock Returns

This paper documents a long-lived asymmetrical relationship between interest rate changes and subsequent stock returns. Drops in interest rates are followed by twelve months of excess stock returns, while increases in interest rates have little effect. The results are robust to the choices of short-term interest rate and stock index. These findings cannot be explained by Geske and Roll's [10] reversed causality argument; nor do they appear to result from periods of unusual interest rates or stock returns. Since interest rate changes are generally used as proxies for changes in expected inflation, the results provide new insights into previous research on inflation and stock returns, and there are important implications for the literature on time-varying risk premia.     

Consumption, Aggregate Wealth, and Expected Stock Returns

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 that deviations from this shared trend summarize agents' expectations of future returns on the market portfolio.     

Dividend Dynamics,Learning, and Expected Stock Index Returns

We present a latent variable model of dividends that predicts, out‐of‐sample, 39.5% to 41.3% of the variation in annual dividend growth rates between 1975 and 2016. Further, when learning about dividend dynamics is incorporated into a long‐run risks model, the model predicts, out‐of‐sample, 25.3% to 27.1% of the variation in annual stock index returns over the same time horizon, with learning contributing approximately half of the predictability in returns. These findings support the view that investors' aversion to long‐run risks and their learning about these risks are important in determining stock index prices and expected returns.     

Corporate Governance and Expected Stock Returns: Evidence from Germany

Recent empirical work shows evidence for higher valuation of firms in countries with a better legal environment. We investigate whether differences in the quality of firm‐level corporate governance also help to explain firm performance in a cross‐section of companies within a single jurisdiction. Constructing a broad corporate governance rating (CGR) for German public firms, we document a positive relationship between governance practices and firm valuation. There is also evidence that expected stock returns are negatively correlated with firm‐level corporate governance, if dividend yields are used as proxies for the cost of capital. An investment strategy that bought high‐CGR firms and shorted low‐CGR firms earned abnormal returns of around 12% on an annual basis during the sample period.     

Structural Changes in Expected Stock Returns Relationships: Evidence from ASE

Abstract:  This paper suggests a recursive application of Fama and MacBeth's (1973) testing procedure to assess the significance of macroeconomic factors and firm-specific effects priced in explaining the cross-sectional variation of expected stock returns over time. The paper applies the suggested testing procedure to investigate the source of risks of the Athens Stock Exchange (ASE). Among the variables examined, it finds out that the changes in the short term interest rates and firm size can explain a significant proportion of the variation of the ASE individual returns. The paper argues that the significance of interest rate changes can be associated with monetary policy changes introduced by the Greek authorities after the mid-nineties. These changes were focused on targeting interest rates, instead of monetary aggregates.     

Value versus Growth: Time‐Varying Expected Stock Returns

Is the value premium predictable? We study time variations of the expected value premium using a two‐state Markov switching model. We find that when conditional volatilities are high, the expected excess returns of value stocks are more sensitive to aggregate economic conditions than the expected excess returns of growth stocks. As a result, the expected value premium is time varying. It spikes upward in the high volatility state, only to decline more gradually in the subsequent periods. However, out‐of‐sample predictability of the value premium is close to nonexistent.     

Expected Option Returns

This paper examines expected option returns in the context of mainstream asset-pricing theory. Under mild assumptions, expected call returns exceed those of the underlying security and increase with the strike price. Likewise, expected put returns are below the risk-free rate and increase with the strike price. S&P index option returns consistently exhibit these characteristics. Under stronger assumptions, expected option returns vary linearly with option betas. However, zero-beta, at-the-money straddle positions produce average losses of approximately three percent per week. This suggests that some additional factor, such as systematic stochastic volatility, is priced in option returns.     

Accounting Ratios and the Cross‐section of Expected Stock Returns

Under clean‐surplus accounting, the log return on a stock can be decomposed into a linear function of the contemporaneous log return on equity, the contemporaneous log dividend–price ratio (if the stock pays a dividend), and both the contemporaneous and lagged values of the log book‐to‐market equity ratio. This paper studies the implications of this decomposition for the cross‐section of conditional expected stock returns. The empirical analysis reveals that the log accounting ratios capture cross‐sectional variation in both the conditional mean and conditional variance of log stock returns, which is consistent with the decomposition. It also brings fresh insights to the relation between firm size (market equity) and conditional expected stock returns. The evidence indicates that the conditional median return increases with firm size, while the conditional return skewness decreases with firm size. Empirically, the skewness effect outweighs the median effect, leading to the well‐documented inverse relation between size and average returns. The results of out‐of‐sample tests suggest that investors could use the information provided by the observed values of the log accounting ratios to formulate more effective portfolio strategies.     

Debt/Equity Ratio and Expected Common Stock Returns: Empirical Evidence

The expected common stock returns are positively related to the ratio of debt (noncommon equity liabilities) to equity, controlling for the beta and firm size and including as well as excluding January, though the relation is much larger in January. This relationship is not sensitive to variations in the market proxy, estimation technique, etc. The evidence suggests that the “premium” associated with the debt/equity ratio is not likely to be just some kind of “risk premium”.     

Expected Idiosyncratic Volatility Measures and Expected Returns

We find that idiosyncratic volatility forecasts using information available to traders at the time of the forecast are not related to expected returns. The positive relation documented in a number of other papers only exists when forward‐looking information is incorporated into the volatility estimate. That positive relation is driven by the realized idiosyncratic volatility component that cannot be forecasted by investors. Our findings are robust to several different empirical tests, volatility forecasting models and time periods.     

The Cross-Section of Expected Stock Returns: New Evidence from an Emerging Market

Over 300 factors have been found to explain the cross-section of expected stock returns. Empirical studies also show that findings from multifactor asset-pricing models have not been consistent in an emerging market. Using DuPont analysis and a residual income valuation model for 284 nonfinancial companies on Ho Chi Minh Stock Exchange during the period 2008–2014, findings suggest that the return on equity and its change are informative for stock returns in Vietnam. In addition, the level of capital turnover, financial cost ratio (FCR), and changes in capital and in the FCR contain incremental explanatory power for stock returns.