We apply machine learning techniques to predict international stock returns using firm characteristics. Market-specific training is important, as neural network models (NNs) achieve stronger results when they are trained in each market separately than in a global model trained with U.S. data. NNs outperform linear models in predicting stock return rankings and forming profitable portfolios. In contrast, regression trees underperform linear models when the number of observations is low. We also show that adding variables constructed from U.S. firm characteristics, which may contain information beyond the characteristics of international stocks, further enhances the return predictability of market-specific NNs.

We find that people revise their beliefs about climate change upward when experiencing warmer than usual temperatures in their area. Using international data, we show that attention to climate change, as proxied by Google search volume, increases when the local temperature is abnormally high. In financial markets, stocks of carbon-intensive firms underperform firms with low carbon emissions in abnormally warm weather. Retail investors (not institutional investors) sell carbon-intensive firms in such weather, and return patterns are unlikely to be driven by changes in fundamentals. Our study sheds light on peoples’ collective beliefs and actions about global warming.

To dampen trading frenzy in the stock market, the Chinese government tripled the stamp tax for stock trading on May 30, 2007. The greatly increased trading cost triggered a migration of the trading frenzy from the stock market to the warrant market, which was not subject to the stamp tax. This migration exacerbated a price bubble in the warrant market. Our analysis of investor account data uncovers not only large inflows of new investors to the warrant market but also greatly intensified trading by existing warrant investors. This episode exemplifies the so-called “whack-a-mole” game in financial regulations.

We investigate the effects of managerial outsourcing on the performance and incentives of mutual funds. Fund families outsource the management of a large fraction of their funds to advisory firms. These funds underperform those run internally by about 52 basis points per year. After instrumenting for a fund's outsourcing status, the estimated underperformance is three times larger. We hypothesize that contractual externalities due to firm boundaries make it difficult to extract performance from an outsourced relationship. Consistent with this view, outsourced funds face higher powered incentives; they are more likely to be closed after poor performance and excessive risk‐taking.

Mutual funds investing in illiquid corporate bonds actively manage Treasury positions to buffer redemption shocks. This liquidity management practice can transmit non-fundamental fund flow shocks onto Treasuries, generating excess return volatility. Consistent with this hypothesis, we find that Treasury excess return volatility is positively associated with bond fund ownership, and this pattern is more pronounced among funds conducting intensive liquidity management. Causal evidence is provided by exploiting the U.S. Securities and Exchange Commission’s 2017 Liquidity Risk Management Rule. Evidence also suggests that the COVID-19 Treasury market turmoil was attributed to intensified liquidity management, an unintended consequence of the 2017 Liquidity Risk Management Rule.

There is causal evidence that mortgage credit expansions increase house prices. Does an expansion of margin lending increase stock prices? Because unconstrained arbitrageurs are more important for pricing stocks than homes, the impact is not obvious. Tests are limited because sizable shocks to margin lending are rare. We examine a major Chinese margin-lending expansion between 2010 and 2015. Institutional holding, regression discontinuity, and event study evidence—exploiting the rollout of margin lending across stocks—shows that arbitrageurs anticipated and bought in advance of a significant causal effect of credit. We develop a model to rationalize our findings. Our estimates suggest that margin debt contributes to stock market fluctuations.

We show that Keeping-Up-with-the-Joneses preferences can explain several puzzling retail investor behaviors, including the excessive trading of small local stocks. Status concerns lead households, especially those living in affluent areas, to demand these stocks to track their neighbors' wealth. This demand varies procyclically with the stock market's value and generates household trading. Using Chinese data on local stock turnover, stock message boards, and brokerage account trading, we test and confirm this hypothesis by exploiting the uneven rise of affluence across Chinese cities between 1998 and 2012.