A market making quotation strategy based on dual deep learning agents for option pricing and bid-Ask spread estimation

Pei Ying Hsu, Chin Chou, Szu-Hao Huang, An-Pin Chen

研究成果: Conference contribution同行評審

5 引文 斯高帕斯(Scopus)

摘要

Traditional professional traders and institutional investors utilized complex statistical models to price various derivative contracts and make trading decisions in the option and future markets. In recent years, with the rapid growth of algorithmic trading and program trading, the advanced information and communication technology has become an indispensable element for high-frequency traders, especially for the market makers. In addition, artificial intelligence and deep learning also plays an important role in novel financial technology (FinTech) research field. In this paper, we proposed a market making quotation strategy based on deep learning structure and practical finance domain knowledge. The proposed dual agents will simultaneously model the option prices and bid-Ask spreads. The experiments demonstrate that our system can precisely estimate the value of options than famous financial engineering models. It also can be extended to develop proper market making quotation strategies to trade the options of Taiwan Stock Exchange Capitalization Weighted Stock Index(TAIEX).

原文American English
主出版物標題Proceedings - 2018 IEEE International Conference on Agents, ICA 2018
發行者Institute of Electrical and Electronics Engineers Inc.
頁面99-104
頁數6
ISBN(列印)9781538681800
DOIs
出版狀態Published - 10 9月 2018
事件2018 IEEE International Conference on Agents, ICA 2018 - Singapore, Singapore
持續時間: 28 7月 201831 7月 2018

出版系列

名字Proceedings - 2018 IEEE International Conference on Agents, ICA 2018

Conference

Conference2018 IEEE International Conference on Agents, ICA 2018
國家/地區Singapore
城市Singapore
期間28/07/1831/07/18

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