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題名 高頻交易研究的演化及新興趨勢
Evolution and emerging trends in HFT research作者 李耀中
Lee, Mike Y. J.貢獻者 劉文卿
Liu, Wenqing
李耀中
Lee, Mike Y. J.關鍵詞 High Frequency Trading
HFT
Social network analysis
SNA
emerging trends日期 2019 上傳時間 5-Sep-2019 15:43:14 (UTC+8) 摘要 In this research, the evolution and emerging trends of High Frequency Trading (HFT) research is conducted by examining papers published in the Web of Science (WOS) from 1993 to 2017. A total of 241 papers are included, and 1876 keywords from these articles were extracted and analyzed. For tracing the dynamic changes of the HFT Research, the whole 24 years are further separated into three consecutive periods: 1993-2002, 2003-2012, and 2013-2017. The Ucinet is adopted to get keywords network, or knowledge network, to study the relationship of each research theme. NetDraw is applied to visualize network. The social network analysis (SNA) technique is used to reveal patterns and trends in the research by measuring the association strength of terms representative of relevant publications produced in HFT field. Results indicate that HFT research has been strongly influenced by these keywords: “market”, “prices”, “finance”, “liquidity”, “statistics”, “financial markets”, “stock”, “stochastic”, “model” and “trades” as shown in Table 3, which represent some established research themes. These are major focuses and the bridges connecting to other research themes in HFT. The detailed analysis in “Discussions and implications” provides an overview of evolution and emerging trends in HFT Research. It concludes that “market performance” related keywords, which represent some established research themes, have become the major focus in HFT research. It also changes rapidly to embrace new themes. Especially, this research may make contribution to enlarge research method in that there is no SNA research in HFT research before. 參考文獻 1.Brogaard, J., Carrion, A., Moyaert, T., Riordan, R., Shkilko, A., & Sokolov, K. (2018). High frequency trading and extreme price movements. Journal of Financial Economics, 128(2), 253-265.2.https://snipethetrade.com/us/high-frequency-trading3.https://en.wikipedia.org/wiki/High-frequency_trading4.https://www.investopedia.com/terms/h/high-frequency-trading.asp5.https://www.investopedia.com/ask/answers/09/high-frequency-trading.asp6.https://www.investopedia.com/terms/h/high-frequency-trading.asp7.Kumar, P., Goldstein, M., Graves, F., & Borucki, L. (2011). Trading at the speed of Light. The Brattle Group Finance (2).8.Aldridge, I. (2013). High-frequency trading: a practical guide to algorithmic strategies and trading systems (Vol. 604). John Wiley & Sons. ISBN 978-0-470-56376-29.https://www.sec.gov/fast-answers/answersmktmakerhtm.html10.Hendershott, T., Jones, C. M., & Menkveld, A. J. (2011). Does algorithmic trading improve liquidity?. The Journal of Finance, 66(1), 1-33.11.Cartea, A., & Jaimungal, S. (2013). Modelling asset prices for algorithmic and high-frequency trading. Applied Mathematical Finance, 20(6), 512-547.12.Gueant, O., Lehalle, C. A., & Fernandez-Tapia, J. (2013). Dealing with the inventory risk: a solution to the market making problem. Mathematics and financial economics, 7(4), 477-507.13.Jovanovic, B., & Menkveld, A. J. (2016). Middlemen in limit order markets. Available at SSRN 1624329.14.Menkveld, A. J. (2013). High frequency trading and the new market makers. Journal of financial Markets, 16(4), 712-740.15.Weil, J. (2014). Weil on Finance: FBI Hops on Michael Lewis Bandwagon. Bloomberg. URL: https://www.bloomberg.com/opinion/articles/2014-04-01/weil-on-finance-fbi-hops-on-michael-lewis-bandwagon16.Bradford, H. (2014). FBI Investigating High-Frequency Traders: WSJ. Huffington Post. URL: https://www.huffingtonpost.com/2014/03/31/fbi-high-speedtrading_n_5065622.html (???? ?????????: 19.03. 2018).17.https://www.coursehero.com/file/p32cbud/The-World-Of-High-Frequency-Trading-6-Primary-Strategies/18.Gregoriou, G. N. (Ed.). (2015). 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An introduction to structural analysis: The network approach to social research: SD Berkowitz, Toronto: Butterworth, 1982.112.https://cambridge-intelligence.com/keylines-faqs-social-network-analysis/113.Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social networks, 1(3), 215-239.114.Goldstein, M. A., P. Kumar, & F. C. Graves (2014), Computerized and High-Frequency Trading, The Financial Review, Vol. 49, Issue 2, 177–202 : May 2014115.Chung, K. H., & Lee, A. J. (2016). High?frequency Trading: Review of the Literature and Regulatory Initiatives around the World. Asia?Pacific Journal of Financial Studies, 45(1), 7-33. 描述 博士
國立政治大學
資訊管理學系
96356505資料來源 http://thesis.lib.nccu.edu.tw/record/#G0096356505 資料類型 thesis dc.contributor.advisor 劉文卿 zh_TW dc.contributor.advisor Liu, Wenqing en_US dc.contributor.author (Authors) 李耀中 zh_TW dc.contributor.author (Authors) Lee, Mike Y. J. en_US dc.creator (作者) 李耀中 zh_TW dc.creator (作者) Lee, Mike Y. J. en_US dc.date (日期) 2019 en_US dc.date.accessioned 5-Sep-2019 15:43:14 (UTC+8) - dc.date.available 5-Sep-2019 15:43:14 (UTC+8) - dc.date.issued (上傳時間) 5-Sep-2019 15:43:14 (UTC+8) - dc.identifier (Other Identifiers) G0096356505 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/125522 - dc.description (描述) 博士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理學系 zh_TW dc.description (描述) 96356505 zh_TW dc.description.abstract (摘要) In this research, the evolution and emerging trends of High Frequency Trading (HFT) research is conducted by examining papers published in the Web of Science (WOS) from 1993 to 2017. A total of 241 papers are included, and 1876 keywords from these articles were extracted and analyzed. For tracing the dynamic changes of the HFT Research, the whole 24 years are further separated into three consecutive periods: 1993-2002, 2003-2012, and 2013-2017. The Ucinet is adopted to get keywords network, or knowledge network, to study the relationship of each research theme. NetDraw is applied to visualize network. The social network analysis (SNA) technique is used to reveal patterns and trends in the research by measuring the association strength of terms representative of relevant publications produced in HFT field. Results indicate that HFT research has been strongly influenced by these keywords: “market”, “prices”, “finance”, “liquidity”, “statistics”, “financial markets”, “stock”, “stochastic”, “model” and “trades” as shown in Table 3, which represent some established research themes. These are major focuses and the bridges connecting to other research themes in HFT. The detailed analysis in “Discussions and implications” provides an overview of evolution and emerging trends in HFT Research. It concludes that “market performance” related keywords, which represent some established research themes, have become the major focus in HFT research. It also changes rapidly to embrace new themes. Especially, this research may make contribution to enlarge research method in that there is no SNA research in HFT research before. en_US dc.description.tableofcontents 1. Introduction 71.1 Background 71.1.1 What is High-Frequency Trading (HFT) 81.1.2 How does HFT work? 91.1.3 Basics of High-Frequency Trading 101.1.4 What’s the importance of speed in trading? 101.1.5 High Frequency Trading Strategies 111.1.5.1 Market making 121.1.5.2 Fraud 131.1.5.3 Ticker tape trading 131.1.5.4 Event arbitrage 141.1.5.5 Statistical arbitrage 141.1.5.6 Index arbitrage 151.1.5.7 News-based trading 151.1.5.8 Low-latency strategies 151.1.5.9 Order properties strategies 161.1.6 What algorithms are used in HFT? 161.1.7 Benefits and critiques of HFT 171.2 History of high-frequency trading 181.2.1 Market growth 201.2.2 Market share 201.3 Definition of high-frequency trading 221.3.1 Metrics for Defining HFT from SEC 221.3.2 HFT definition from NASDAQ.com 231.3.3 HFT definition from Wikipedia 231.3.4 HFT definition from Investopedia 231.3.5 Conclusion 241.4 Current qualitative research in HFT 241.5 Research categories and findings in HFT 251.6 Problems being investigated 291.7 General approach 301.8 Purpose of the Research 302. Literature review 322.1 Social network analysis (SNA) 322.2 Some SNA measures 322.2.1 SNA measure 1 : Degree Centrality 332.2.2 SNA measure 2 : Betweenness centrality 332.2.3 SNA measure 3 : Closeness centrality 342.2.4 SNA measure 4 : PageRank 352.2.5 SNA measure 5 : EigenCentrality 352.3 Some SNA software packages 362.3.1 KeyLines from Cambridge Intelligence 362.3.2 UCINET 6 for Windows 372.4 Bibliometrics 372.4.1 Bradford’s law 382.4.2 Lotka`s law 382.4.3 Zipf`s law 382.4.4 Comparison and Summary 392.4.5 Other Bibliometrics methods 392.4.5.1 Publication outputs of paper titles 392.4.5.2 Publication outputs of KeyWords Plus 402.4.5.3 Publication outputs by abstract analysis 403. Research methods 413.1 Purpose of the research 413.2 Materials and methods 413.2.1 Author keywords 423.2.2 Keywords Plus 433.2.3 Screening Methodology 433.3 Refinement of keywords and keywords databases 433.4 Constructing keywords network 443.5 Centrality measures of the SNA network 453.6 Network visualizations 454. The experiment and results 474.1 Keywords network 474.2 Betweenness centrality measuring for all period (1993-2017) 484.3 Changes in important keywords over time 505. Discussions and implications 525.1 The findings of the quantitative analysis results 525.2 Comparison with previous qualitative research in HFT546. Conclusion 566.1 The original problem statement 566.2 Summarization 566.3 Limitations 576.4 Paths for future research 57Reference 59 zh_TW dc.format.extent 3374496 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0096356505 en_US dc.subject (關鍵詞) High Frequency Trading en_US dc.subject (關鍵詞) HFT en_US dc.subject (關鍵詞) Social network analysis en_US dc.subject (關鍵詞) SNA en_US dc.subject (關鍵詞) emerging trends en_US dc.title (題名) 高頻交易研究的演化及新興趨勢 zh_TW dc.title (題名) Evolution and emerging trends in HFT research en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 1.Brogaard, J., Carrion, A., Moyaert, T., Riordan, R., Shkilko, A., & Sokolov, K. (2018). High frequency trading and extreme price movements. 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