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.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

Mutual Fund Trading Pressure: Firm‐Level Stock Price Impact and Timing of SEOs

We use price pressure resulting from purchases by mutual funds with large capital inflows to identify overvalued equity. This is a relatively exogenous overvaluation indicator as it is associated with who is buying—buyers with excess liquidity—rather than what is being purchased. We document substantial stock price impact associated with purchases by high‐inflow mutual funds, and find the probability of a seasoned equity offering (SEO), insider sales, and the probability of a stock‐based acquisition increase significantly in the four quarters following the mutual fund buying pressure. These results provide new evidence that firm managers are able to identify and exploit overvalued equity.     

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。     

Firm‐Level Competition and Exchange Rate Exposure: Evidence from a Global Survey of Firms

We examine the effect of competition on exchange rate exposure using survey data from 55 countries. We find that exposure increases with the intensity of competition. Exposure is higher when firms face price competition in international and domestic product markets and when rivals compete using an unfair financial advantage. Furthermore, competition is a leading determinant of exposure, dominating the usual determinants. Exposure also increases with several determinants not previously empirically examined, such as firm‐level financial constraints. These results hold for small, large, foreign‐involved, and purely domestic firms. Finally, import‐only firms have higher exposure than export‐only firms. Our survey results are likely to capture exposure before firms’ hedging actions.     

CMBS Subordination,Ratings Inflation,and Regulatory‐Capital Arbitrage

Using detailed origination and performance data on a comprehensive sample of commercial mortgage‐backed security (CMBS) deals, along with their underlying loans and a set of similarly rated residential mortgage‐backed securities (RMBS), we apply reduced‐form and structural modeling strategies to test for regulatory‐capital arbitrage and ratings inflation in the CMBS market. We find that the spread between CMBS and corporate‐bond yields fell significantly for ratings AA and AAA after a loosening of capital requirements for highly rated CMBS in 2002, whereas no comparable drop occurred for lower rated bonds (which experienced no similar regulatory change). We also find that CMBS rated below AA upgraded to AA or AAA significantly faster than comparable RMBS (for which there was no change in risk‐based capital requirements). We use a structural model to investigate these results in more detail and find that little else changed in the CMBS market over this period except for the rating agencies' persistent reductions in subordination levels between 1997 and late 2007. Indeed, had the 2005 vintage CMBS used the subordination levels from 2000, there would have been no losses to the senior bonds in most CMBS structures.     

Do Accruals Drive Firm‐Level Stock Returns? A Variance Decomposition Analysis

This paper extends the variance decomposition framework of Campbell [1991], Campbell and Ammer [1993], and Vuolteenaho [2002] to address the relative value relevance of accrual news, cash flow news, and expected-return news in driving firm-level equity returns. The extension is based on the Feltham-Ohlson [1995, 1996] clean surplus relations. Using three models, this study shows that all three factors, accruals, cash flows, and expected future discount rates are value relevant. Moreover, accrual news is found to significantly dominate expected-return news in driving firm-level stock returns. Operating income news is also found to significantly dominate both expected-return news and free cash flow news in driving firm-level stock returns. Furthermore, after splitting net income into cash flow and accrual earnings components in the Vuolteenaho model, accrual earnings news and cash flow earnings news are found to equally drive firm-level stock returns and to dominate expected-return news. Further disaggregation of the data yields some evidence that accrual earnings news is a more important factor than cash flow earnings news in driving current stock returns. Overall, the three models indicate that changes in expected future accruals are a primary driver, if not the primary driver, of current stock returns.     

Idiosyncratic volatility and cross‐sectional stock returns in Southeast Asian stock markets

We examine the role of idiosyncratic risk in five ASEAN markets of Malaysia, Singapore, Thailand, Indonesia, and the Philippines. Our research was motivated by the findings of Ang et al. (2006, 2009) of a ‘puzzling’ negative relation between idiosyncratic volatility and 1‐month ahead stock returns in developed markets and the suggestion of the ubiquity of these results in other markets. In contrast, we find no evidence of an idiosyncratic volatility puzzle in these Asian stock markets; instead, we document a positive relationship between idiosyncratic volatility and returns in Malaysia, Singapore, Thailand, and Indonesia and no relationship in the Philippines. The idiosyncratic volatility trading strategy could result in significant trading profits in Malaysia, Singapore, Thailand, and to some extent in Indonesia. Our study underscores the fact that generalizing empirical results obtained in developed stock markets to new and emerging markets could potentially be misleading.     

Time‐Varying Asset Volatility and the Credit Spread Puzzle

Most extant structural credit risk models underestimate credit spreads—a shortcoming known as the credit spread puzzle. We consider a model with priced stochastic asset risk that is able to fit medium‐ to long‐term spreads. The model, augmented by jumps to help explain short‐term spreads, is estimated on firm‐level data and identifies significant asset variance risk premia. An important feature of the model is the significant time variation in risk premia induced by the uncertainty about asset risk. Various extensions are considered, among them optimal leverage and endogenous default.     

The Age of the Dragon: The Effect of Imports from China on Firm‐Level Prices

We analyze the impact of increased import penetration from China on the dynamics of firm‐level output prices in Italy. Accounting for potential endogeneity biases we find a significant and negative causal relationship: a 0.1 percentage point higher Chinese import penetration restrains price growth by 0.17 percentage points per year. This relationship reflects a procompetitive effect induced by cheaper imports, and, thanks to the firm‐level dimension of our data, we show that it is driven by low‐productivity firms within less skill‐intensive sectors. Finally, we show that Chinese import competition also had a dampening effect on Italian overall inflation.     

The Time‐Varying Volatility of Earnings and Aggregate Consumption Growth

Time‐varying specifications for the conditional variance of earnings of U.S. households are estimated with micro data over the period 1968–92. The cross‐sectional mean of the estimated time‐varying uncertainty of individual households has a significant impact on aggregate consumption growth. As such, aggregate precautionary savings may be more important than what is suggested by the results of estimating standard regression equations for aggregate consumption growth that incorporate only lagged income growth and the real interest rate. The estimation of a buffer stock consumption model with time‐varying earnings uncertainty suggests that the precautionary savings motive is cyclical and has become less important in the 1980s.     

Good‐Specific Habit Formation and the Cross‐Section of Expected Returns

I study asset prices in a general equilibrium framework in which agents form habits over individual varieties of goods rather than over an aggregate consumption bundle. Goods are produced by monopolistically competitive firms whose elasticities of demand depend on consumers' habit formation. Firms that produce goods with a high habit level relative to consumption have low demand elasticities, set high prices for their product, have low expected returns on their stock, and have low asset pricing betas and stock return volatilities. I find supportive evidence for these predictions in the data.