Trading Strategy Leakage

Modern machine learning (ML) technology has shifted information extraction from simple, direct patterns to complex, deep relationships. I study how ML affects the trade-offs of information disclosure via a novel channel: the reverse-engineering of underlying strategies. First, I provide empirical evidence for this channel using a rare natural experiment in the asset management industry, where an unexpected disclosure of trades facilitates statistical learning of complex relationships—but not of simple patterns. Second, I conduct ML-based counterfactual analysis and show that disclosure frequency—by increasing the volume of training data—is central to facilitating reverse-engineering. Finally, I develop a stylized dynamic model featuring strategic interaction between a fund manager and an outsider, and demonstrate that ML raises disclosure costs (outweighing its benefits) by prompting strategic deviation to deter strategy leakage. Overall, my findings highlight that more frequent disclosure requirements can unintentionally reduce market efficiency due to reverse-engineering.