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Deep Learning from Implied Volatility Surfaces

Bryan T. Kelly, Boris Kuznetsov, Semyon Malamud and Teng Andrea Xu
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Bryan T. Kelly: Yale School of Management; AQR Capital Management; NBER
Boris Kuznetsov: Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute
Semyon Malamud: Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute; and CEPR
Teng Andrea Xu: Ecole Polytechnique Fédérale de Lausanne

No 23-60, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: We develop a novel methodology for extracting information from option implied volatility (IV) surfaces for the cross-section of stock returns, using image recognition techniques from machine learning (ML). The predictive information we identify is essentially uncorrelated with most of the existing option-implied characteristics, delivers a higher Sharpe ratio, and has a significant alpha relative to a battery of standard and option-implied factors. We show the virtue of ensemble complexity: Best results are achieved with a large ensemble of ML models, with the out-of-sample performance increasing in the ensemble size, saturating when the number of model parameters significantly exceeds the number of observations. We introduce principal linear features, an analog of principal components for ML and use them to show IV feature complexity: A low-rank rotation of the IV surface cannot explain the model performance. Our results are robust to short-sale constraints and transaction costs.

Pages: 92 pages
Date: 2023-08
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-fmk and nep-rmg
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