The return impact of realized and expected idiosyncratic volatility

We show that the negative relation between realized idiosyncratic volatility, measured over the prior month, and returns is robust in non-January months. Controlling for realized idiosyncratic volatility, we show that the relation between returns and expected idiosyncratic volatility is positive and robust. Realized and expected idiosyncratic volatility are separate and important effects describing the cross-section of returns. We find the negative return on a zero-investment portfolio that is long high realized idiosyncratic volatility stocks and short low realized idiosyncratic volatility stocks is dependent on aggregate investor sentiment. In cross-sectional tests, we find the negative relation is weaker for stocks with a large analyst following and stronger for stocks with high dispersion of analyst forecasts. The positive relation between expected idiosyncratic volatility and returns is not due to mispricing.     

Are extreme returns priced in the stock market? European evidence

This paper revisits some recently found evidence in the literature on the cross-section of stock returns for a carefully constructed dataset of euro area stocks. First, we confirm recent results for US data and find evidence of a negative cross-sectional relation between extreme positive returns and average returns after controlling for characteristics such as momentum, book-to-market, size, liquidity and short term return reversal. We argue that this is the case because these stocks have lottery-like characteristics, which is attractive to certain investors. Also, these stocks tend to be very volatile so that arbitrageurs are discouraged from correcting potential mispricing. As a consequence, these stocks are often overpriced and hence face lower expected returns. Second, when we control for extreme returns, the recently found negative relationship between idiosyncratic risk and future returns is less robust. In our models, after adding maximum returns, the relationship is insignificant and sometimes even positive. We also find that idiosyncratic skewness and coskewness play an important role for asset pricing, as predicted by several theoretical models.     

The MAX effect: European evidence

The maximum daily return over the previous month (MAX) of Bali et al. (2011) is a strong and significant predictor of future stock returns in non-U.S. equity markets. Once it is controlled for MAX in the cross-section of average returns, the puzzling negative idiosyncratic volatility-return relation disappears. Consistent with the assumption that MAX is the true effect, for which idiosyncratic volatility is just a proxy, we find that MAX can be traced back to firm fundamentals in the manner of idiosyncratic volatility. The negative MAX-return relation is stronger among firms with high cash flow volatility and weaker among firms with high profitability.     

On the characteristics and performance of long-short, market-neutral and bear mutual funds

We evaluate the return performance of long-short, market-neutral and bear mutual funds using multi-factor models and a conditional CAPM that allows for time-varying risk. Differences in the bearish posture of these mutual funds result in different performance characteristics. Returns to long-short mutual funds vary with the market, returns to market-neutral mutual funds are uncorrelated with the market and returns to bear mutual funds are negatively correlated. Using the conditional CAPM we document significant changes in the market-risk exposure of the most bearish of these funds during different economic climates. We then assess the flow-performance relationship for up to 60 months following up and down markets and find that investors direct flows towards market-neutral and bearish funds for several months after down markets. Market-neutral funds provide a down market hedge, but bear funds do not generate the returns that investors hope for.     

“Hot Hands” in bond funds

We investigate persistence in the relative performance of 3549 bond mutual funds from 1990 to 2003. We show that bond funds that display strong (weak) performance over a past period continue to do so in future periods. The out-of-sample difference in risk-adjusted return between the top and bottom decile of funds ranked on past alpha exceeds 3.5 percent per year. We demonstrate that a strategy based on past fund returns earns an economically and statistically significant abnormal return, suggesting that bond fund investors can exploit the observed persistence. Our results are robust to a wide range of model specifications and bootstrapped test statistics.     

Share restrictions and asset pricing: Evidence from the hedge fund industry

This paper presents evidence on the relation between hedge fund returns and restrictions imposed by funds that limit the liquidity of fund investors. The excess returns of funds with lockup restrictions are approximately 4–7% per year higher than those of nonlockup funds. The average alpha of all funds is negative or insignificant after controlling for lockups and other share restrictions. Also, a negative relation is found between share restrictions and the liquidity of the fund's portfolio. This suggests that share restrictions allow funds to efficiently manage illiquid assets, and these benefits are captured by investors as a share illiquidity premium.     

Reference-dependent preferences and the risk–return trade-off

This paper studies the cross-sectional risk–return trade-off in the stock market. A fundamental principle in finance is the positive relation between risk and expected return. However, recent empirical evidence suggests the opposite. Using several intuitive risk measures, we show that the negative risk–return relation is much more pronounced among firms in which investors face prior losses, but the risk–return relation is positive among firms in which investors face prior gains. We consider a number of possible explanations for this new empirical finding and conclude that reference-dependent preference is the most promising explanation.     

Dispersion in analysts’ earnings forecasts and credit rating

This paper shows that the puzzling negative cross-sectional relation between dispersion in analysts’ earnings forecasts and future stock returns may be explained by financial distress, as proxied by credit rating downgrades. Focusing on a sample of firms rated by Standard & Poor's (S&P), we show that the profitability of dispersion-based trading strategies concentrates in a small number of the worst-rated firms and is significant only during periods of deteriorating credit conditions. In such periods, the negative dispersion–return relation emerges as low-rated firms experience substantial price drop along with considerable increase in forecast dispersion. Moreover, even for this small universe of worst-rated firms, the dispersion–return relation is non-existent when either the dispersion measure or return is adjusted by credit risk. The results are robust to previously proposed explanations for the dispersion effect such as short-sale constraints and leverage.     

Left-tail momentum: Underreaction to bad news,costly arbitrage and equity returns

This paper documents a significantly negative cross-sectional relation between left-tail risk and future returns on individual stocks trading in the US and international countries. We provide a behavioral explanation to this anomaly based on the idea that investors underestimate the persistence in left-tail risk and overprice stocks with large recent losses. Thus, low returns in the left-tail of the distribution persist into the future causing left-tail return momentum. We find that the left-tail risk anomaly is stronger for stocks that are more likely to be held by retail investors, that receive less investor attention, and that are costlier to arbitrage.     

Nonlinear mean reversion in stock prices

This paper provides new evidence on the time-series predictability of stock market returns by introducing a test of nonlinear mean reversion. The performance of extreme daily returns is evaluated in terms of their power to predict short- and long-horizon returns on various stock market indices and size portfolios. The paper shows that the speed of mean reversion is significantly higher during the large falls of the market. The parameter estimates indicate a negative and significant relation between the monthly portfolio returns and the extreme daily returns observed over the past one to eight months. Specifically, in a quarter in which the minimum daily return is −2% the expected excess return is 37 basis points higher than in a month in which the minimum return is only −1%. This result holds for the value-weighted and equal-weighted stock market indices and for each of the size decile portfolios. The findings are also robust to different sample periods, different indices, and investment horizons.     

Do extreme returns matter in emerging markets? Evidence from the Chinese stock market

Recent evidence in the U.S. and Europe indicates that stocks with high maximum daily returns in the previous month, perform poorly in the current month. We investigate the presence of a similar effect in the emerging Chinese stock markets with portfolio-level analysis and firm-level Fama–MacBeth cross-sectional regressions. We find evidence of a MAX effect similar to the U.S. and European markets. However, contrary to U.S. and European evidence, the MAX effect in China does not weaken much less reverse the anomalous idiosyncratic volatility (IV) effect. Both the MAX and IV effects appear to independently coexist in the Chinese stock markets. Interpreted together with the strong evidence of risk-seeking behaviour among Chinese investors, our results partially support the suggestion that the negative MAX effect is driven by investor preference for stocks with lottery-like features.     

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.     

Financial crises and the asymmetric relation between returns on banks,risk factors,and other industry portfolio returns

We show that the relations between the returns on the banking industry, risk factors, and other industries often are asymmetric. Lagged banking industry returns seem to improve predictability but the positive impact of a 1‐month lag of the return on the banking portfolio is much higher in the lower part of the return distribution. However, after the Dodd‐Frank Act in 2010, the cross‐autocorrelation with banks is changed and becomes negative in the upper part of the distribution. Returns on banks also seem to lead returns on five risk factors. This relation, however, is not robust across the distribution.     

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.     

Endogenous leverage and expected stock returns

This note clarifies conditions under which endogenous choice of debt induces a negative relation between leverage or default risk and expected stock returns. In the context of the model of George and Hwang [2009. Journal of Financial Economics 96, 56–79], we correct the contention that variation in bankruptcy costs across firms is sufficient. Variation in asset risk parameters can lead to the desired relation, but may not when also controlling for variation in book-to-market ratios. A simple parameterization of cross-sectional heterogeneity in risk and profitability implies a negative association of expected return with leverage and distress risk and a positive association with book-to-market.     

When is a MAX not the MAX? How news resolves information uncertainty

A well-known asset pricing anomaly, the “MAX” effect, measured by the maximum daily return in the past month, depicts stocks’ lottery-like features and investor gambling behaviour. Using the comprehensive stock-level Dow Jones (DJNS) news database between 1979 and 2016, we consider in a empirical setting how the presence of news reports affects these lottery-type stocks. We find an augmented negative relationship between MAX stocks without news and expected returns, whereby MAX with news coverage generates return momentum. The differing future return relationships between MAX stocks with and without news appears to be best explained by information uncertainty mitigation upon news arrival. Overall, our findings suggest that news plays a role in resolving information uncertainty in the stock market.     

Higher risk,lower returns: What hedge fund investors really earn

The returns of hedge fund investors depend not only on the returns of the funds they hold but also on the timing and magnitude of their capital flows in and out of these funds. We use dollar-weighted returns (a form of Internal Rate of Return (IRR)) to assess the properties of actual investor returns on hedge funds and compare them to buy-and-hold fund returns. Our main finding is that annualized dollar-weighted returns are on the magnitude of 3% to 7% lower than corresponding buy-and-hold fund returns. Using factor models of risk and the estimated dollar-weighted performance gap, we find that the real alpha of hedge fund investors is close to zero. In absolute terms, dollar-weighted returns are reliably lower than the return on the Standard & Poor's (S&P) 500 index, and are only marginally higher than the risk-free rate as of the end of 2008. The combined impression from these results is that the return experience of hedge fund investors is much worse than previously thought.     

The Other January Effect: Evidence against market efficiency?

The Other January Effect (OJE), which suggests positive (negative) equity market returns in January predict positive (negative) returns in the following 11 months of the year, underperforms a simple buy-and-hold strategy before and after risk-adjustment. Even the best modified OJE strategy, which benefits from several ex-post adjustments, does not generate statistically or economically significant excess returns. When the OJE is tested with a method that is consistent with investor experience it is clear the OJE is no more profitable than an 11-month strategy that uses November or December as the conditioning month.     

The Conditional Beta and the Cross-Section of Expected Returns

We examine the cross-sectional relation between conditional betas and expected stock returns for a sample period of July 1963 to December 2004. Our portfolio-level analyses and the firm-level cross-sectional regressions indicate a positive, significant relation between conditional betas and the cross-section of expected returns. The average return difference between high- and low-beta portfolios ranges between 0.89% and 1.01% per month, depending on the time-varying specification of conditional beta. After controlling for size, book-to-market, liquidity, and momentum, the positive relation between market beta and expected returns remains economically and statistically significant.     

Dynamic Volume-Return Relation of Individual Stocks

We examine the dynamic relation between return and volume ofindividual stocks. Using a simple model in which investors tradeto share risk or speculate on private information, we show thatreturns generated by risk-sharing trades tend to reverse themselves,while returns generated by speculative trades tend to continuethemselves. We test this theoretical prediction by analyzingthe relation between daily volume and first-order return autocorrelationfor individual stocks listed on the NYSE and AMEX. We find thatthe cross-sectional variation in the relation between volumeand return autocorrelation is related to the extent of informedtrading in a manner consistent with the theoretical prediction.