This paper will partially solve the puzzle of implied volatility spreads from the perspective of short-selling (option-implied borrowing rate). Specifically, we use Chinese SSE 50 ETF options data to examine the relationship between the option-implied volatility spreads and option-implied borrow rate. Using nonparametric regression models, we find that there is a clear negative correlation between the implied volatility spreads and the implied borrowing rate. Furthermore, our results show that there is a significant nonlinearity between these two variables. Finally, it is interesting to note that the option volatility spreads are zero when the option prices include the short selling cost.

This study focuses on the relationship between individual stocks’ systemic tail risk and future returns. Analyzing data from China's A-share market, we document an abnormal negative crosssectional relationship between stocks’ systemic tail risk and returns, which cannot be explained by firm-specific characteristics. We show that the joint effect of investor expectation of stock return persistence and investor sentiment contributes to the systemic tail risk anomaly. Investors tend to underestimate the loss persistence of stocks that have suffered large losses in the most recent period and overprice such stocks, leading to a strong negative relationship between stock systemic tail risk and return. In addition, constraints on short selling exacerbate individual stocks’ systemic tail risk and also explain the systemic tail risk anomaly.

We study the effect of managerial career concerns on informational feedback from stockmarkets. We set up a model in which managers with career concerns can learn information from their own information production or from stock price feedback. A key insight from the model is that if managerial career concerns are high, a less frictional market induces more information production by both managers and speculators. As a result, although price informativeness improves, managers learn less from the stock market, contrasting with the classical view in the literature on stock price feedback. Exploiting a quasi-natural experiment, we document evidence consistent with model predictions.

Using multiple short sale measures, we examine the predictive power of short sales for future stock returns in 38 countries from July 2006 to December 2014. We find that the days-to-cover ratio and the utilization ratio measures have the most robust predictive power for future stock returns in the global capital market. Our results display significant cross-country and cross-firm differences in the predictive power of alternative short sale measures. The predictive power of shorts is stronger in countries with non-prohibitive short sale regulations and for stocks with relatively low liquidity, high shorting fees, and low price efficiency.

We propose a model of trading in the over-the-counter corporate bond market where investors can buy and sell bonds through a dealer and can short bonds by borrowing them in the securities lending market. The model predicts that higher dealer inventory costs are associated with lower short interest for bonds, particularly for high-credit-quality bonds. We construct bond-level proxies for inventory costs and provide empirical evidence in support of the model's prediction. We find that much of the dramatic decline in short interest observed since the Great Financial Crisis (GFC) can be explained by an increase in proxies for inventory costs. We document that the short-sale constraints imposed by higher dealer inventory costs have had a negative impact on price efficiency. Our findings suggest that tighter post-GFC regulation may have had unintended consequences for bond market quality.

This paper uses ensemble learning to build a predictive model to analyze the short selling mechanism of short institutions. We demonstrate the value of combining domain knowledge and machine learning methods in financial market. On the basis of the benchmark model, we use three input data: stock price, financial data and textual data and we employ one of the most powerful machine learning methods, ensemble learning, rather than the commonly used method of logistic regression. In specific methods, we use LSTM-AdaBoost and CART-AdaBoost for model prediction. The results show that the model we train have strong prediction ability for short-selling and the company' s financial text data is more likely to have an impression of whether it would be shorted or not.

Stock market trading restrictions affect stock prices and liquidity directly through constraints on investors’ transactions and indirectly by altering the information environment. We isolate this indirect effect by analyzing how stock market restrictions affect corporate-bond prices. Exploiting the staggered relaxations of trading restrictions in the Chinese stock market as a quasi-natural experiment, we document that the easing of trading restrictions on a firm’s stock decreases the credit spread of its corporate bond. This effect is stronger for firms with less transparency or lower credit ratings. Our evidence suggests that the effect is likely due to improved stock price informativeness.

China?s recent removal of short selling and margin trading bans on selected stocks enables testing of the relative effect of margin trading and short selling. We find the prices of the shortable stocks decrease, on average, relative to peer A-shares and cross-listed H-shares, suggesting that short selling dominates margin trading effects. However, there is negligible short sales activity and contrary to the regulators? intention, and recent empirical evidence, liquidity declines and bid-ask spreads increase in these shortable stocks. Consistent with Ausubel (1990), together these results imply uninformed-investors avoid these stocks to reduce the risk of trading with informed-investors.

China began allowing short selling and margin trading in 90 stocks in March 2010. This event provides an opportunity to test the relative effect of margin trading and short selling. We find the prices of these 90 stocks decrease, on average, relative to peer stocks in China and cross-listed H-shares, suggesting that short selling dominates margin trading effects. Contrary to the regulators? intention, and recent empirical evidence, liquidity declines in the shortable stocks. This may imply avoidance of these stocks by uninformed investors. There is also evidence of higher bid-ask spreads following the regulation change.

This paper evidences a lead-lag relationship between securities which experience high levels of short-selling and those that do not. This is based on evidence that short-selling increases the speed with which information, especially negative information, is absorbed into prices. Previous literature mainly focus on the presence of short-selling and its effect on prices. This paper focuses on the magnitude of short-selling and finds a strong lead-lag relationship between returns of stocks that experience heavy short-selling compared to those that experience slight amounts. The relationship conforms to that of Chordia & Swaminthan’s (2000) speed adjustment hypothesis, in that it facilitates the imputation of common information. The relationship is strongest in small illiquid stocks where short-selling aids in the imputation of common information symmetrically and asymmetrically, and reduces as stocks become larger and more liquid. However in extremely volatile markets this relationship suffers. The relationship is robust to various factors including out of sample tests, accounting for size, and accounting for volume. Of note is the finding that short-selling aids in information imputation over-and-above the efficiency attributed to sophisticated investors. This indicates that market maker and uninformed short-sales add to the lead-lag effect.