A behavioral explanation of the value anomaly based on time-varying return reversals

We investigate the dynamics of the value anomaly in order to identify the driving forces of the anomaly. We show that the large positive value-minus-growth portfolio returns are explained by an over-reaction (under-reaction) to the positive (negative) market movements in short, specific time periods, during which the average returns of value-minus-growth portfolios are more than 2% a month. We propose an explanation based on behavioral biases: the dynamics of the value anomaly reflect the increased speed of return reversals subsequent to overreaction. Two conditions that increase the return reversals are proposed: when investors respond to public signals asymmetrically or when public signals become noisy. Our empirical results reveal that the value anomaly is explained by either one of these two channels.     

Trading activity and price reversals in futures markets

We use the standard contrarian portfolio approach to examine short-horizon return predictability in 24 US futures markets. We find strong evidence of weekly return reversals, similar to the findings from equity market studies. When interacting between past returns and lagged changes in trading activity (volume and/or open interest), we find that the profits to contrarian portfolio strategies are, on average, positively associated with lagged changes in trading volume, but negatively related to lagged changes in open interest. We also show that futures return predictability is more pronounced if interacting between past returns and lagged changes in both volume and open interest. Our results suggest that futures market overreaction exists, and both past prices and trading activity contain useful information about future market movements. These findings have implications for futures market efficiency and are useful for futures market participants, particularly commodity pool operators.     

The Moving Average Ratio and Momentum

I show the ratio of the short‐term moving average to the long‐term moving average (moving average ratio, MAR) has significant predictive power for future returns. The MAR combined with nearness to the 52‐week high explains most of the intermediate‐term momentum profits. This suggests that an anchoring bias, in which investors use moving averages or the 52‐week high as reference points for estimating fundamental values, is the primary source of momentum effects. Momentum caused by the anchoring bias do not disappear in the long‐run even when there are return reversals, confirming that intermediate‐term momentum and long‐term reversals are separate phenomena.     

On the Role of Intangible Information and Capital Gains Taxes in Long‐Term Return Reversals

Prior studies have linked long‐term reversals to the magnitude of locked‐in capital gains suggesting that reversals are driven by tax effects and not overreaction. I find that locked‐in capital gains do not explain the reversals in winners when winner returns are based on intangible information. In fact, the reversals for intangible return winners are long lasting and robust to controls for growth in assets and capital expenditures. To the extent that reversals associated with intangible information stem from investors’ overreaction to intangible information and given the prior results linking reversals only to intangible information, my results suggest that overreaction still explains reversal patterns in US stock returns.     

Idiosyncratic risk and the cross-section of expected stock returns

Theories such as Merton [1987. A simple model of capital market equilibrium with incomplete information. Journal of Finance 42, 483–510] predict a positive relation between idiosyncratic risk and expected return when investors do not diversify their portfolio. Ang, Hodrick, Xing, and Zhang [2006. The cross-section of volatility and expected returns. Journal of Finance 61, 259–299], however, find that monthly stock returns are negatively related to the one-month lagged idiosyncratic volatilities. I show that idiosyncratic volatilities are time-varying and thus, their findings should not be used to imply the relation between idiosyncratic risk and expected return. Using the exponential GARCH models to estimate expected idiosyncratic volatilities, I find a significantly positive relation between the estimated conditional idiosyncratic volatilities and expected returns. Further evidence suggests that Ang et al.'s findings are largely explained by the return reversal of a subset of small stocks with high idiosyncratic volatilities.     

News-driven return reversals: Liquidity provision ahead of earnings announcements

This study documents a six-fold increase in short-term return reversals during earnings announcements relative to non-announcement periods. Following prior research, we use reversals as a proxy for expected returns market makers demand for providing liquidity. Our findings highlight significant time-series variation in the magnitude of short-term return reversals and suggest that market makers demand higher expected returns prior to earnings announcements because of increased inventory risks that stem from holding net positions through the release of anticipated earnings news. Collectively, our findings suggest that uncertainty regarding anticipated information events elicits predictable increases in the compensation demanded for providing liquidity and that these increases significantly affect the dynamics and information content of market prices.     

Long‐Term Return Reversals: Overreaction or Taxes?

Long‐term reversals in U.S. stock returns are better explained as the rational reactions of investors to locked‐in capital gains than an irrational overreaction to news. Predictors of returns based on the overreaction hypothesis have no power, while those that measure locked‐in capital gains do, completely subsuming past returns measures that are traditionally used to predict long‐term returns. In data from Hong Kong, where investment income is not taxed, reversals are nonexistent, and returns are not forecastable either by traditional measures or by measures based on the capital gains lock‐in hypothesis that successfully predict U.S. returns.     

Bias in estimating the systematic risk of extreme performers: Implications for financial analysis, the leverage effect, and long-run reversals

We show how bias can arise systematically in the beta estimates of extreme performers when long-run return reversals are present and partly, or wholly, due to sign changes in unanticipated factor realizations. Our evidence is consistent with this bias being responsible for the large shifts in the beta estimates of extreme performers, more so than the leverage effect, which has been the predominant explanation in prior literature. Bias in these contemporaneous realized betas, estimated with the same returns that are to be risk adjusted, arises due to the general problem of “overconditioning,” where betas are estimated conditional on information that is not yet known. Several methods for conditioning betas on out-of-sample returns are evaluated and found to be lacking, although some offer improvement under certain circumstances. We also show evidence of this bias in the Fama-French Three-factor loadings of extreme performers. Our findings indicate not only that previous studies of long-run reversals understate contrarian profits but that bias is prevalent in the OLS beta estimates of extreme performers, and this has implications for estimating the cost of capital and measuring long-run performance. We offer recommendations for identifying when this bias is likely present, as well as general methods to correct for it.     

International Stock Return Predictability: What Is the Role of the United States?

We investigate lead‐lag relationships among monthly country stock returns and identify a leading role for the United States: lagged U.S. returns significantly predict returns in numerous non‐U.S. industrialized countries, while lagged non‐U.S. returns display limited predictive ability with respect to U.S. returns. We estimate a news‐diffusion model, and the results indicate that return shocks arising in the United States are only fully reflected in equity prices outside of the United States with a lag, consistent with a gradual information diffusion explanation of the predictive power of lagged U.S. returns.     

Are idiosyncratic volatility and MAX priced in the Canadian market?

The negative relationship between realized idiosyncratic volatility (RIvol) and future returns uncovered by Ang et al. (2006) for the U.S. market has been attributed to return reversals. For the Canadian market where return reversals are considerably less important, we find that RIvol is positively related to future returns, even after controlling for risk loadings, illiquidity and reversals. Unlike the findings of Bali et al. (2001) for the U.S. market, we find that the relationship between extreme positive returns (MAX) and future returns for the Canadian market is positive and that idiosyncratic volatility continues to be consistently positively related to future returns after controlling for MAX. We find evidence that suggests that reversals for stocks with extreme daily returns are confined to (typically small) stocks with low institutional holdings.     

Forecasting cryptocurrency returns with machine learning

This article employs machine learning models to predict returns for 3703 cryptocurrencies for the 2013 – 2021 period. Based on daily data, we build an equal (capital)-weighted portfolio that generates 7.1 % (2.4 %) daily return with a 1.95 (0.27) Sharpe ratio. We obtain an out-of-sample R² of 4.855 %. Our results suggest that cryptocurrencies behave like conventional assets than fiat currencies since variables, including lagged returns, can predict future returns. As assets, cryptocurrencies are not weakly efficient, and production costs do not determine their prices. Returns for small cryptocurrencies are more predictable than larger ones. The predictive power of the 1-day lagged return is stronger than all other features (predictors) combined. The results offer new insights for crypto investors, traders, and financial analysts.     

Causality in quantiles and dynamic stock return–volume relations

This paper investigates the causal relations between stock return and volume based on quantile regressions. We first define Granger non-causality in all quantiles and propose testing non-causality by a sup-Wald test. Such a test is consistent against any deviation from non-causality in distribution, as opposed to the existing tests that check only non-causality in certain moment. This test is readily extended to test non-causality in different quantile ranges. In the empirical studies of three major stock market indices, we find that the causal effects of volume on return are usually heterogeneous across quantiles and those of return on volume are more stable. In particular, the quantile causal effects of volume on return exhibit a spectrum of (symmetric) V-shape relations so that the dispersion of return distribution increases with lagged volume. This is an alternative evidence that volume has a positive effect on return volatility. Moreover, the inclusion of the squares of lagged returns in the model may weaken the quantile causal effects of volume on return but does not affect the causality per se.     

On the Stability of the Cross‐Section of Expected Stock Returns in the Cross‐Section: Understanding the Curious Role of Share Turnover

In this paper, we shed further light on cross‐sectional predictors of stock return performance. Specifically, we explore whether the cross‐section of expected stock returns is robust within stock groups sorted by past monthly return. We find that the book/market and momentum effects are remarkably robust to sorting on past returns. However, share turnover is negatively related to future returns for stocks with abnormally low stock price performance in the recent past, but postively related to returns for well‐performing stocks. This casts doubt on the use of turnover as a liquidity proxy, but is consistent with turnover being a proxy for momentum trading which pushes prices in the direction of past price movements. Our results are robust to both NYSE/AMEX and Nasdaq stocks, and also robust to stratifying the sample by time period.     

Institutional Ownership and Return Reversals Following Short‐Term Return Consistency

Securities with consistently strong positive (negative) returns during the previous two weeks have future returns that are higher (lower) than those that do not. The results hold for various robustness checks, including those involving firm size, share turnover, past return levels, and bid‐ask bounce. The returns to short horizon consistency trading strategies are reliable through time and are both economically and statistically significant. There is also some evidence that longer periods of consistency lead to greater risk‐adjusted profits. Most surprising is that this effect holds only for those firms with high institutional ownership.     

ESTIMATING EXPECTED EXCESS RETURNS USING HISTORICAL AND OPTION‐IMPLIED VOLATILITY

We test the relation between expected and realized excess returns for the S&P 500 index from January 1994 through December 2003 using the proportional reward‐to‐risk measure to estimate expected returns. When risk is measured by historical volatility, we find no relation between expected and realized excess returns. In contrast, when risk is measured by option‐implied volatility, we find a positive and significant relation between expected and realized excess returns in the 1994–1998 subperiod. In the 1999–2003 subperiod, the option‐implied volatility risk measure yields a positive, but statistically insignificant, risk‐return relation. We attribute this performance difference to the fact that, in the 1994–1998 subperiod, return volatility was lower and the average return was much higher than in the 1999–2003 subperiod, thereby increasing the signal‐to‐noise ratio in the latter subperiod.     

TIME-VARYING FACTORS AND CROSS-AUTOCORRELATIONS IN SHORT-HORIZON STOCK RETURNS

In this paper I show that the lead-lag pattern between large and small market value portfolio returns is consistent with differential variations in their expected return components. I find that the larger predictability of returns on the portfolio of small stocks may be due to a higher exposure of these firms to persistent (time-varying) latent factors. Additional evidence suggests that the asymmetric predictability cannot be fully explained by lagged price adjustments to common factor shocks: (i) lagged returns on large stocks do not have a strong causal effect on returns on small stocks; (ii) trading volume is positively related to own- and cross-autocorrelations in weekly portfolio returns; and (iii) significant cross-autocorrelation exists between current returns on large stocks and lagged returns on small stocks when trading volume is high.     

Risk factor and industry effects in the cross-country comovement of momentum returns

This paper examines the sources of cross-country comovement of momentum returns over the 1975–2004 period. Using data on more than 17,000 individual firms across 100 industries from 40 countries, we document the profitability of country-neutral individual firm, industry, and industry-adjusted return momentum. We show that country-neutral momentum returns are significantly correlated across countries, the correlation is time-varying, and that comovement among industries cannot explain the comovement of country-neutral momentum returns. However, we find that standard risk factor models do explain a significant portion of the cross-country comovement of momentum returns, even though they do not explain average momentum returns.     

Maxing out: Stocks as lotteries and the cross-section of expected returns

Motivated by existing evidence of a preference among investors for assets with lottery-like payoffs and that many investors are poorly diversified, we investigate the significance of extreme positive returns in the cross-sectional pricing of stocks. Portfolio-level analyses and firm-level cross-sectional regressions indicate a negative and significant relation between the maximum daily return over the past one month (MAX) and expected stock returns. Average raw and risk-adjusted return differences between stocks in the lowest and highest MAX deciles exceed 1% per month. These results are robust to controls for size, book-to-market, momentum, short-term reversals, liquidity, and skewness. Of particular interest, including MAX reverses the puzzling negative relation between returns and idiosyncratic volatility recently shown in 2 and 3.     

Is asymmetric mean-reverting pattern in stock returns systematic? Evidence from Pacific-basin markets in the short-horizon

This paper applies asymmetric nonlinear smooth transition generalized autoregressive conditional heteroskedasticity (ANST-GARCH) models to the analysis of mean-reversion and time-varying volatility in weekly index returns of the stock markets of nine countries in the Pacific-basin. It finds that the returns exhibit an asymmetric pattern of return reversals, viz., on average, a negative return reverts more quickly, with a greater magnitude, to a positive return than a positive return reverting to a negative one. The asymmetric pattern of return reversals is directly associated with the unequal pricing behavior on the part of investors. Following a negative return shock, investors do not appear to require any additional premium to the leverage effect; instead they actually neutralize the risk in the form of a reduced premium! The reduction in risk premium causes not only the current stock price to rise but also the realized negative return to revert faster with a greater magnitude.