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’ 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).
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References
Andersen, T.G., Bollerslev, T., Diebold, F.X., Vega, C.: Micro effects of macro announcements: real-time price discovery in foreign exchange. Am. Econ. Rev. 93(1), 38–62 (2003)
Antoine, B., Cyrille, G., Carlos, A.R., Christian, W., Steffen, N.: High-frequency trading activity in EU equity markets. Econ. Rep. 1, 1–31 (2014)
Baldauf, M., Mollner, J.: High-frequency trading and market performance. J. Finance 75, 1495–1526 (2020)
Brogaard, J., Carrion, A., Moyaert, T., Riordan, R., Shkilko, A., Sokolov, K.: High frequency trading and extreme price movements. J. Financ. Econ. 128(2), 253–265 (2018)
Brogaard, J., Hendershott, T., Riordan, R.: High-frequency trading and price discovery. Rev. Financ. Stud. 27(8), 2267–2306 (2014)
Brogaard, J., Hendershott, T., Riordan, R.: Price discovery without trading: evidence from limit orders. J. Finance 74(4), 1621–1658 (2019)
Cao, C., Hansch, O., Wang, X.: The information content of an open limit-order book. J. Futures Mark.: Futures Options Deriv. Prod. 29(1), 16–41 (2009)
Carrion, A.: Very fast money high-frequency trading on the NASDAQ. J. Financ. Mark. 16(4), 680–711 (2013)
Conrad, J., Wahal, S., Xiang, J.: High-frequency quoting, trading, and the efficiency of prices. J. Financ. Econ. 116(2), 271–291 (2015)
Diebold, F.X., Strasser, G.: On the correlation structure of microstructure noise: a financial economic approach. Rev. Econ. Stud. 80(4), 1304–1337 (2013)
Foucault, T., Hombert, J. Roşu, I.: News trading and speed. J. Finance 71(1), 335–382 (2016)
Gai, J., Choi, D. J., O’Neal, D., Ye, M., Sinkovits, R.S.: Fast construction of nanosecond level snapshots of financial markets. Concurr. Comput. Pract. Exp. 26(13), 2149–2156 (2014)
Grossman, S.J., Miller, M.H.: Liquidity and market structure. J. Finance 43(3), 617–633 (1988)
Hasbrouck, J.: Assessing the quality of a security market: a new approach to transaction-cost measurement. Rev. Financ. Stud. 6(1), 191–212 (1993)
Hasbrouck, J., Saar, G.: Low-latency trading. J. Financ. Mark. 16(4), 646–679 (2013)
Hirschey, N.: Do high-frequency traders anticipate buying and selling pressure?. Available at SSRN 2238516 (2019)
Kaniel, R., Saar, G., Titman, S.: Individual investor trading and stock returns. J. Finance 63(1), 273–310 (2008)
Kelley, E.K., Tetlock, P.C.: How wise are crowds? Insights from retail orders and stock returns. J. Finance 68(3), 1229–1265 (2013)
Kirilenko, A., Kyle, A.S., Samadi, M., Tuzun, T.: The flash crash high-frequency trading in an electronic market. J. Finance 72(3), 967–998 (2017)
Lee, E.J.: High frequency trading in the Korean index futures market. J. Futures Mark. 35(1), 31–51 (2015)
Lin, W.T., Tsai, S.C., Chiu, P.: Do foreign institutions outperform in the Taiwan options market? North Am. J. Econ. Finance 35, 101–115 (2016)
Lin, W.T., Tsai, S.C., Zheng, Z., Qiao, S.: Does options trading convey information on futures prices? North Am. J. Econ. Finance 39, 182–196 (2017)
Lin, W.T., Tsai, S.C., Zheng, Z., Qiao, S.: Retrieving aggregate information from option volume. Int. Rev. Econ. Finance 55, 220–232 (2018)
Malinova, K., Park, A., Riordan, R. Do retail investors suffer from high frequency traders?. Available at SSRN 2183806 (2018)
Menkveld, A., Koopman, S.J., Lucas, A.: Modeling around-the-clock price discovery for cross-listed stocks using state space models. J. Bus. Econ. Stat. 25, 213–225 (2007)
Petris, G., Petrone, S.: State space models in R. J. Stat. Softw. 41(4), 1–25 (2011)
U. S. Securities and Exchange Commission: Equity market structure literature review Part II High frequency trading. Staff of the Division of Trading and Markets (2014)
Van Kervel, V., Menkveld, A.J.: High-frequency trading around large institutional orders. J. Finance 74(3), 1091–1137 (2019)
Yu, W., Li, S.: Research on financial data analysis based on data mining algorithm. Concurr. Comput.: Pract. Exp. 31(10), e4780 (2019)
Zhao, Y., Wan, D.: Institutional high frequency trading and price discovery evidence from an emerging commodity futures market. J. Futures Mark. 38(2), 243–270 (2018)
Zhang, J., Xu, L., Li, Y.: Classifying Python code comments based on supervised learning. In: Meng, X., Li, R., Wang, K., Niu, B., Wang, X., Zhao, G. (eds.) WISA 2018. LNCS, vol. 11242, pp. 39–47. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02934-0_4
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Lin, W.T., Huang, ZH., Tsai, SC. (2020). Fast Trading and Price Discovery in the Financial Crisis: Evidence from the Taiwan Futures Market. In: Wang, G., Lin, X., Hendler, J., Song, W., Xu, Z., Liu, G. (eds) Web Information Systems and Applications. WISA 2020. Lecture Notes in Computer Science(), vol 12432. Springer, Cham. https://doi.org/10.1007/978-3-030-60029-7_47
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