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.     

Sensitivity to investor sentiment and stock performance of open market share repurchases

This paper finds that stocks of repurchasers with high sensitivity to investor sentiment are more likely to be mispriced. Thus, such repurchases are followed by superior post-buyback stock performance. This abnormal return associated with sensitivity to sentiment cannot be explained by other undervaluation factors: book-to-market or prior return effects. My results are robust with factor model analysis and controls for contamination effects. I conclude that this sentiment-driven undervaluation may result from the difficulty to value and/or limits to arbitrage rather than investor overreaction.     

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%.     

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.     

Mispricing firm-level productivity

This paper provides a mispricing-based explanation for the negative relation between firm-level productivity and stock returns. Investors appear to underprice unproductive firms and overprice productive firms. We find evidence consistent with the speculative overpricing of productive firms driven by investor sentiment and short sale constraints. Investors erroneously extrapolate past productivity growth and its associated operating performance and stock returns, despite their subsequent reversals. Such mispricing is perpetuated because of limits to arbitrage and is partially corrected around earnings announcements when investors are surprised by unexpected earnings news. Decomposition analysis indicates that extrapolative mispricing and limits to arbitrage explain most of the return predictability of firm-level productivity.     

Costly arbitrage and idiosyncratic risk: Evidence from short sellers

Previous studies have shown that high short interest stocks have low subsequent returns. We test whether the persistence of this effect is due to costs limiting arbitrage. The arbitrage cost that we focus on is idiosyncratic risk which, regardless of the arbitrageur’s level of diversification, deters arbitrage activity. Consistent with costly arbitrage, we find that among high short interest stocks a one standard deviation increase in idiosyncratic risk predicts a more than 1% decline in monthly returns. Moreover, idiosyncratic risk does not predict returns across low short interest stocks, and short interest does not predict low returns across low idiosyncratic risk stocks. Our results are robust to commonly used proxies for both transaction costs and short sale constraints.     

WHAT'S IN A NAME? A CAUTIONARY TALE OF PROFITABILITY ANOMALIES AND LIMITS TO ARBITRAGE

The recent literature investigating profitability anomalies defines profitability in various ways (i.e., gross, operating, and cash based). We show that limits to arbitrage are associated with returns of gross and cash-based operating profitability anomalies, suggesting mispricing. In contrast, returns from the operating profitability strategy have no relation with barriers to arbitrage and exhibit no evidence of mispricing. Additionally, we show that the differential effects of limited arbitrage-related mispricing of gross and cash-based operating profitability anomalies are attributable to their respective correlations with selling, general, and administrative (SG&A) expense and accruals anomalies. We find that SG&A return predictability, like that of accruals, is related to limits to arbitrage. These findings suggest that investors and researchers should proceed with caution when searching for return predictability by redefining profitability measures.     

Investor Sentiment and the Cross-Section of Stock Returns

We study how investor sentiment affects the cross‐section of stock returns. We predict that a wave of investor sentiment has larger effects on securities whose valuations are highly subjective and difficult to arbitrage. Consistent with this prediction, we find that when beginning‐of‐period proxies for sentiment are low, subsequent returns are relatively high for small stocks, young stocks, high volatility stocks, unprofitable stocks, non‐dividend‐paying stocks, extreme growth stocks, and distressed stocks. When sentiment is high, on the other hand, these categories of stock earn relatively low subsequent returns.     

Firm efficiency and stock returns: Australian evidence

We employ a stochastic frontier approach to estimate firm efficiency - the efficiency with which a firm converts its inputs into output. We find a negative relation between firm efficiency and the cross-section of stock returns (‘firm efficiency effect’) in the Australian stock market. The firm efficiency effect is robust after controlling for other firm characteristics and is more pronounced in stocks with high limits-to-arbitrage. However, we find no evidence that firm efficiency is a priced factor in the cross-section. Our findings suggest that investors misprice firm efficiency and arbitrage costs perpetuate its return predictability.     

Oil price shocks,investor sentiment,and asset pricing anomalies in the oil and gas industry

This paper documents that stocks are not efficiently priced in the oil and gas industry. We find significant cross-sectional effects on stock returns from various firm characteristics in the oil and gas industry. Specifically, 13 out of 15 prominent capital market anomalies are robust in the oil and gas industry. Investor sentiment has significantly positive impact on 4 anomalies: composite equity issues, investment to assets, net stock issues, and value effect. Among the three oil shocks, we find that aggregate demand shocks have significantly impact on 6 anomalies: composite equity issues, financial distress, net stock issues, O-SCORE, return on assets, and idiosyncratic volatility. Our results are consistent with the view that high arbitrage costs and risks have significant deterring effects on arbitrage in the oil and gas industry. Our findings also have practical investment and policy implications for investors, firm managers, and policy makers alike.     

Investor attention,information acquisition,and value premium: A mispricing perspective

This paper investigates the impact of investor attention on the dynamics of the value premium. We find superior return differences to value-growth strategy conditioned as low degree of investor attention. In contrast, return differences to the value-growth strategy conditioned as high investor attention are indifferent from zero. We show that return differences to low degree of investor attention across value and growth firms are attributed to mispricing explanation using common risk factors, mispricing factors, sentiment analysis, multivariate analysis, and market expectation errors approach. The findings suggest that investor attention contributes to generating superior return differences to standard value-growth strategy. Our finding concludes that long-short investment strategy in value stocks and growth stocks conditioned as low investor attention generate superior value premium.     

Do idiosyncratic jumps matter?

We show that idiosyncratic jumps are a key determinant of mean stock returns from both an ex post and ex ante perspective. Ex post, the entire annual average return of a typical stock accrues on the four days on which its price jumps. Ex ante, idiosyncratic jump risk earns a premium: a value-weighted weekly long-short portfolio that buys (sells) stocks with high (low) predicted jump probabilities earns annualized mean returns of 9.4% and four-factor alphas of 8.1%. This strategy’s returns are larger when there are greater limits to arbitrage. These results are consistent with investor aversion to idiosyncratic jump risk.     

Do the diversification choices of individual investors influence stock returns?

This paper shows that the diversification choices of individual investors influence stock returns. A zero-cost portfolio that takes a long (short) position in stocks with the least (most) diversified individual investor clientele generates an annual, risk-adjusted return of 5–9%. This spread reflects the combined effects of sentiment-induced mispricing, narrow risk framing, and asymmetric information, where the sentiment effect is the strongest. Furthermore, the influence on returns is stronger among smaller, low institutionally owned, and hard-to-arbitrage stocks. These results are robust to concerns about relatively short sample size, improper factor model specification, slow information diffusion, and high transactions costs.     

Investor sentiment and return predictability of disagreement

This study examines the influence of investor sentiment on the relationship between disagreement among investors and future stock market returns. We find that the relationship between disagreement and future stock market returns time-varies with the degree of investor sentiment. Higher disagreement among investors’ opinions predicts significantly lower future stock market returns during high-sentiment periods, but it has no significant effect on future stock market returns during low-sentiment periods. Our findings imply that investor sentiment is related to several causes of short-sale impediments suggested in the previous literature on investor sentiment, and that the stock return predictability of disagreement is driven by investor sentiment. We demonstrate that investor sentiment has a significant impact on the stock market return predictability of disagreement through in-sample and out-of-sample analyses. In addition, the profitability of our suggested trading strategy exploiting disagreement and investor sentiment level confirms the economic significance of incorporating investor sentiment into the relationship between disagreement among investors and future stock market returns.     

The high returns to low volatility stocks are actually a premium on high quality firms

Recent empirical research shows that low volatility stocks outperform high volatility stocks around the world. This study documents that the volatility effect is associated with the quality of the firm using a large sample of international stocks. First, adding a quality factor to the Fama–French model contributes to the explanation of the volatility effect. Furthermore, the negative volatility–return relation is shown to be stronger and significant only among high quality firms which are profitable and have stable cash flows. Second, a fundamental investment strategy that goes long high quality firms and short low quality firms performs like a volatility strategy and cannot be explained by common asset pricing models. However, a low–high volatility factor adds to the explanation of the return difference between high and low quality stocks as volatility and quality strategies have a common component.     

Investing in Chapter 11 stocks: Trading,value, and performance

We address questions about Chapter 11 stocks regarding their trading environment, fundamental value, and performance. First, there exists active trading for Chapter 11 stocks throughout the bankruptcy process. Second, equity value after filing is positively related to asset value, asset volatility, risk-free rate, and expected duration and is negatively related to liabilities. Furthermore, the return correlation between bankrupt stocks and their matching samples exhibits non-linearity similar to out-of-money call options. Third, investing in Chapter 11 stocks incurs large losses. Consistent with heterogeneous beliefs and limits to arbitrage, stocks with higher levels of information uncertainty and more binding short-sale constraints experience more negative returns.     

Retail Investor Sentiment and Return Comovements

Using a database of more than 1.85 million retail investor transactions over 1991–1996, we show that these trades are systematically correlated—that is, individuals buy (or sell) stocks in concert. Moreover, consistent with noise trader models, we find that systematic retail trading explains return comovements for stocks with high retail concentration (i.e., small‐cap, value, lower institutional ownership, and lower‐priced stocks), especially if these stocks are also costly to arbitrage. Macroeconomic news and analyst earnings forecast revisions do not explain these results. Collectively, our findings support a role for investor sentiment in the formation of returns.     

Filter rules based on price and volume in individual security overreaction

I present evidence of predictability in a sample constructedto minimize concerns about time-varying risk premia and market-microstructureeffects. I use filter rules on lagged return and lagged volumeinformation to uncover weekly over-reaction profits on large-capitalizationNYSE and AMEX securities. I find that decreasing-volume stocksexperience greater reversals. Increasing-volume stocks exhibitweaker reversals and positive autocorrelation. A real-time simulationof the filter strategies suggests that an investor who pursuesthe filter strategy with relatively low transaction costs willstrongly outperform an investor who follows a buy-and-hold strategy.     

Institutional ownership and return predictability across economically unrelated stocks

We document strong weekly lead-lag return predictability across stocks from different industries with no customer-supplier linkages (economically unrelated stocks). Between 1980 and 2010, the industry-neutral long-short hedge portfolio earns an average of over 19 basis points per week. This predictability is related to common institutional ownership and is distinct from previously documented lead-lag effects. Common institutional ownership is a complementary rather than a substitute explanation for return predictability. Information linkages are enough to induce return predictability among stocks in the same industry, but economically unrelated stocks exhibit return predictability only when they have common institutional owners. Our findings suggest that institutional portfolio reallocations can induce return predictability among otherwise unrelated stocks.     

Investor sentiment and the dispersion of stock returns: Evidence based on the social network of investors

This paper extracts an investor sentiment indicator for the 30 DJIA stocks based on the textual classification of 289,024 online tweets posted on the so-called StockTwits, and examines its contemporaneous and predictability effects on the dispersion of stock returns using the quantile regression technique. We find that both contemporaneous and predictability effects of sentiment are heterogeneous throughout the return distribution. Specifically, sentiment is positively contemporaneously associated with stock returns at higher quantiles. However, it is a strong negative predictor of future returns at lower quantiles. Overall, our findings are broadly consistent with most behavioural theories and show that sentiment mainly affects the valuation of assets in extreme market conditions.