Option Return Predictability with Machine Learning and Big Data

• 成果类型: Article
• 署名作者: Bali, Turan G.; Beckmeyer, Heiner; Morke, Mathis; Weigert, Florian
• 署名单位: Georgetown University; University of Munster; University of St Gallen; University of Neuchatel
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhad017
• 发表日期: 2023
• 页码: 3548
• 关键词: RISK-NEUTRAL SKEWNESS; cross-section; EMPIRICAL PERFORMANCE; time-series; SHORT SALES; volatility; uncertainty; INFORMATION; illiquidity; combination

摘要

Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample performance of option and stock characteristics in predicting future option returns. The nonlinear machine learning models generate statistically and economically sizable profits in the long-short portfolios of equity options even after accounting for transaction costs. Although option-based characteristics are the most important standalone predictors, stock-based measures offer substantial incremental predictive power when considered alongside option-based characteristics. Finally, we provide compelling evidence that option return predictability is driven by informational frictions and option mispricing. Authors have furnished an , which is available on the Oxford University Press Web site next to the link to the final published paper online.