This paper studies the cross-sectional properties of return forecasts derived from Fama-MacBeth regressions. These forecasts mimic how an investor could, in real time, combine many firm characteristics to obtain a composite estimate of a stockâs expected return. Empirically, the forecasts vary substantially across stocks and have strong predictive power for actual returns. For example, using ten-year rolling estimates of Fama- MacBeth slopes and a cross-sectional model with 15 firm characteristics (all based on low-frequency data), the expected-return estimates have a cross-sectional standard deviation of 0.87% monthly and a predictive slope for future monthly returns of 0.74, with a standard error of 0.07.

/pdf/the-cross-section-of-expected-stock-returns-3oji85htnb.pdf

The Cross-section of Expected Stock Returns

https://faculty.fuqua.duke.edu/~charvey/Research/Working_Papers/W73_The_economic_implications.pdf

The economic implications of corporate financial reporting

Our article comprehensively reexamines the performance of variables that have been suggested by the academic literature to be good predictors of the equity premium. We find that by and large, these models have predicted poorly both in-sample (IS) and out-of-sample (OOS) for 30 years now; these models seem unstable, as diagnosed by their out-of-sample predictions and other statistics; and these models would not have helped an investor with access only to available information to profitably time the market.

/pdf/a-comprehensive-look-at-the-empirical-performance-of-equity-a8mudsid3i.pdf

A Comprehensive Look at the Empirical Performance of Equity Premium Prediction

We examine the pricing of aggregate volatility risk in the cross-section of stock returns. Consistent with theory, we find that stocks with high sensitivities to innovations in aggregate volatility have low average returns. Stocks with high idiosyncratic volatility relative to the Fama and French (1993, Journal of Financial Economics 25, 2349) model have abysmally low average returns. This phenomenon cannot be explained by exposure to aggregate volatility risk. Size, book-to-market, momentum, and liquidity effects cannot account for either the low average returns earned by stocks with high exposure to systematic volatility risk or for the low average returns of stocks with high idiosyncratic volatility. IT IS WELL KNOWN THAT THE VOLATILITY OF STOCK RETURNS varies over time. While considerable research has examined the time-series relation between the volatility of the market and the expected return on the market (see, among others, Campbell and Hentschel (1992) and Glosten, Jagannathan, and Runkle (1993)), the question of how aggregate volatility affects the cross-section of expected stock returns has received less attention. Time-varying market volatility induces changes in the investment opportunity set by changing the expectation of future market returns, or by changing the risk-return trade-off. If the volatility of the market return is a systematic risk factor, the arbitrage pricing theory or a factor model predicts that aggregate volatility should also be priced in the cross-section of stocks. Hence, stocks with different sensitivities to innovations in aggregate volatility should have different expected returns. The first goal of this paper is to provide a systematic investigation of how the stochastic volatility of the market is priced in the cross-section of expected stock returns. We want to both determine whether the volatility of the market

https://www0.gsb.columbia.edu/faculty/rhodrick/crosssection.pdf

The Cross-Section of Volatility and Expected Returns

Earnings Management and Investor Protection: An International Comparison

This paper takes a new look at the predictability of stock market returns with risk measures. We ¢nd a signi¢cant positive relation between average stock variance (largely idiosyncratic) and the return on the market. In contrast, the variance of the market has no forecasting power for the market return. These relations persist after we control for macroeconomic variables known to forecast the stock market. The evidence is consistent with models of timevarying risk premia based on background risk and investor heterogeneity. Alternatively, our ¢ndings can be justi¢ed by the option value of equity in the capital structure of the ¢rms. MOSTASSET PRICING MODELS, starting with Merton’s (1973) ICAPM, suggest a positive relation between risk and return for the aggregate stock market. There is a long empirical literature that has tried to establish the existence of such a tradeoi between risk and return for stock market indices. 1 Unfortunately, the results have been inconclusive. Often the relation between risk and return has been found insigni¢cant, and sometimes even negative. The innovation in this paper is to look at average stock risk in addition to market risk.We measure average stock risk in each month similarly to Campbell et al. (2001; hereafter CLMX), as the cross-sectional average of the variances of all the stocks traded in that month.We then run predictive regressions of market returns on this variance measure as well as the variance of the market. Consistent with some previous studies, we ¢nd that market variance has no forecasting power for the market return. However, we do ¢nd a signi¢cant positive relation between average stock variance and the return on the market.

/pdf/idiosyncratic-risk-matters-18samrc723.pdf

Idiosyncratic Risk Matters

Our paper suggests a simple, recursive residuals (out-of-sample) graphical approach to evaluating the predictive power of popular equity premium and stock market time-series forecasting regressions. When applied, we find that dividend ratios should have been known to have no predictive ability even prior to the 1990s, and that any seeming ability even then was driven by only two years, 1973 and 1974. Our paper also documents changes in the time-series processes of the dividends themselves and shows that an increasing persistence of dividend-price ratio is largely responsible for the inability of dividend ratios to predict equity premia. Cochrane's (1997) accounting identity--that dividend ratios have to predict long-run dividend growth or stock returns--empirically holds only over horizons longer than 5--10 years. Over shorter horizons, dividend yields primarily forecast themselves.

/pdf/predicting-the-equity-premium-with-dividend-ratios-1zap0vwyf7.pdf

Predicting the Equity Premium with Dividend Ratios

This paper takes a new look at the predictability of stock market returns with risk measures. We find a significant positive relation between average stock variance (largely idiosyncratic) and the return on the market. In contrast,the variance of the market has no forecasting power for the market return. These relations persist after we control for macroeconomic variables known to forecast the stock market. The evidence is consistent with models of time-varying risk premia based on background risk and investor heterogeneity. Alternatively, our findings can be justified by the option value of equity in the capital structure of the firms.

This paper documents a strong relationship between short-run reversals and stock return illiquidity, even after controlling for trading volume. The largest reversals and the potential contrarian trading strategy profits occur in the high turnover, low liquidity stocks, as the price pressures caused by non-informational demands for immediacy are accommodated. Thus, the high frequency negative autocorrelations are more likely to result from stresses in the market for liquidity. The contrarian trading strategy profits are smaller than the likely transactions costs because the high turnover, low liquidity stocks face large transaction and market impact costs. This lack of profitability and the fact that the overall findings are consistent with rational equilibrium paradigms suggest that the violation of the efficient market hypothesis due to short-term reversals is not so egregious after all.

Amit Goyal

Papers

A Comprehensive Look at the Empirical Performance of Equity Premium Prediction

Idiosyncratic Risk Matters

Predicting the Equity Premium with Dividend Ratios

Idiosyncratic Risk Matters

Liquidity and Autocorrelations in Individual Stock Returns