@inproceedings{2cbeb62dc2e444b2aa96ad1c41fec2eb,
title = "A market making quotation strategy based on dual deep learning agents for option pricing and bid-Ask spread estimation",
abstract = "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).",
keywords = "Bid-Ask spread, Deep learning, Market makers, Option pricing",
author = "Hsu, {Pei Ying} and Chin Chou and Szu-Hao Huang and An-Pin Chen",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Agents, ICA 2018 ; Conference date: 28-07-2018 Through 31-07-2018",
year = "2018",
month = sep,
day = "10",
doi = "10.1109/AGENTS.2018.8460084",
language = "American English",
isbn = "9781538681800",
series = "Proceedings - 2018 IEEE International Conference on Agents, ICA 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "99--104",
booktitle = "Proceedings - 2018 IEEE International Conference on Agents, ICA 2018",
address = "United States",
}