At the height of the COVID-19 related market stress in March 2020, six European countries coordinated to impose market-wide short selling bans. Complementing existing literature in three ways, the analysis uses regulatory data on share trading volumes and short positions to assess the link between the bans and market fragmentation, and extend the analysis to the sectors most affected by the market stress. Based on a difference-in-difference approach and consistent with prior theoretical and empirical work, our estimation confirms that the 2020 short selling bans are associated with a deterioration in liquidity, volatility and volumes, with persistent liquidity effects. However, the bans did neither support nor harm the prices of banned shares over the enactment period. The deterioration of liquidity appears more pronounced for large-cap stocks, highly fragmented stocks, and stocks with listed derivatives – pointing towards stronger effects for shares deemed as liquid. Sectoral effects are observed for the stocks most affected by the market stress, namely the healthcare and the consumer cyclical sectors. Shares that were already shorted before the bans saw an expected stronger increase of their illiquidity, but a volatility decrease under the bans, signalling a more difficult price discovery process.

Environmental, Social and Governance (ESG) principles have grown in popularity lately causing a surge in assets managed under the green mandate. In our study we examine the impact on capital flows and performance of U.S. equity mutual funds that signaled an ESG connection. We use two different greening events to examine investor’s response, the first event is by becoming a PRI signatory and the second event is when a fund changes their name to include an ESG indicative word. Our study reveals that one of the two events analyzed had a significant impact on fund flows, increasing the annual fund flow by 11%. However, we did not find any significant impact on returns.

A large literature establishes a set of predictors that robustly forecast future market returns, raising questions about these predictors' origins. We develop an approach to determine whether a particular predictor represents a proxy for fundamental risk, which is based on an intuitive assumption that risk-based predictors should be linked to new information about economic conditions. We show that an overwhelming majority of predictors forecast returns either on days with macroeconomic announcements or on the remaining days. This suggests that the sources of return predictability differ across predictors with some driven by fundamental risk and others having different origins. Shiller's excess volatility puzzle is confined to non-announcement days, indicating that the ability to forecast the noise component of stock market movements underlies much of the stock market return predictability documented in the literature.