Journal of Financial Markets最新文献

We investigate the impact of margin trading and short selling (MTSS) on information asymmetry using data from a unique Chinese pilot program that permits MTSS for a specific list of stocks. We establish a theoretical framework indicating that MTSS enhances the pricing system’s informativeness, leading to reduced information asymmetry. Motivated by this theoretical framework, we design a quasi-experiment to analyze data from 2013 to 2016. We find that indeed MTSS weakens information asymmetry. Furthermore, the reduction of information asymmetry can be attributed to both margin trading and short selling.

We employ a regression discontinuity design to study how a market division experiment affects stock liquidity and firm valuation in an illiquid market. We document that an increase in firm visibility can substantially improve a firm's liquidity (measured by trading immediacy) and increase valuation by 28.4% for thinly traded assets. We also find some evidence that an improved information environment may contribute to enhanced liquidity and valuation.

Search friction is a key driver of changes in corporate bond yield spreads over time. In the cross-section, the liquidity risk stemming from search friction is significantly priced, and is strongly correlated with the misallocation of bond positions among different traders. I propose a novel measure of bond-specific misallocation, which is the negative covariance between traders’ private valuations and their inventory positions for each bond. I find that bonds with higher levels of misallocation are associated with lower absolute levels of liquidity risk from search friction. I develop a search-and-matching model to explain this correlation.

We investigate the returns to specialized knowledge in asset management by comparing the performance of medical specialists versus generalists in managing healthcare sector mutual funds. Specialist alpha is 5% higher on an annualized basis. This result is robust to controlling for other observable characteristics and using different performance measures, subperiods, and variable definitions. The positive effect of a medical education on fund performance and specialists’ ability to time industry-specific events suggest that knowledge-based skills, rather than social connections, are key to specialist outperformance. Our paper provides new evidence on fund manager characteristics that provide a competitive advantage in generating alpha.

Satellites can “see” oil inventory in oil tanks, but they are sensitive to cloud cover. Cloud cover introduces a new uncertainty related to information quality. We measure such information uncertainty by assessing cloud cover over floating roof oil tanks. Using a cloud cover index, we demonstrate that higher information uncertainty leads to lower future returns (mean effect) and a stronger momentum anomaly (interaction effect). These two effects can be explained by investor overconfidence and arbitrage costs, respectively. An investor with a mean–variance preference obtains sizable gains in terms of certainty equivalent return, which accounts for the mean effect.

We explore the relationship between options trading and corporate social responsibility (CSR). We find that options trading can enhance CSR by (i) fostering shareholder activism, as indicated by increased long-term and socially responsible institutional ownership and CSR proposals and (ii) intensifying product market competition, which spurs green innovation and product-related CSR initiatives. The positive effects are more pronounced in well-governed firms but diminished in firms with managerial entrenchment. Moreover, CSR improvements driven by options trading positively influence firm value, with notable enhancements in environmental performance. Our results underscore that options markets play a crucial role in promoting responsible corporate practices.

The volatility of stock investor returns depends not only on the volatility of the stocks they hold but also on their time-varying capital exposure to these holdings. Using individual stocks, portfolios of stocks, and indexes across U.S. and international stock markets, we provide comprehensive evidence that the volatility of investor returns is consistently higher than the corresponding volatility of stock returns across nearly all specifications. The relative magnitude of the volatility differential ranges from 10% to 75%, increasing with investment horizon. This discrepancy is driven primarily by investors’ propensity to "flee volatility," withdrawing equity capital following periods of high volatility.

We examine whether the strategic response to short selling by other informed investors decelerates the incorporation of positive information. We find a sizeable reduction of positive information impounding before earnings announcements for stocks more exposed to short selling. Consistent with strategic behavior, we find that investors with positive views slow down their trades when short sellers are also present. Furthermore, they break up their buy trades across multiple brokers, suggesting they wish to prevent a price impact. Thus, the strategic reaction to short selling appears to have implications for information impounding before public information releases.

I study the effect of algorithmic trading on market efficiency, taking into account past market and limit order flows alike. I find that an exogenous increase in algorithmic trading around the introduction of the NYSE Hybrid Market leads to a significant decrease in the predictive power of surprises in market order imbalance and limit order book imbalances, especially at the outer levels of the limit order book. However, the predictive power of past returns remains largely unchanged. This suggests that algorithmic trading improves market efficiency by facilitating the incorporation of information embedded in both market and limit order flows.

We investigate the application of machine learning algorithms for predicting stock price crash risks by employing a set of firm-specific characteristics of the Chinese stock market. The results suggest that machine learning techniques are superior in capturing the nuances of stock price crash risk, particularly through profitability and value versus growth features. These techniques perform well within state-owned enterprises and during periods of low economic policy uncertainty, and predictive insights primarily originate from intra-industry dynamics. In addition, we offer corporate finance- and financial market-based interpretations of machine learning's predictability, as well as a comprehensive understanding of its key determinants.