with Matthew Baron and Jamil Rahman
By analyzing 43 economies over 1870-2016, we find evidence that large credit expansions interacted with rapid price run-ups, have strong forecasting power for negative future returns. The mean cumulative excess return is -22.1% for the housing market over 6 years, and -15.4% for the stock market over 3 years. Growth stocks, stocks with high levels of analyst disagreement, and highly leveraged stocks are particularly vulnerable to the bust, with their respective factor portfolios significantly underperforming compared to their unconditional means. We show that by avoiding stocks with extreme exposures to the credit cycle, investors can partially mitigate the risks induced by a credit-fueled bubble.
Gravity variables capture country level differences in exposure to global shocks. Currencies of countries which are geographically distant and culturally different have larger differences in exposure to global shocks and as such comove less. The explanatory power of gravity for currency comovement is also present in trade flows and financial linkages. Predicted values of trade flows and foreign debt holdings contain around 80% of the explanatory power of gravity variables for currency comovement.
Using the launch of the Shenzhen Hong Kong Stock Connect (SZHKConnect), I examine the effects that an increase in institutional trading has on the liquidity of eligible stocks listed on the Shenzhen Stock Exchange. The difference-in-difference approach finds that an increase in institutional trading improves liquidity measured by quoted spreads and proportional spreads, though it is only significant at the 10% level. Conditioning on a stock being eligible however, the intensity of trading activity through the SZHKConnect is not significantly correlated with stock liquidity. Overall, I find that there is weak evidence that an increase in institutional trading is associated with stock liquidity.
Works in Progress
Institutional Trading During Credit-Fueled Stock Market Booms and Busts
An emerging literature shows that credit booms and rapid asset price growth jointly predict a high probability of a market crash event and significant negative expected returns. Relatively little is known about how investors behave during these predictable market crashes. This paper is the first to study institutional investor trading during these events in a multi-country and multi-event setting spanning 40 economies from 1999-2018. Constructing a rich dataset with over 100 million observations, I ask three questions: first, how do institutional investors change their portfolio allocation to a country experiencing a credit-fueled stock market boom? Second, do different types of institutional investors apply heterogeneous strategies? Third, how do institutional investors change their portfolio within a given country?