A closer look at the relationship between firm-specific return variation and stock returns

This paper examines the idiosyncratic volatility puzzle and whether investor sentiment influences the relation between idiosyncratic volatility and stock returns in the Chinese stock market. The findings indicate the existence of a negative idiosyncratic volatility effect. In addition, the results show that the relation between idiosyncratic volatility and returns significantly depends on investor sentiment. Thus, investor sentiment plays a very important role in reconciling the relation between idiosyncratic volatility and stock returns in the Chinese stock market. This implies that investor sentiment may be one of the major risk factors that should be considered in the Chinese stock market. In terms of predictive ability of investor sentiment, idiosyncratic volatility and market volatility, the findings indicate that idiosyncratic volatility positively predicts future excess market returns in the Chinese stock market.

Is There a Volatility Puzzle in the Hong Kong Stock Market?

Price informativeness is the amount of private information incorporated into stock prices and measured by from regressions of stock returns on systematic risk factors. While is closely related with idiosyncratic volatility, it is not interchangeable because magnitude of systematic volatility simultaneously changes. By controlling for the systematic volatility using the double-sorting portfolio approach, we suggest a potential involvement of price informativeness with the idiosyncratic volatility puzzle. Both cross-sectional evidence of monotonic and inverse relationship between idiosyncratic volatility and and time-series evidence of disappearing alphas of the low-minus-high idiosyncratic volatility portfolios during recessions support an explanation of the idiosyncratic volatility puzzle in association with price informativeness.

This thesis examines predictability and seasonality in the cross-section of stock returns. The first chapter, titled ``Infrequent Rebalancing, Return Autocorrelation, and Seasonality,'' shows that a model of infrequent rebalancing can explain specific predictability patterns in the time series and cross-section of stock returns. First, infrequent rebalancing produces return autocorrelations that are consistent with empirical evidence from intraday returns and new evidence from daily returns. Autocorrelations can switch sign and become positive at the rebalancing horizon. Second, the cross-sectional variance in expected returns is larger when more traders rebalance. This effect generates seasonality in the cross-section of stock returns, which can help explain available empirical evidence. The second chapter, titled ``Seasonalities in Anomalies,'' investigates return seasonalities in a set of well-known anomalies in the cross-section of U.S. stocks returns. A January seasonality goes beyond a size effect and strongly affects most anomalies, which can even switch sign in January. Both tax-loss selling and firm size are important in explaining the turn-of-the-year pattern. Return seasonality exists outside of January, with respect to the month of the quarter. Small stocks earn abnormally high average returns on the last day of each quarter, which significantly affects size, idiosyncratic volatility, and illiquidity portfolios. The results have implications for the interpretation and analysis of many anomalies, such as asset growth and momentum. The third chapter, titled ``The Cross-Section of Intraday and Overnight Returns,'' uses a thirty-year sample of U.S. stock returns to document substantial cross-sectional variation in returns over the trading day and overnight. Market closures have a large impact on returns. Small and illiquid stocks earn high average returns in the last thirty minutes of trading. In contrast, large and liquid stocks perform poorly at this time. I find support for institutional and information asymmetry theories. But these theories do not fully explain the cross-sectional evidence. Portfolios based on other characteristics, such as beta and idiosyncratic volatility, earn their return gradually throughout the trading day–contrary to the market and a benchmark based on random portfolios. These portfolios also tend to incur large negative returns overnight, consistent with mispricing at the open.

Disagreement, Speculation, and the Idiosyncratic Volatility Puzzle

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.

Idiosyncratic volatility generates a lot of interest in literature, as there are conflicting evidences regarding the relationship between stocks’ idiosyncratic volatility and future returns. In this paper, we re-investigate this relationship. We argue that the impact of idiosyncratic volatility on stock return is contextual, i.e., depending on the stocks’ attributes. We focus on stocks’ quality measures. We hypothesize that for companies with high quality measures, the idiosyncratic volatility would have a positive impact on stock return, as it is more likely to be idiosyncratic volatilities on the good side, while on the other hand, for companies with bad qualities, higher idiosyncratic volatility might lead to negative return, as these companies are facing larger default probabilities due to their higher idiosyncratic volatilities. Our empirical results show that within our expectations, for stocks with higher quality measures there is a positive and significant relationship between stocks’ future return and their idiosyncratic volatility, while the story for stocks with lower quality measures is mixed. Our research will contribute to the idiosyncratic volatility literature by providing a thorough and conditional analysis about its impact on stock returns. It will also provide insights for the investors who wish to better understand the impact of idiosyncratic volatility on future stock return and, thus, are able to make better investment decisions.

Good idiosyncratic volatility, bad idiosyncratic volatility, and the cross-section of stock returns

Ambiguity, Macro Factors, and Stock Return Volatility

Stochastic Idiosyncratic Operating Risk and Real Options: Implications for Stock Returns

This paper investigates the empirical relationship between idiosyncratic volatility and the cross-section of stock returns of NEEQ Select. It finds that idiosyncratic volatility is positively related to expected stock returns, with no presence of idiosyncratic volatility anomalies. Idiosyncratic volatility serves as an important asset pricing factor of NEEQ Select, which better represents investors’ psychological expectation of idiosyncratic risk premium. Both expected and unexpected idiosyncratic volatility display a positive and robust relationship to stock returns, but unexpected idiosyncratic risk, which controls for unexpected returns, may exert relatively greater marginal effects. Based on the above conclusions, some suggestions are put forward to actively achieve the diversification management efficiency of NEEQ Select portfolios.

Earnings Announcement Idiosyncratic Volatility and the Cross-Section of Stock Returns

We examine the cross-sectional relation between idiosyncratic volatility and stock returns and propose that the joint effect of the percentage of zero returns, that affects the loading on the systematic risk factors, and the bid-ask spread, that inflates the variance of the returns, biases the estimate of idiosyncratic volatility and the resulting pricing ability of idiosyncratic volatility. We model the microstructure influence on returns and derive a closed-form solution for the bias in the estimated idiosyncratic volatility. Motivated by this theory, our empirical results show that controlling for the liquidity costs on the estimation of idiosyncratic volatility diminishes to insignificance the ability of idiosyncratic volatility estimates to predict future returns. We confirm our findings by examining external shocks to liquidity, due to reductions in the stated quotes after 1997 and the decimalization of quotes in 2001, and find a significant reduction in the pricing ability of idiosyncratic volatility. Finally, minimizing liquidity’s influence on the estimated idiosyncratic volatility, by orthogonalizing the percentage of zero return and spread effects on the estimated idiosyncratic volatility, demonstrates that the resulting idiosyncratic volatility estimate has little pricing ability.

The Information Content of Idiosyncratic Volatility


 The aim of research paper is to examine the effect of stock price informativeness, which is conveyed by stock prices, on firm’s open market repurchases (OMRs) activity. The finding of study is to provide evidence of presence of managerial learning whereby managers could use firm-specific stock return variation to buy back their shares via open market. Evidence is found in the subsample analysis where target sample is separated to two subsamples periods and test separately based on stock market performance. The finding of the 1st subsample (Q12008-Q42013) for OMRs activity indicates that variation of stock return and firm size significantly and positively explains OMRs activity. The finding of 1st subsample is robust in terms of random and fixed effect model. The finding implies that managers observed and learned information from firm-specific variation in stock return during financial crisis and recovery periods when performing open market repurchases.
 Keywords: Open market repurchase, managerial learning hypothesis, stock price informativeness, firm-specific return variation, informed trading.