Impact of idiosyncratic volatility on stock returns: A cross-sectional study

This paper proposes a new approach to estimate the idiosyncratic volatility premium. In contrast to the popular two-pass regression method, this approach relies on a novel GMM-type estimation procedure that uses only a single cross-section of return observations to obtain consistent estimates. Also, it enables a comparison of idiosyncratic volatility premia estimated using stock returns with different holding periods. The approach is empirically illustrated by applying it to daily, weekly, monthly, quarterly, and annual US stock return data over the course of 2000–2011. The results suggest that the idiosyncratic volatility premium tends to be positive on daily return data, but negative on monthly, quarterly, and annual data. They also indicate the presence of a January effect.     

Incomplete information,idiosyncratic volatility and stock returns

When investors have incomplete information, expected returns, as measured by an econometrician, deviate from those predicted by standard asset pricing models by including a term that is the product of the stock’s idiosyncratic volatility and the investors’ aggregated forecast errors. If investors are biased this term generates a relation between idiosyncratic volatility and expected stocks returns. Relying on forecast revisions from IBES, we construct a new variable that proxies for this term and show that it explains a significant part of the empirical relation between idiosyncratic volatility and stock returns.     

The dynamics of short-term mutual fund flows and returns: A time-series and cross-sectional investigation

This study analyzes the dynamics of daily mutual fund flows. A Vector Auto Regression (VAR) of flows and returns shows that the behavior of fund investors is more consistent with contrarian rather than momentum characteristics. Past fund flows have a positive impact on future fund returns, with the long-term information effect dominating the transient price-pressure effect. Seasonality in daily flows, such as day-of-week and day-of-month patterns are present, and daily flows are generally mean-reverting. Probit regressions indicate that fund investment objective, marketing policy and level of active management explain cross-sectional variation in the behavioral patterns displayed in daily flows. Our results are robust to the different methods of calculating daily flows based on whether or not the day-end TNA figures include the current-day’s flow. Throughout the analysis, we contrast the dynamics of daily flows with established results for monthly fund flows and find important differences between the two.     

Expected stock returns,common idiosyncratic volatility and average idiosyncratic correlation

Motivated by Herskovic et al. (2016), we examine the role of the average idiosyncratic correlation (ICOR) in two types of markets: an emerging market and a developed market. Examining daily stock data from the Chinese stock market for the period 1995 to 2020 and from the US for the period 1926 to 2019, we adopt high-dimensional principal component analysis (PCA) and thresholding methods to re-estimate ICOR. We find that ICOR plays an important role in explaining the expected stock returns, as the common idiosyncratic volatility (CIV) does in Herskovic et al. (2016). ICOR has been neglected in the literature due to large estimation error in the idiosyncratic covariance matrix and our analysis provides evidence that ICOR is nonnegligible in both markets when we control for several common market factors. We show that the average idiosyncratic covariance, which is the numerator of ICOR, exhibits the same pattern as CIV. Furthermore, our regression analyses of expected stock returns in response to ICOR change in both markets show that, in contrast to the negative result for CIV, the stocks’ high risk exposure to ICOR change comes with a higher risk premium, perhaps because of the synchronized but disproportionate changes in the monthly idiosyncratic covariance and idiosyncratic volatility.     

Measurement effects and the variance of returns after stock splits and stock dividends

This article examines the relation between two factors affectingstock returns, the bid-ask spread and price discreteness, andthe increase in return variance after ex-dates of stock splitsand stock dividends. Controlling for these effects, the varianceof daily returns still increases significantly. The varianceof weekly returns also increases significantly, and the varianceof returns for a control sample of nonsplitting firms showsno significant increase. Variance ratio tests show that bid-askerrors are small for these stocks and therefore cannot explainthe large increase in variance. Spreads and price discretenessdo not explain increased variance after stock distributions.     

Cross section of option returns and idiosyncratic stock volatility

This paper presents a robust new finding that delta-hedged equity option return decreases monotonically with an increase in the idiosyncratic volatility of the underlying stock. This result cannot be explained by standard risk factors. It is distinct from existing anomalies in the stock market or volatility-related option mispricing. It is consistent with market imperfections and constrained financial intermediaries. Dealers charge a higher premium for options on high idiosyncratic volatility stocks due to their higher arbitrage costs. Controlling for limits to arbitrage proxies reduces the strength of the negative relation between delta-hedged option return and idiosyncratic volatility by about 40%.     

The relationship between asset growth and the cross-section of stock returns

There is a large body of literature examining the association between stock characteristics and the cross-section of stock returns in international markets. Recently, Cooper et al. (2008) reported a strong association between total asset growth and stock returns in the US. In this paper, we show that an asset-growth effect also exists in the Australian equity market. Of particular interest, it is present amongst the largest Australian stocks. Over the 1983-2007 period, an equally-weighted portfolio of low-growth Big stocks outperforms a portfolio of high-growth Big stocks by an average 1% per month, equating to nearly 13% per annum. At an individual stock level of analysis, the asset-growth effect remains even after controlling for other variables whose association with the cross-section of returns is well known. Finally, we explicitly test whether asset growth is a priced risk factor using the common two-stage cross-sectional regression methodology. We find no evidence to support a risk-based explanation, thereby lending credence to Cooper et al.’s (2008) suggestion that the asset-growth effect is attributable to mispricing.     

Liquidity costs,idiosyncratic volatility and expected stock returns

This paper considers liquidity as an explanation for the positive association between expected idiosyncratic volatility (IV) and expected stock returns. Liquidity costs may affect the stock returns, through bid-ask bounce and other microstructure-induced noise, which will affect the estimation of IV. We use a novel method (developed by Weaver, 1991) to eliminate microstructure influences from stock closing price-based returns and then estimate IV. We show that there is a premium for IV in value-weighted portfolios, but this premium is less strong after correcting returns for microstructure bias. We further show that this premium is driven by liquidity in the prior month after correcting returns for microstructure noise. The pricing results from equally-weighted portfolios indicate that IV does not predict returns either before or after controlling for liquidity costs. These findings are robust after controlling for common risk factors as well as analysing double-sorted portfolios based on IV and liquidity.     

Bank stock returns and economic growth

Previous research has established (i) that a country’s financial sector influence future economic growth and (ii) that stock market index returns affect future economic growth. We extend and tie together these two strands of the growth literature by analyzing the relationship between banking industry stock returns and future economic growth. Using dynamic panel techniques to analyze panel data from 18 developed and 18 emerging markets, we find a positive and significant relationship between bank stock returns and future GDP growth that is independent of the previously documented relationship between market index returns and economic growth. We also find that much of the informational content of bank stock returns is captured by country-specific and institutional characteristics, such as bank-accounting-disclosure standards, banking crises, enforcement of insider trading law and government ownership of banks.     

The effects of oil price shocks on U.S. stock order flow imbalances and stock returns

This paper investigates for the first time the effects of oil demand shocks and oil supply shocks on stock order flow imbalances leading to changes in stock returns. Through the estimation of a structural VAR model, positive oil demand shocks are able to explain almost 36% of the observed variation in the daily average stock order flow imbalances measured by the buy/sell trades ratio; which consequently lead to a negative rather than positive stock returns reaction. In contrast, oil supply shocks exhibit a negative and marginally significant effect on stock order flow imbalances. Our aggregate analysis suggests that positive shocks on stock order flow imbalances are negatively related to stock returns. These effects are stronger for oil-related sectors when compared with the rest of the equities sectors.     

A tale of idiosyncratic volatility and illiquidity shocks: Their correlation and effects on stock returns

This paper documents that the idiosyncratic volatility shock is not a stand-alone anomaly compared to the illiquidity shock. Our empirical evidence shows that the illiquidity shock Granger-causes idiosyncratic volatility shock and is statistically and economically significant in predicting stock returns. More importantly, it subsumes most of the predictive power of idiosyncratic volatility shock. In addition, we find that it is the illiquidity shock, not idiosyncratic shock, that is significantly affected by earnings surprise and is more consistently related to other information-signaling variables such as changes in analyst dispersion, number of sell-side analyst coverage, and institutional ownership.     

Extreme downside risk and expected stock returns

We propose a measure for extreme downside risk (EDR) to investigate whether bearing such a risk is rewarded by higher expected stock returns. By constructing an EDR proxy with the left tail index in the classical generalized extreme value distribution, we document a significantly positive EDR premium in cross-section of stock returns even after controlling for market, size, value, momentum, and liquidity effects. The EDR premium is more prominent among glamor stocks and when high market returns are expected. High-EDR stocks are generally characterized by high idiosyncratic risk, large downside beta, lower coskewness and cokurtosis, and high bankruptcy risk. The EDR premium persists after these characteristics are controlled for. Although Value at Risk (VaR) plays a significant role in explaining the EDR premium, it cannot completely subsume the EDR effect.     

Revisiting the empirical linkages between stock returns and trading volume

This paper investigates whether the empirical linkages between stock returns and trading volume differ over the fluctuations of stock markets, i.e., whether the return–volume relation is asymmetric in bull and bear stock markets. Using monthly data for the S&P 500 price index and trading volume from 1973M2 to 2008M10, strong evidence of asymmetry in contemporaneous correlation is found. As for a dynamic (causal) relation, it is found that the stock return is capable of predicting trading volume in both bear and bull markets. However, the evidence for trade volume predicting returns is weaker.     

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.     

Time-varying rare disaster risk and stock returns

This study provides empirical support for theoretical models that allow for time-varying rare disaster risk. Using a database of 447 international political crises during the period 1918-2006, we create a crisis index that shows substantial variation over time. Changes in this crisis index, our proxy for changes in perceived disaster probability, have a large impact on both the mean and volatility of world stock market returns. Crisis risk is positively correlated with the earnings-price ratio and the dividend yield. Cross-sectional tests also show that crisis risk is priced: Industries that are more crisis risk sensitive yield higher returns.     

Daily short interest,idiosyncratic risk,and stock returns

This paper examines the relation between short selling and returns and the impact of arbitrage costs on short sellers’ behavior. Using daily UK short selling data, we find that stocks with low short interest levels experience significant positive returns on both an equal- and value-weighted basis. Economic theory predicts that short sellers avoid establishing positions in stocks with high idiosyncratic risk. Our results indicate a negative relation between short interest and returns among high idiosyncratic risk stocks and that short selling activity is mostly concentrated in low idiosyncratic risk stocks where it is less costly to arbitrage fundamental risk.     

Seasonality in the cross-section of stock returns

This paper presents a new pattern in the cross-section of expected stock returns. Stocks tend to have relatively high (or low) returns every year in the same calendar month. We recognize the annual cross-sectional autocorrelation pattern documented in Jegadeesh [1990. Evidence of predictable behavior of security returns. Journal of Finance 45, 881–898] at lags of 12, 24, and 36 months as part of a general pattern that lasts up to 20 annual lags, superimposed on the general momentum/reversal patterns. This pattern explains an economically and statistically significant magnitude of the cross-sectional variation in average stock returns. Volume and volatility exhibit similar seasonal patterns but they do not explain the seasonality in returns. The pattern is independent of size, industry, earnings announcements, dividends, and fiscal year. The results are consistent with the existence of a persistent seasonal effect in stock returns.     

Share issuance and cross-sectional returns: International evidence

Share issuance predicts cross-sectional returns in a non-U.S. sample of stocks from 41 different countries. Issuance predictability has greater statistical significance than either size or momentum, and is similar to book-to-market. As in the U.S., the international issuance effect is robust across both small and large firms. Unlike the U.S., the effect is driven more by low returns after share creation rather than positive returns following share repurchases. Issuance return predictability is stronger in countries with greater issuance activity, greater stock market development, and stronger investor protection. The results suggest that the share issuance effect is related to the ease with which firms can issue and repurchase their shares.     

A careful re-examination of seasonality in international stock markets: Comment on sentiment and stock returns

In questioning Kamstra, Kramer, and Levi’s (2003) finding of an economically and statistically significant seasonal affective disorder (SAD) effect, Kelly and Meschke (2010) make errors of commission and omission. They misrepresent their empirical results, claiming that the SAD effect arises due to a “mechanically induced” effect that is non-existent, labeling the SAD effect a “turn-of-year” effect (when in fact their models and ours separately control for turn-of-year effects), and ignoring coefficient-estimate patterns that strongly support the SAD effect. Our analysis of their data shows, even using their low-power statistical tests, there is significant international evidence supporting the SAD effect. Employing modern, panel/time-series statistical methods strengthens the case dramatically. Additionally, Kelly and Meschke represent the finance, psychology, and medical literatures in misleading ways, describing some findings as opposite to those reported by the researchers themselves, and choosing selective quotes that could easily lead readers to a distorted understanding of these findings.     

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.