Arbitrage Asymmetry and the Idiosyncratic Volatility Puzzle

Buying is easier than shorting for many equity investors. Combining this arbitrage asymmetry with the arbitrage risk represented by idiosyncratic volatility (IVOL) explains the negative relation between IVOL and average return. The IVOL‐return relation is negative among overpriced stocks but positive among underpriced stocks, with mispricing determined by combining 11 return anomalies. Consistent with arbitrage asymmetry, the negative relation among overpriced stocks is stronger, especially for stocks less easily shorted, so the overall IVOL‐return relation is negative. Further supporting our explanation, high investor sentiment weakens the positive relation among underpriced stocks and, especially, strengthens the negative relation among overpriced stocks.     

Price Momentum and Idiosyncratic Volatility

We find that returns to momentum investing are higher among high idiosyncratic volatility ( IVol) stocks, especially high IVol losers. Higher IVol stocks also experience quicker and larger reversals. The findings are consistent with momentum profits being attributable to underreaction to firm‐specific information and with IVol limiting arbitrage of the momentum effect. We also find a positive time‐series relation between momentum returns and aggregate IVol. Given the long‐term rise in IVol, this result helps explain the persistence of momentum profits since Jegadeesh and Titman's (1993) study.     

Expected Idiosyncratic Volatility Measures and Expected Returns

We find that idiosyncratic volatility forecasts using information available to traders at the time of the forecast are not related to expected returns. The positive relation documented in a number of other papers only exists when forward‐looking information is incorporated into the volatility estimate. That positive relation is driven by the realized idiosyncratic volatility component that cannot be forecasted by investors. Our findings are robust to several different empirical tests, volatility forecasting models and time periods.     

Idiosyncratic Volatility Covariance and Expected Stock Returns

Given that the idiosyncratic volatility (IDVOL) of individual stocks co‐varies, we develop a model to determine how aggregate idiosyncratic volatility (AIV) may affect the volatility of a portfolio with a finite number of stocks. In portfolio and cross‐sectional tests, we find that stocks whose returns are more correlated with AIV innovations have lower returns than those that are less correlated with AIV innovations. These results are robust to controlling for the stock's own IDVOL and market volatility. We conclude that risk‐averse investors pay a premium for stocks that pay well when AIV is high, consistent with our model.     

The Dynamic Relation Between Returns and Idiosyncratic Volatility

We claim that regressing excess returns on one-lagged volatility provides only a limited picture of the dynamic effect of idiosyncratic risk, which tends to be persistent over time. By correcting for the serial correlation in idiosyncratic volatility, we find that idiosyncratic volatility has a significant positive effect. This finding seems robusrt for various firm size portfolios, sample periods, and measures of idiosyncratic risk. Our findings suggest stock markets mis-price idiosyncratic risk. There may be some measurement problems with idiosyncratic risk. There may be some measurement problems with idiosyncratic risk that could be related to nondiversifiable risk.     

High Idiosyncratic Volatility and Low Returns: A Prospect Theory Explanation

The well‐documented negative relationship between idiosyncratic volatility and stock returns is puzzling if investors are risk‐averse. However, under prospect theory, while investors are risk‐averse in the domain of gains, they exhibit risk‐seeking behavior in the domain of losses. Consistent with risk‐seeking investors’ preference for high‐volatility stocks in the loss domain, we find that the negative relationship between idiosyncratic volatility and stock returns is concentrated in stocks with unrealized capital losses, but is nonexistent in stocks with unrealized capital gains. This finding is robust to control for short‐term return reversals and maximum daily return, among other variables.     

Industry‐Specific Human Capital,Idiosyncratic Risk,and the Cross‐Section of Expected Stock Returns

Human capital is one of the largest assets in the economy and in theory may play an important role for asset pricing. Human capital is heterogeneous across investors. One source of heterogeneity is industry affiliation. I show that the cross‐section of expected stock returns is primarily affected by industry‐level rather than aggregate labor income risk. Furthermore, when human capital is excluded from the asset pricing model, the resulting idiosyncratic risk may appear to be priced. I find that the premium for idiosyncratic risk documented by several empirical studies depends on the covariance between stock and human capital returns.     

Does Greater Firm‐Specific Return Variation Mean More or Less Informed Stock Pricing?

Roll [1988] observes low R² statistics for common asset pricing models due to vigorous firm‐specific return variation not associated with public information. He concludes that this implies “either private information or else occasional frenzy unrelated to concrete information”[p. 56]. We show that firms and industries with lower market model R² statistics exhibit higher association between current returns and future earnings, indicating more information about future earnings in current stock returns. This supports Roll's first interpretation: higher firm‐specific return variation as a fraction of total variation signals more information‐laden stock prices and, therefore, more efficient stock markets.     

How Naïve Is the Market’s Use of Firm‐Specific Earnings Information?

Recent studies suggest the apparent delay in the stock-price response to earnings announcements (i.e., post-earnings announcement drift) is caused by investors who underestimate the autocorrelation of seasonally-differenced earnings (persistence). I present results that suggest: (1) a firm's future persistence is predictable on the basis of its past persistence; (2) the immediate stock-price response to earnings is positively related to historical persistence; (3) post-earnings-announcement drift is independent of historical persistence; and (4) consistent with (2) and (3), the difference between a firm's current observed persistence and that implied in stock prices is independent of its historical persistence. These results extend prior research by demonstrating that investors are aware not only that seasonally-differenced earnings are autocorrelated, but that investors recognize firm-specific differences in the magnitude of the autocorrelation.     

What Drives Firm‐Level Stock Returns?

I use a vector autoregressive model (VAR) to decompose an individual firm's stock return into two components: changes in cash-flow expectations (i.e., cash-flow news) and changes in discount rates (i.e., expected-return news). The VAR yields three main results. First, firm-level stock returns are mainly driven by cash-flow news. For a typical stock, the variance of cash-flow news is more than twice that of expected-return news. Second, shocks to expected returns and cash flows are positively correlated for a typical small stock. Third, expected-return-news series are highly correlated across firms, while cash-flow news can largely be diversified away in aggregate portfolios.     

Volume and Volatility: News or Noise?

This paper presents a market microstructure model that is consistent with several empirical regularities. The model embeds separate latent ARCH‐like volatility processes: one representing movements in the underlying fundamental and one representing noise caused by the trading process. This structure allows the regularities to depend either on news or noise. The heteroskedasticity and persistence in the data are due to both ARCH‐like processes. The model has difficulty in simultaneously capturing the size and persistence of trading volume. Several extensions of the basic model, particularly including a constant level of non‐informational trading, improve the model's ability to capture the relevant characteristics of the data.     

Regulatory Arbitrage and Cross‐Border Bank Acquisitions

We study how differences in bank regulation influence cross‐border bank acquisition flows and share price reactions to cross‐border deal announcements. Using a sample of 7,297 domestic and 916 majority cross‐border deals announced between 1995 and 2012, we find evidence of a form of “regulatory arbitrage” whereby acquisition flows involve acquirers from countries with stronger regulations than their targets. Target and aggregate abnormal returns around deal announcements are positive and larger when acquirers come from more restrictive bank regulatory environments. We interpret this evidence as more consistent with a benign form of regulatory arbitrage than a potentially destructive one.     

特质风险与市场收益动态关系的实证研究

本文使用流通市值加权的股票平均波动率作为股票市场未分散特质风险的间接衡量指标,对A股市场特质风险与市场预期收益之间的动态关系进行研究。有别于国内已有研究,本文使用流通市值加权股票平均波动率的自回归残差项作为回归模型的解释变量,避免了解释变量高度持续性特征给回归结果造成的不利影响,发现A股市场未分散的特质风险对预期市场超额收益具有预测能力,两者呈正相关关系,这种预测能力在考虑市场分割、流动性、经济周期以及不同特质风险度量方法后依然存在。     

Investment‐Specific News Shocks and U.S. Business Cycles

We provide robust evidence that news shocks about future investment‐specific technology (IST) constitute a significant force behind U.S. business cycles. Positive IST news shocks induce comovement, that is, raise output, consumption, investment, and hours. These shocks account for 70% of the business cycle variation in output, hours, and consumption, and 60% of the variation in investment, and have played an important role in 9 of the last 10 U.S. recessions. Our findings provide strong support for shifting focus to IST news shocks when investigating the role of news in driving U.S. business cycles.     

How Markets Process Information: News Releases and Volatility

We examine the impact of scheduled macroeconomic news announcements on interest rate and foreign exchange futures markets. We find these announcements are responsible for most of the observed time-of-day and day-of-the-week volatility patterns in these markets. While the bulk of the price adjustment to a major announcement occurs within the first minute, volatility remains substantially higher than normal for roughly fifteen minutes and slightly elevated for several hours. Nonetheless, these subsequent price adjustments are basically independent of the first minute's return. We identify those announcements with the greatest impact on these markets.     

The Influence of Country‐ and Firm‐level Governance on Financial Reporting Quality: Revisiting the Evidence

This paper examines how firm‐level governance and country‐level governance interplay in shaping financial reporting quality. Using IFRS adoption as a source of variation in firms’ reporting discretion, and a large sample of European firms that mandatorily switch to the new set of standards, we find that in countries with low enforcement and weak oversight over financial reporting, only firms with strong board‐level corporate governance mechanisms experience an increase in financial reporting quality, consistent with firm‐ and country‐level governance mechanisms being substitutes. However, in countries with high enforcement and strict oversight over financial reporting, firms with either strong or weak board‐level governance mechanisms experience an increase in financial reporting quality, even if the increase is larger for the former group. Overall, our findings indicate that in the debate about the effects of governance on the quality of financial reporting, it is important to consider both country‐ and firm‐level corporate governance mechanisms.     

Good News,Bad News,Volatility, and Betas

We investigate the conditional covariances of stock returns using bivariate exponential ARCH (EGARCH) models. These models allow market volatility, portfolio-specific volatility, and beta to respond asymmetrically to positive and negative market and portfolio returns, i.e., “leverage” effects. Using monthly data, we find strong evidence of conditional heteroskedasticity in both market and non-market components of returns, and weaker evidence of time-varying conditional betas. Surprisingly while leverage effects appear strong in the market component of volatility, they are absent in conditional betas and weak and/or inconsistent in nonmarket sources of risk.     

Idiosyncratic momentum and the cross‐section of stock returns: Further evidence

In this article, we evaluate the profitability and economic source of the predictive power of the idiosyncratic momentum effect, by using five popular asset pricing models to construct the idiosyncratic momentum. We show that all five idiosyncratic momentum strategies produce similar return predictability and consistently outperform the conventional momentum strategy in the cross‐sectional pricing of equity portfolios and individual stocks. This positive effect of idiosyncratic momentum on returns is consistent with the investment capital asset pricing model (CAPM). Further analysis reveals that the firm‐level idiosyncratic momentum effect cannot extend to the aggregate stock market.