| Title | Journal | Date | Author | Abstract | Link |
|---|
| Informed Trading Intensity | Journal of Finance | 20240401 | Bogousslavsky, Vincent; Fos, Vyacheslav; Muravyev, Dmitriy | We train a machine learning method on a class of informed trades to develop a new measure of informed trading, informed trading intensity (ITI). ITI increases before earnings, mergers and acquisitions, and news announcements, and has implications for return reversal and asset pricing. ITI is effective because it captures nonlinearities and interactions between informed trading, volume, and volatility. This data-driven approach can shed light on the economics of informed trading, including impatient informed trading, commonality in informed trading, and models of informed trading. Overall, learning from informed trading data can generate an effective informed trading measure. | View Infographic |
| The Virtue of Complexity in Return Prediction | Journal of Finance | 20240201 | Kelly, Bryan; Malamud, Semyon; Zhou, Kangying | Much of the extant literature predicts market returns with "simple" models that use only a few parameters. Contrary to conventional wisdom, we theoretically prove that simple models severely understate return predictability compared to "complex" models in which the number of parameters exceeds the number of observations. We empirically document the virtue of complexity in U.S. equity market return prediction. Our findings establish the rationale for modeling expected returns through machine learning. | View Infographic |
| (Re-)imag(in)ing Price Trends | Journal of Finance | 20231201 | Jiang, Jingwen; Kelly, Bryan; Xiu, Dacheng | We reconsider trend-based predictability by employing flexible learning methods to identify price patterns that are highly predictive of returns, as opposed to testing predefined patterns like momentum or reversal. Our predictor data are stock-level price charts, allowing us to extract the most predictive price patterns using machine learning image analysis techniques. These patterns differ significantly from commonly analyzed trend signals, yield more accurate return predictions, enable more profitable investment strategies, and demonstrate robustness across specifications. Remarkably, they exhibit context independence, as short-term patterns perform well on longer time scales, and patterns learned from U.S. stocks prove effective in international markets. | View Infographic |
