Deep learning in option pricing: an application in China A-shares

Abstract

The objective of this study was to investigate whether the estimated options price from the deep learning, method fits with the actual price based on high accuracy and determine the feasibility of applying deep learning in practical options pricing. We predicted the price of China A-shares with the long short-term memory method, which is the recurrent neural network in deep learning, using the past 6 years call and put options prices of China A-shares. The results from the long short-term memory method illustrate that the method could predict the prices of China A-shares with high precision and we could reveal that the deep learning methods are feasible in options pricing. Furthermore, we conclude that the analysis of China A-shares pricing and long short-term methods has provided a deeper insight into promoting the deep learning methods in the actual options market. In addition, future research should provide more details about applying the other deep learning methods for the other options with an appropriate range of historical prices to have stronger support about the deep learning methods are reliable in the practical options market.