@inproceedings{689d28af78254026b6946a377d7d1eef,
title = "Option pricing using deep convolutional neural networks enhanced by technical indicators",
abstract = "Artificial Neural networks are increasingly employed for option pricing in recent years. However, the pricing ability and effectiveness of convolutional neural networks (CNNs) have not been investigated adequately in the field of financial options. Hence, it is interesting to investigate whether convolutional neural networks are effective in pricing Chinese options. In this paper, an innovative idea of combining 2D-CNN with market technical indicators (TIs) has been implemented. The research is conducted based on the 50ETF options which are obtained from Shanghai Stock Exchange covering a time span from January 2018 to December 2022. Having compared the pricing accuracies between the CNNs (with and without TIs) and the Black-Scholes model, we show that the method of combining CNNs with technical indicators (TIs) are effective in pricing Chinese options. This research is useful for practitioners and researchers in the field of option trading.",
keywords = "Chinese Options, Convolutional Neural Network, Option Pricing, Technical indicators",
author = "David Liu and Yu Wu",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 9th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2023 ; Conference date: 12-04-2023 Through 13-04-2023",
year = "2023",
doi = "10.1109/CCIS59572.2023.10262865",
language = "English",
series = "Proceeding of 2023 9th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "143--147",
editor = "Xuegong Zhang and Mengqi Zhou and Weining Wang and Wenbai Chen and Yaru Zou and Yanna Liu",
booktitle = "Proceeding of 2023 9th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2023",
}