A0182
Title: The expected returns on machine-learning strategies
Authors: Vitor Azevedo - RPTU (Germany)
Christopher Hoegner - TUM (Germany)
Mihail Velikov - Pennsylvania State University (United States) [presenting]
Abstract: The expected returns of machine learning-based anomaly trading strategies are assessed, accounting for transaction costs, post-publication decay, and the post-decimalization era of high liquidity. Contrary to claims in prior literature, more sophisticated machine learning strategies are profitable, earning net out-of-sample monthly returns of up to 1.42\%, despite having turnover rates exceeding 50\% and selecting some difficult-to-arbitrage stocks. A trading strategy that employs a long short-term memory model to combine anomaly characteristics yields a six-factor generalized (net) alpha of 1.20\% (t-stat of 3.46). While prevalent cost-mitigation techniques reduce turnover and costs, they do not improve net anomaly performance. Overall, return predictability is documented from deep-learning models that cannot be explained by common risk factors or limits to arbitrage.
