@inproceedings{c40e0dbc934b4a128dac850526630198,
title = "Fast trading and price discovery in the financial crisis: Evidence from the Taiwan futures market",
abstract = "This paper used millisecond-level intraday data from the Taiwan futures market during the financial crisis to propose an effective data processing method using the program and a non-SQL database. Fast traders were classified based on the investors{\textquoteright} trading volume and position size. First, the state space model was used to decompose the prices. It was discovered that fast trading (FT) can cause permanent price increments, which are independent of temporary prices. FT during the financial crisis helped improve price efficiency and liquidity. Second, the activity of FT is based on public information, which makes price discovery during a high-Volatility Index (VIX) period possible and causes an increase in the adverse selection cost of non-fast traders (non-FT).",
keywords = "Data mining, Fast trading, Financial crisis, Futures market, Price discovery",
author = "Lin, {William T.} and Huang, {Zi Huang} and Tsai, {Shih Chuan}",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 17th International Conference on Web Information Systems and Applications, WISA 2020 ; Conference date: 23-09-2020 Through 25-09-2020",
year = "2020",
doi = "10.1007/978-3-030-60029-7_47",
language = "English",
isbn = "9783030600280",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "525--536",
editor = "Guojun Wang and Xuemin Lin and James Hendler and Wei Song and Zhuoming Xu and Genggeng Liu",
booktitle = "Web Information Systems and Applications - 17th International Conference, WISA 2020, Proceedings",
}