Stock (geology)

About: Stock (geology) is a research topic. Over the lifetime, 31009 publications have been published within this topic receiving 783542 citations.

Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?

Abstract: Goyal and Welch (2007) argue that the historical average excess stock return forecasts future excess stock returns better than regressions of excess returns on predictor variables. In this article, we show that many predictive regressions beat the historical average return, once weak restrictions are imposed on the signs of coefficients and return forecasts. The out-of-sample explanatory power is small, but nonetheless is economically meaningful for mean-variance investors. Even better results can be obtained by imposing the restrictions of steady-state valuation models, thereby removing the need to estimate the average from a short sample of volatile stock returns. (JEL G10, G11) Towards the end of the last century, academic finance economists came to take seriously the view that aggregate stock returns are predictable. During the 1980s, a number of papers studied valuation ratios, such as the dividend-price ratio, earnings-price ratio, or smoothed earnings-price ratio. Value-oriented investors in the tradition of Graham and Dodd (1934) had always asserted that high valuation ratios are an indication of an undervalued stock market and should predict high subsequent returns, but these ideas did not carry much weight in the academic literature until authors such as Rozeff (1984), Fama and French (1988), and Campbell and Shiller (1988a, 1988b) found that valuation ratios are positively correlated with subsequent returns and that the implied predictability of returns is substantial at longer horizons. Around the same time, several papers pointed out that yields on short- and long-term treasury and corporate bonds are correlated with subsequent stock returns (Fama and Schwert,1977;KeimandStambaugh,1986;Campbell,1987;FamaandFrench, 1989).

Stock Prices, Earnings, and Expected Dividends

Abstract: This paper presents estimates indicating that, for aggregate U.S. stock market data 1871-1986, a long historical average of real earnings is a good predictor of the present value of future real dividends. This is true even when the information contained in stock prices is taken into account. We estimate that for each year the optimal forecast of the present value of future real dividends is roughly a weighted average of moving average earnings and current real price, with between 2/3 and 3/4 of the weight on the earnings measure. This means that simple present value models of stock market prices can be strongly rejected. We use a vector autoregressive approach which enables us to compute the implications of this for the behavior of stock prices and returns. We estimate that log dividend-price ratios are more variable than, and virtually uncorrelated with, their theoretical counterparts given the present value models. Annual returns on stocks are quite highly correlated with their theoretical counterparts, but are two to four times as variable. Our approach also reveals the connection between recent papers showing forecastability of long-horizon returns on corporate stocks, and earlier literature claiming that stock prices are too volatile to be accounted for in terms of simple present value models. We show that excess volatility directly implies the forecastability of long-horizon returns.

Bad News Travels Slowly: Size, Analyst Coverage and the Profitability of Momentum Strategies

Abstract: A number of theories have been proposed to explain the medium-term momentum in stock returns identified by Jegadeesh and Titman (1993). We test one such theory--based on the gradual-information-diffusion model of Hong and Stein (1997)--and establish three key results. First, once one moves past the very smallest stocks (where thin market-making capacity appears to be an issue) the profitability of momentum strategies declines sharply with firm size. Second, holding size fixed, momentum strategies work particularly well among stocks which have low analyst coverage. Finally, there is a strong asymmetry: the effect of analyst coverage is much more pronounced for stocks that are past losers than for stocks that are past winners. These findings are consistent with the hypothesis that firm-specific information only gradually across the investing public.

Differences of Opinion and the Cross Section of Stock Returns

Abstract: We provide evidence that stocks with higher dispersion in analysts’ earnings forecasts earn lower future returns than otherwise similar stocks. This effect is most pronounced in small stocks and stocks that have performed poorly over the past year. Interpreting dispersion in analysts’ forecasts as a proxy for differences in opinion about a stock, we show that this evidence is consistent with the hypothesis that prices will ref lect the optimistic view whenever investors with the lowest valuations do not trade. By contrast, our evidence is inconsistent with a view that dispersion in analysts’ forecasts proxies for risk. IN THIS PAPER WE ANALYZE THE ROLE of dispersion in analysts’ earnings forecasts in predicting the cross section of future stock returns. We find that stocks with higher dispersion in analysts’ earnings forecasts earn significantly lower future returns than otherwise similar stocks. In particular, a portfolio of stocks in the highest quintile of dispersion underperforms a portfolio of stocks in the lowest quintile of dispersion by 9.48 percent per year. This effect is strongest in small stocks, and stocks that have performed poorly over the past year. Our results are robust to various risk-adjustment techniques, and are inconsistent with an interpretation of dispersion in analysts’ forecasts as a proxy for risk. We postulate that dispersion in analysts’ earnings forecasts can be viewed as a proxy for differences of opinion among investors. Differences of opinion are typically modeled via dogmatic beliefs or asymmetric information sets, and have been included in numerous models that relax the standard