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Building an Options Portfolio with Deep Learning

Abstract

Recent applications of powerful machine learning models within the field of portfolio op- timization have shown promising results. This paper explores the application of similar methods to create an actively managed options portfolio, rather than a traditional portfolio containing stocks and ETFs. Although financial options are popular assets amongst both retail and institutional investors, they are almost exclusively traded for one of two purposes: hedging or intra-asset speculation. Little, if any, literature exists on the topic of inter-asset options strategies. Using percent change in implied volatility data as a proxy for single-day straddle returns, various machine learning models are trained by directly optimizing the Sharpe Ratio–a risk-adjusted return metric. The models output daily volatility positions in 315 underlying assets thereby creating the Options Portfolio. Results show significant potential in such a strategy, with the Attention Transformer model yielding a before-cost average annual Sharpe Ratio of 10.76 compared to the GRU model of 6.41, the LSTM model of 5.07, and the equal-weighted baseline of 0.53.

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