Predicting stock returns

This paper studies whether incorporating business cycle predictors benefits a real time optimizing investor who must allocate funds across 3,123 NYSE-AMEX stocks and cash. Realized returns are positive when adjusted by the Fama-French and momentum factors as well as by the size, book-to-market, and past return characteristics. The investor optimally holds small-cap, growth, and momentum stocks and loads less (more) heavily on momentum (small-cap) stocks during recessions. Returns on individual stocks are predictable out-of-sample due to alpha variation, whereas the equity premium predictability, the major focus of previous work, is questionable.     

Models for stock returns

Historically, the normal variance model has been used to describe stock return distributions. This model is based on taking the conditional stock return distribution to be normal with its variance itself being a random variable. The form of the actual stock return distribution will depend on the distribution for the variance. In practice, the distributions chosen for the variance appear to be very limited. In this note, we derive a comprehensive collection of formulas for the actual stock return distribution, covering some sixteen flexible families. The corresponding estimation procedures are derived by the method of moments and the method of maximum likelihood. We feel that this work could serve as a useful reference and lead to improved modelling with respect to stock market returns.     

Long term dependence in stock returns

We show, using the modified rescaled range statistic, that none of the return series of indices of five European countries, the United States and Japan exhibits long term dependence. This statistic — introduced by Lo (1991) — correct Hurst's (1951) ‘classical’ rescaled range statistic for short term dependence. We also report the classical rescaled range statistic after adjusting the series for short term dependence. This procedure shows, for cases where the results of the modified rescaled range statistic are mixed, that no long term dependence can be found. Simulations indicate reasonable power of this adjustment procedure. Furthermore, we find that estimates of the Hurst exponent, a related measure of long term dependence, are also biased by short term dependence. Simulations show that this measure — that has recently attracted growing interest — cannot distinguish between models with or without long term dependence.     

A monthly effect in stock returns

The mean return for stocks is positive only for days immediately before and during the first half of calendar months, and indistinguishable from zero for days during the last half of the month. This ‘monthly effect’ is independent of other known calendar anomalies such as the January effect documented by others and appears to be caused by a shift in the mean of the distribution of returns from days in the first half of the month relative to days in the last half.     

Long-term dependence in common stock returns

The efficient market, martingale model of security price movements requires that the arrival of new information be promptly arbitraged away. A necessary and sufficient condition for the existence of an arbitraged price is that statistical dependence among prices must decrease very rapidly. If persistent statistical dependence is present, the arbitraged price changes do not follow a martingale and should have an infinite variance. Using a technique for detecting long-term dependence, called R/S analysis, 200 daily stock return series are studied; many series are characterized by long-term dependence. Thus, in the presence of long-term dependence, the martingale model does not hold. Also, the distribution of security returns is non-normal stable Paretian as opposed to Gaussian.     

Market efficiency, the Mexican peso crisis, and the US bank stock returns: An application of the event parameter method

This paper examines the impact of the 1994 Mexican peso crisis on US bank returns using an event parameter approach. The event parameter approach explicitly predicts the stochastic return generating process on the occurrence or nonoccurrence of specific events. The event parameter method assumes that only the intercept term may vary between the estimation and analysis periods. Specifically, this study will examine two pairs of hypotheses, new information vs. information leakage and rational pricing vs. investor contagion, in the context of the 1994 Mexican peso and banking crisis. The empirical results support the new information hypothesis, which states that US stock prices reacted quickly to events related to the Mexican peso crisis. This research has also found evidence for bank contagion, although these did not spill over to other banks. The US government and international agencies acted promptly to contain the effect of the Mexican peso and banking crisis spreading to the US and to other Latin American countries.     

Multifrequency news and stock returns

Equity prices are driven by shocks with persistence levels ranging from intraday horizons to several decades. To accommodate this diversity, we introduce a parsimonious equilibrium model with regime shifts of heterogeneous durations in fundamentals, and estimate specifications with up to 256 states on daily aggregate returns. The multifrequency equilibrium has higher likelihood than the Campbell and Hentschel [1992. No news is good news: an asymmetric model of changing volatility in stock returns. Journal of Financial Economics 31, 281–318] specification, while producing volatility feedback 10 to 40 times larger. Furthermore, Bayesian learning about volatility generates a novel trade-off between skewness and kurtosis as information quality varies, complementing the uncertainty channel [e.g., Veronesi, 1999. Stock market overreaction to bad news in good times: a rational expectations equilibrium model. Review of Financial Studies 12, 975–1007]. Economies with intermediate information best match daily returns.     

Revenue surprises and stock returns

This paper examines the relation between revenue surprises and contemporaneous and future stock returns. It also investigates whether analysts update their earnings forecasts in response to revenue surprises in a timely and unbiased fashion. Stock price reaction on the earnings announcement date is significantly related to contemporaneous as well as past revenue surprises. After controlling for earnings surprises, we find significant abnormal returns in the post-announcement period for stocks that have large revenue surprises. Although analysts revise their forecasts of future earnings in response to revenue surprises, they are slow to incorporate fully the information in revenue surprises.     

Innovative efficiency and stock returns

We find that innovative efficiency (IE), patents or citations scaled by research and development expenditures, is a strong positive predictor of future returns after controlling for firm characteristics and risk. The IE-return relation is associated with the loading on a mispricing factor, and the high Sharpe ratio of the Efficient Minus Inefficient (EMI) portfolio suggests that mispricing plays an important role. Further tests based upon attention and uncertainty proxies suggest that limited attention contributes to the effect. The high weight of the EMI portfolio return in the tangency portfolio suggests that IE captures incremental pricing effects relative to well-known factors.     

Comoment risk and stock returns

We estimate investable comoment equity risk premiums for the US markets. The stock's contribution to the asymmetry and the fat tails of the market portfolio's payoff are priced into a coskewness premium and a cokurtosis premium. We construct zero-investment strategies that are long and short in coskewness and cokurtosis equity risks; we infer from the spread the returns attached to a unit exposure to US equity coskewness and cokurtosis. The coskewness and cokurtosis premiums present positive monthly average returns of 0.27% and 0.14% from January 1959 to December 2011. Comoment risks appear to be significantly priced within the US stock market and display significant explanatory power regarding the US size and book-to-market effects. The premiums do not subsume, but rather complement the empirical capital asset pricing model. Our analysis relies on data collected from CRSP (Chicago Research Center for Security Prices) over December 1955 to December 2011. To our knowledge, the paper is the first to propose investable higher-moment risk factors over such an extensive time period.     

Asset liquidity and stock returns

We document the significant predictive power of firms' asset liquidity in the cross section of subsequent stock returns. The annual return spread between portfolios featuring the highest and lowest levels of asset liquidity is significantly positive. Our proposed measure of asset liquidity outperforms those measures developed by Gopalan et al. (2012) in predicting returns. The asset liquidity anomaly also provides significantly positive alphas when controlling for the asset pricing factors in the Fama and French (1993) three-factor model and the Carhart (1997) four-factor model. Asset liquidity exhibits strong return forecasting power even after controlling for acknowledged cross-sectional determinants of return. The positive relation between asset liquidity and future returns tends to be stronger for firms with greater asset productivity, higher quality cash flow and lower capital investment.     

Google searches and stock returns

We investigate whether data from Google Trends can be used to forecast stock returns. Previous studies have found that high Google search volumes predict high returns for the first one to two weeks, with subsequent price reversal. By using a more recent dataset that covers the period from 2008 to 2013 we find that high Google search volumes lead to negative returns. We also examine a trading strategy based on selling stocks with high Google search volumes and buying stocks with infrequent Google searches. This strategy is profitable when the transaction cost is not taken into account but is not profitable if we take into account transaction costs.     

The cross-section of stock returns in an early stock market

Using a new dataset which contains monthly data on 1015 stocks traded on the London Stock Exchange between 1825 and 1870, we investigate the cross section of stock returns in this early capital market. Unique features of this market allow us to evaluate the veracity of several popular explanations of asset pricing behavior. Using portfolio analysis and Fama–MacBeth regressions, we find that stock characteristics such as beta, illiquidity, dividend yield, and past-year return performance are all positively correlated with stock returns. However, market capitalization and past-three-year return performance have no significant correlation with stock returns.     

Investor recognition and stock returns

It is well established that investment fundamentals, such as earnings and cash flows, can explain only a small proportion of the variation in stock returns. We find that investor recognition of a firm’s stock can explain relatively more of the variation in stock returns. Consistent with Merton’s (J Finance 42(3):483–510, 1987) theoretical analysis, we show that (i) contemporaneous stock returns are positively related to changes in investor recognition, (ii) future stock returns are negatively related to changes in investor recognition, (iii) the above relations are stronger for stocks with greater idiosyncratic risk and (iv) corporate investment and financing activities are both positively related to changes in investor recognition. Our research suggests that investors and managers who are concerned with firm valuation should consider investor recognition in addition to accounting information and related investment fundamentals.     

Predicting firm stock returns with customer stock returns: Moderating effects of customer characteristics

Based on the multiple regression model, this study examines the potential predictive effect of customer stock returns to firm stock returns and the moderating effect of diverse customer characteristics on the predictability. By using a sample of Chinese A-share manufacturing firms listed on the Shanghai stock exchange and Shenzhen stock exchange between 2009 and 2017, we find that customer stock returns positively predict firm stock returns in the subsequent month. Additional examinations reveal that the positive predictive effect of customer stock returns on firm stock returns is more intense for firm with high proportion of state-owned customers, customer stability, customer bargaining power and customer concentration than for those with low indicators. Overall, this study contributes to the growing literature on supply chain and predictability of stock returns by shedding light on the forecasting effect of customer stock returns on firm stock returns and the predictive heterogeneity owing to customer characteristics.     

Geographic dispersion and stock returns

This paper shows that stocks of truly local firms have returns that exceed the return on stocks of geographically dispersed firms by 70 basis points per month. By extracting state name counts from annual reports filed with the Securities and Exchange Commission (SEC) on Form 10-K, we distinguish firms with business operations in only a few states from firms with operations in multiple states. Our findings are consistent with the view that lower investor recognition for local firms results in higher stock returns to compensate investors for insufficient diversification.     

Expected stock returns and volatility

This paper examines the relation between stock returns and stock market volatility. We find evidence that the expected market risk premium (the expected return on a stock portfolio minus the Treasury bill yield) is positively related to the predictable volatility of stock returns. There is also evidence that unexpected stock market returns are negatively related to the unexpected change in the volatility of stock returns. This negative relation provides indirect evidence of a positive relation between expected risk premiums and volatility.     

Slow-moving capital and stock returns

This paper studies the effects that delay in capital allocations in the stock market and high short-term trading incentives have on returns of this market. We report that capital inertia makes the Sharpe ratio and the volatility of the stock returns many times higher than in an economy with no capital delays. Furthermore, in agreement with empirical literature, the stock price displays short-term overreaction and high volatility of the conditional Sharpe ratio.     

Financial constraints and stock returns

We test whether the impact of financial constraints on firmvalue is observable in stock returns. We form portfolios offirms based on observable characteristics related to financialconstraints and test for common variation in stock returns.Financially constrained firms' stock returns move together overtime, suggesting that constrained firms are subject to commonshocks. Constrained firms have low average stock returns inour 1968-1997 sample of growing manufacturing firms. We findno evidence that the relative performance of constrained firmsreflects monetary policy, credit conditions, or business cycles.     

Earnings acceleration and stock returns

We document that earnings acceleration, defined as the quarter-over-quarter change in earnings growth, has significant explanatory power for future excess returns. These excess returns are robust to a wide range of previously documented anomalies and a battery of risk controls. The future return predictability appears to be consistent with investors assuming a seasonal random walk model for quarterly earnings and missing predictable implications of earnings acceleration for future earnings growth. Finally, the excess returns from the basic earnings acceleration strategy can be enhanced further by focusing on profit firms, low earnings volatility firms and on specific patterns of earnings acceleration.