Idiosyncratic Risk and the Creative Destruction in Japan

The dramatic rise and fall of the Japanese equity market provides a unique opportunity to examine market-and firm-specific risks over different market conditions. The price behavior of Japanese equities in the 1990s is found to resemble that of U.S. equities during the Great Depression. Both show increasing market volatility and a prolonged large co-movement in equity prices. What is unique about the Japanese case is the surprising fall in firm-level volatility and turnover in Japanese stocks after its market crash in 1990. This large decrease in firm-level volatility may have impeded Japan's capital formation process as it has become more difficult over the past decade for both investors and managers to separate high quality from low quality firms. Using data on firm performance fundamentals and corporate bankruptcies, we show that the fall in firm-level volatility and turnover in Japanese stocks could be attributed to the sharp increase in earnings homogeneity among Japanese firms and the lack of corporate restructuring.

Idiosyncratic Risk and Creative Destruction in Japan

The dramatic rise and fall of the Japanese equity market provides a unique opportunity to examine market-and firm-specific risks over different market conditions. The price behavior of Japanese equities in the 1990s is found to resemble that of U.S. equities during the Great Depression. Both show increasing market volatility and a prolonged large co-movement in equity prices. What is unique about the Japanese case is the surprising fall in firm-level volatility and turnover in Japanese stocks after its market crash in 1990. This large decrease in firm-level volatility may have impeded Japan’s capital formation process as it has become more difficult over the past decade for both investors and managers to separate high quality from low quality firms. Using data on firm performance fundamentals and corporate bankruptcies, we show that the fall in firm-level volatility and turnover could be attributed to the sharp increase in earnings homogeneity among Japanese firms and the lack of corporate restructuring

Understanding Closed-End Fund Puzzles A Stochastic Turnover Perspective

This paper presents a dynamic equilibrium model to study the pricing of most closed-end funds and their tendency to sell at discounts from the value of their assets. Based on the institutional structure of closed-end funds, our model demonstrates the importance of a stochastic turnover factor in determining both the levels and the dynamics of closed-end fund discounts. For example, discounts are likely to increase when (1) the turnover becomes less persistent, (2) a fund’s portfolio composition is less transparent, (3) volatilities of the underlying securities increase, (4) the interest rate rises, and (5) investors become more risk averse. Our model also implies weak return autocorrelations, the excess return volatility, and positive cross correlations between current discounts and future returns, which are consistent with empirical findings. In addition, we document that the level of discounts decreases with both the persistence and the volatility of discounts, while it increases with the volatility of returns from the net asset value. At the same time, cross correlations decrease with return volatilities of underlying assets and the persistence of discounts. We also show that the closed-end fund returns are both more predictable and more positively skewed than that of the returns from the corresponding net asset value. This evidence supports our model implications. The structure of our model sheds light on other issues, such as corporate spin-offs and “excess volatility ” of stock market prices

Extracting Factors with Maximum Explanatory Power

Security returns are heteroscedastic both cross-sectionally and over time, which affects the accuracy of standard factor extraction methods. In order to reduce the impact of such heterogeneity and to preserve the true factor structure, this paper studies the performance of a factor extracting method based on maximizing the explanatory power of the extracted factors. The implementation of the methodology is largely based on the principal components analysis on a correlation structure of asset returns. However, such a simple extension allows us to improve the finite sample performance over other popular approaches when returns are heteroscedastic both across individual assets and over time. Moreover, the out-of-sample study suggests that the extracted factors are not only stable across different sample groups, but also more pervasive in explaining the out-of-sample individual stock returns than other methods. These factors even have better out-of-sample explanatory power than the Fama and French factors. In addition, we shed light on the issue of choosing the correct number of factors

Understanding the Information Content of Short Interests

Abstract Existing studies have debated on the information content of the short interest and its predictive power for future stock returns. We explore a unique institutional feature on the time lag between monthly recording of short interests and their public release to draw inference on the information content of short interests. Using a matching approach, we find that the impact of the negative information content of short interests is more pronounced than previously documented, but only exists in the period before the public release of short interest data. There is no significant predictive power of short interests for future stock returns once the information is released. Moreover, much of the previously documented negative abnormal return of the most heavily shorted portfolio formed on published short interests is largely associated with the liquidity risk. In order to better reveal the potential negative information in short sale activities while considering the short sale constraints, we propose a binding ratio between the short interest and the institutional ownership. We show that both short interest and binding ratio are closely related to forthcoming negative earnings surprises in the month. However, only binding ratio reflect other potential negative information. JEL Classification: G12, G1