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.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

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”.     

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.     

Earnings and Expected Returns

The aggregate dividend payout ratio forecasts excess returns on both stocks and corporate bonds in postwar U.S. data. High dividends forecast high returns. High earnings forecast low returns. The correlation of earnings with business conditions gives them predictive power for returns; they contain information about future returns that is not captured by other variables. Dividends and earnings contribute substantial explanatory power at short horizons. For forecasting long-horizon returns, however, only (scaled) stock prices matter. Forecasts of low long-horizon stock returns in the mid-1990s are caused not by earnings or dividends, but by high stock prices.     

Information Uncertainty and Expected Returns

This study examines the role of information uncertainty (IU) in predicting cross-sectional stock returns. We define IU in terms of “value ambiguity,” or the precision with which firm value can be estimated by knowledgeable investors at reasonable cost. Using several different proxies for IU, we show that (1) on average, high-IU firms earn lower future returns (the “mean” effect), and (2) price and earnings momentum effects are much stronger among high-IU firms (the “interaction” effect). These findings are consistent with analytical models in which high IU exacerbates investor overconfidence and limits rational arbitrage.This revised version was published online in August 2005 with a corrected cover date.     

Expected Returns and Habit Persistence

Using a consumption-based asset pricing model with infinite-horizonnonlinear habit formation, Campbell and Cochrane (1999) showthat low consumption in surplus of habit should forecast highexpected returns. This article argues that the finite-horizonlinear habit model also implies an inverse relation betweenexpected returns and surplus consumption. This article alsopresents empirical evidence, which indicates that expected returnson stocks and bonds vary with surplus consumption implied bythe habit models. The volatility of returns and the reward tovolatility are also related to surplus consumption. However,less than 30% of the predictable variation of expected returns,using standard lagged information variables, is attributed tosurplus consumption.     

A Regime-Switching Model of Long-Term Stock Returns

Abstract

In this paper I first define the regime-switching lognormal model. Monthly data from the Standard and Poor’s 500 and the Toronto Stock Exchange 300 indices are used to fit the model parameters, using maximum likelihood estimation. The fit of the regime-switching model to the data is compared with other common econometric models, including the generalized autoregressive conditionally heteroskedastic model. The distribution function of the regime-switching model is derived. Prices of European options using the regime-switching model are derived and implied volatilities explored. Finally, an example of the application of the model to maturity guarantees under equity-linked insurance is presented. Equations for quantile and conditional tail expectation (Tail-VaR) risk measures are derived, and a numerical example compares the regime-switching lognormal model results with those using the more traditional lognormal stock return model.     

Risk and Return: A Revisit Using Expected Returns

This paper uses direct estimates of expected returns to examine the link between standard measures of financial risk and investor return requirements. The results show that systematic risk commands a significant positive risk premium, much larger than found using historical returns as proxies for expectations. Furthermore, there are nonlinearities in the relationship between risk and return. Finally, we show that expected returns and risk premiums in the equity markets change over time and that these changes are related to changes in interest rates on U.S. government obligations.     

Order Flow and Expected Option Returns

I show that the inventory risk faced by market‐makers has a first‐order effect on option prices. I introduce a simple approach that decomposes the price impact of trades into inventory risk and asymmetric information components. While both components are large for option trades, the inventory risk component is larger. Using the full panel of daily option returns, I find that option order imbalances attributable to inventory risk have five times larger impact on option prices than previously thought. Finally, I find that past order imbalances have greater predictive power than any other commonly used predictor of option returns.     

Regime Shifts in Price‐Dividend Ratios and Expected Stock Returns: A Present‐Value Approach

We incorporate regime shifts in the mean of price‐dividend ratios into the present value model of van Binsbergen and Koijen (2010) who propose a latent variable approach to modeling expected returns and dividend growth rates. We find that accounting for regime shifts results in much lower persistence of expected returns and higher volatility of expected returns, and thus higher in‐sample predictability, when compared to the results from the van Binsbergen and Koijen (2010) model. We also show that the main source of the increase in the mean of price‐dividend ratios in the mid‐1990s is a decrease in the mean of expected returns.