Cross-sectional Return Dispersion Research Articles

Herding in the bad times: The 2008 and COVID-19 crises

The objective of this paper is to analyze the imitation behavior of investors in especially convulsed periods, such as the 2008 financial crisis and the recent global pandemic, both of which could affect investors' emotions and behavior, although both have different characteristics and might have different implications. The cross-sectional dispersion of returns is used to measure the level of herding in the markets of Spain and Portugal, using a survivorship-bias-free dataset of daily stock returns during the period January 2000–May 2021, in turn divided into several sub-periods classified as pre-2008 crisis, 2008 crisis, post-2008 crisis, Covid-19 and post Covid-19. Additionally, the existence is studied of differences between days of positive and negative returns, or between days of high volatility compared to the rest, and whether the cross-sectional dispersion of returns in one market is affected by the cross-sectional dispersion of returns in the other market. The results indicate that herding appears with greater intensity in periods prior to the crisis, disappearing during the financial crisis and reappearing, although with less intensity, after it, while it is not generally detected in Covid-19 times. However, herding behavior can be observed in the market during the pandemic on high volatility days.

Herding by corporates in the US and the Eurozone through different market conditions

In this study, we test the herding towards a market consensus in the main financial industries of the United States and the Eurozone equity markets. We find that herding is more likely to be present in high quantiles that reflects turbulent market conditions. This herding appears to be more pronounced during financial crisis periods and in cases of asymmetric conditions of volatility, credit deterioration, and illiquid funding. Furthermore, we provide evidence that the cross-sectional dispersion of returns throughout the domestic equity market can be partly explained by the corresponding dispersions of the financial industries. In our analysis we cover the last two main global financial crises and identify new evidence of “spurious” and “intentional” herding by corporates. Further, our results are robust when considering short-selling bans.

COVID-19 Infection of Australian Companies

This paper analyzes the impact of the coronavirus pandemic on Australian companies’ share prices. We use daily new infections as a risk factor and proxy for the severity of the pandemic. Based on the loading to this risk factor and abnormal returns during the crisis we categorize firms into (i) severely infected, (ii) infected and (iii) mildly or not infected. We find large differences across firms and sectors highlighting that the virus does not affect all firms and not in the same way contrasting evidence of financial contagion and excess co-movement from past crises. The increased cross-sectional dispersion of returns in the pandemic suggests a sophisticated response of investors resulting in significant diversification benefits.

Can China’s cross-sectional dispersion of stock returns influence the herding behaviour of traders in other local markets and China’s trading partners?

This paper examines the influence of China’s cross-sectional dispersion of returns on local markets, as well as its major trading partners. With the cross-sectional average deviation method, we have reported some significant and insignificant results, as well as for tranquil and turbulent phases of the Chinese stock market. It seems that the cross-sectional dispersion of returns in China has influenced markets in countries in the greater China region and some other Asian countries but not markets in Europe or the United States. Although China’s cross-sectional dispersion of returns plays a role in influencing the country’s trading partners, which are categorised as having high, medium or low trade volumes, it does not cause any of these groups to herd around its market. Thus we conclude that although China is the world's second-largest economy after that of the United States, China's role in stock trading is still unmatched by the United States.

Cross-sectional return dispersion and volatility prediction

We use intraday and daily data to examine the impact of cross-sectional return dispersion on volatility forecasting in the Chinese equity market. We adopt the GARCH, GJR-GARCH, and HAR models and, by augmenting them with return dispersion measures, provide empirical evidence that the return dispersion exhibits substantial information in describing the volatility dynamics by generating significantly lower forecasting errors at market and industry levels. Furthermore, the information content of the return dispersion tends to offer economic gain to a mean-variance utility investor. The findings are robust with respect to alternative volatility proxies, subsample analysis, and alternative market-wide stock indices.

Cross-sectional return dispersion and currency momentum

Abstract I assess the relation between cross-sectional return dispersion in foreign exchange (FX) markets and currency momentum. I find that cross-sectional dispersion is priced in the cross-section of currency momentum returns and that an unexpected increase in cross-sectional dispersion is associated with positive (negative) excess returns to winner (loser) currencies. This mechanism can be related to monetary policy conditions. The empirical findings are robust to the inclusion of traditional currency risk factors, liquidity and market volatility variables, and transaction costs. Finally, the explanatory ability of cross-sectional dispersion extends to broader cross-sections of currency portfolios and to individual currencies.

Cross-sectional alpha dispersion and performance evaluation

Our paper explores the link between cross-sectional fund return dispersion and performance evaluation. The foundation of our model is the simple intuition that in periods of high return dispersion, which is associated with high levels of idiosyncratic risk for zero-alpha funds, unskilled managers can more easily disguise themselves as skilled. Rational investors should be more skeptical and apply larger discounts to reported performance in high dispersion environments. Our empirical results are consistent with this prediction. Using fund flow data, we show that a one standard deviation increase in cross-sectional return dispersion is associated with an 11% to 17% decline in flow-performance sensitivity. The effect is stronger for recent data and among outperforming funds.

Net Buybacks and the Seven Dwarfs

During the last two decades, the relationship between economic growth and equity returns has been weak across global stock markets. Economic growth has not been a good proxy for dividend-per-share growth. In the majority of the stock markets, issuance has exceeded buybacks and has resulted in a dilution effect. In several markets, however, buybacks have exceeded issuance, resulting in an accretion effect. The main finding of our study is that, whether investors use a dividend model or a total payout model to decompose equity returns, net buybacks explain more than 80% of the cross-sectional dispersion of stock market returns.

Cross-Sectional Alpha Dispersion and Performance Evaluation

Our paper explores the link between cross-sectional fund return dispersion and performance evaluation. The foundation of our model is the simple intuition that in periods of high return dispersion, which is associated with high levels of idiosyncratic risk for zero-alpha funds, it is easier for unskilled managers to disguise themselves as skilled. Rational investors should be more skeptical and apply larger discounts to reported performance in high dispersion environments. Our empirical results are consistent with this prediction. Using fund flow data, we show that a one-standard deviation increase in cross-sectional return dispersion is associated with an 11% to 17% decline in flow-performance sensitivity. The effect is stronger for recent data and among outperforming funds.

Risk-managed 52-week high industry momentum, momentum crashes and hedging macroeconomic risk

This is the first study to investigate the profitability of Barroso and Santa-Clara’s [J. Financial Econ., 2015, 116, 111–120] risk-managing approach for George and Hwang’s [J. Finance, 2004, 59, 2145–2176] 52-week high momentum strategy in an industrial portfolio setting. The findings indicate that risk-managing adds value as the Sharpe ratio increases, and the downside risk decreases notably. Even after controlling for the spread of the traditional 52-week high industry momentum strategy in association with standard risk factors, the risk-managed version generates economically and statistically significant pay-offs. Notably, the risk-managed strategy is partially explained by changes in cross-sectional return dispersion, whereas the traditional strategy does not appear to be exposed to such economic risks.

Cross-sectional dispersion and expected returns

This study investigates whether the cross-sectional dispersion of stock returns, which reflects the aggregate level of idiosyncratic risk in the market, represents a priced state variable. We find that stocks with high sensitivities to dispersion offer low expected returns. Furthermore, a zero-cost spread portfolio that is long (short) in stocks with low (high) dispersion betas produces a statistically and economically significant return. Dispersion is associated with a significantly negative risk premium in the cross section (–1.32% per annum) which is distinct from premia commanded by alternative systematic factors. These results are robust to stock characteristics and market conditions.

Macroeconomic and Political Uncertainty and Cross Sectional Return Dispersion Around the World

Cross sectional return dispersion seems a simple, good, real time gauge of uncertainty. Internationally, cross-sectional return dispersion correlates strongly with all sorts of measures of macroeconomic and political uncertainty like, (global) recessions, international political crises, country risk, and uncertainty related to government policies. While we find that return dispersion and implied volatility are correlated, surprisingly only return dispersion relates to the cross section of returns. Return dispersion seems to capture political uncertainty better. Implied volatility seems linked stronger to economic uncertainty.

Herd behavior of the overall market: Evidence based on the cross-sectional comovement of returns

Employing the cross-sectional dispersion of returns as a measure detecting the herd behavior of the overall market, previous studies have reported mixed evidence of herding during periods of large price movements in the US stock markets. We reexamine the herd behavior of the overall market by employing a new measure of herding based on the cross-sectional comovement of returns in excess of its expected value under an assumed asset pricing model. Employing the proposed measure based on either the capital asset pricing model or the Fama-French three factor model during the period 7/1963–12/2014, we find strong evidence of herding during periods of mid to large negative price movements, but weak or no evidence of herding during periods of positive price movements. The recent US subprime crisis period is an exception because during the period no significant evidence of herding is found. We also find evidence that properly accounting for the effects of an assumed asset pricing model when calculating the herding measure is important and that the herding behavior of the overall market is likely to be driven by the contemporaneous market-wide information.

Building Efficient Portfolios Sensitive to Market Volatility

This paper builds relatively efficient diversified portfolios that are sensitive to cross-sectional return dispersion (RD). A two-factor model based on Black's zero-beta CAPM is developed to measure sensitivity to RD. Based on U.S. stock returns in the sample period 1965 to 2015, aggregate portfolios are constructed by combining the CRSP market index with zero-investment portfolios that reflect different levels of RD risk. In out-of-sample tests based on one-month-ahead returns, we find that a substantially higher average return of almost 1.50 percent per month can be achieved relative to the average return of the benchmark CRSP market index of 0.89 percent per month, holding constant total risk. Further results show that zero-investment portfolios with different RD risk levels can be developed that outperform zero-investment portfolios formed on size, value, and momentum investment strategies (e.g., 2.67 percent per month). These and other results suggest that RD is useful in building relatively efficient portfolios.

Cross-Sectional Return Dispersion and Currency Momentum

I assess the relation between cross-sectional return dispersion in foreign exchange (FX) markets and currency momentum. I find that cross-sectional dispersion is priced in the cross-section of currency momentum returns and that an unexpected increase in cross-sectional dispersion is associated with positive (negative) excess returns to winner (loser) currencies. This mechanism can be related to monetary policy conditions. The empirical findings are robust to the inclusion of traditional currency risk factors, liquidity and market volatility variables, and transaction costs. Finally, the explanatory ability of cross-sectional dispersion extends to broader cross-sections of currency portfolios and to individual currencies.

Momentum, Reversals, and other Puzzles in Fama-MacBeth Cross-Sectional Regressions

The existence of reversals and momentum in equity returns has challenged proponents of efficient markets for over 30 years. Although explanations for momentum profits based on cross-sectional mean return dispersion have been proposed, evidence of time-series autocorrelation from Fama-MacBeth cross-sectional regressions persists without any good risk/return explanation. In this paper I show that common implementations of the Fama-MacBeth procedure will yield upward biased estimates of time-series autocorrelation coefficients. Even in absence of autocorrelation, the bias is strictly positive, leading to apparent momentum when there is, in fact, none. This biased implementation of the Fama-MacBeth procedure has found its way into a great many other studies and may, similarly, lead to apparent effects when there are none. I outline conditions under which this bias occurs and prove the existence of bias under these conditions. I also provide a Monte Carlo simulation showing the magnitude of the bias, I demonstrate the impact of this bias with reference to published results in the literature, and I introduce a new test for misspecification of an asset pricing model. Additionally, I suggest and explore simple fixes for this bias. Some variation of a firm fixed-effects model is appropriate to correct for this bias in applications using the Fama-MacBeth method.

An empirical investigation of herding in the U.S. stock market

This paper proposes a new empirical testing method for detecting herding in stock markets. The traditional regression approach is extended to a vector autoregressive framework, in which the predictive power of squared index returns for the cross-sectional dispersion of equity returns is tested using a Granger causality test. Macroeconomic news announcements and the aggregate number of firm-level news items are treated as conditioning variables, while the average sentiment of firm-level news is treated as jointly determined. The testing algorithm allows the change points in the causal relationships between the cross-sectional dispersion of returns and squared index returns to be determined endogenously rather than being chosen arbitrarily a priori. Evidence of herding is detected in the constituent stocks of the Dow Jones Industrial Average at the onset of the subprime mortgage crisis, during the European debt and the U.S. debt-ceiling crises and the Chinese stock market crash of 2015. These results contrast with those obtained from the traditional methods where little evidence of herding is found in the US stock market.

Time-Varying Liquidity and Momentum Profits

A basic intuition is that arbitrage is easier when markets are most liquid. Surprisingly, we find that momentum profits are markedly larger in liquid market states. This finding is not explained by variation in liquidity risk, time-varying exposure to risk factors, or changes in macroeconomic condition, cross-sectional return dispersion, and investor sentiment. The predictive performance of aggregate market illiquidity for momentum profits uniformly exceeds that of market return and market volatility states. While momentum strategies have been unconditionally unprofitable in the United States, in Japan, and in the Eurozone countries in the last decade, they are substantial following liquid market states.

Cross-Sectional Dispersion and Expected Returns

This study investigates whether the cross-sectional dispersion of stock returns, which reflects the aggregate level of idiosyncratic risk in the market, represents a priced state variable. We find that stocks with high sensitivities to dispersion offer low expected returns. Furthermore, a zero-cost spread portfolio that is long (short) in stocks with low (high) dispersion betas produces a statistically and economically significant return, after accounting for its exposure to other systematic risk factors. Dispersion is associated with a significantly negative risk premium in the cross-section (-1.32% per annum) which is distinct from premia commanded by a set of alternative systematic factors. These results are robust to a wide set of stock characteristics, market conditions, and industry groupings.

Theoretical decompositions of the cross-sectional dispersion of stock returns

The cross-sectional standard deviation of stock returns, or dispersion as it is frequently called, has attracted an increasing amount of attention in recent years. In the fund management industry, ...