A Machine Learning Approach to Understanding Short Interest and Stock Returns

We investigate how asset prices incorporate information through the lens of shorting activity, focusing on both rational explanations and potential behavioral biases among different types of market participants. We ask whether observed shorting patterns reflect rational updates to new information or instead reveal biases such as extrapolation, overconfidence, or prospect theory–type behavior. We adapt recent machine-learningbased approaches from empirical asset pricing by replacing future returns as the dependent variable with shorting volume. By leveraging a large cross-section of firm and macro-level predictors—encompassing both standard risk factors and variables linked to behavioral tendencies—we aim to uncover which signals dominate short-seller behavior and how strongly those signals propagate into market prices.