本研究探討台灣期貨市場快速交易對流動性之影響。Lin et al. (2017)指出台灣為新興市場，散戶在市場中的作用舉足輕重，所以本文也將探討不同身份別快速交易者對於市場流動性的不同之處。Brogaard et al. (2014)認為不同積極度的交易者會擁有著不同的資訊，故本文將進一步探究不同積極度快速交易者對市場流動性的影響。本文依據歐洲證券及市場管理局 (ESMA)歸納出的高頻交易者特徵：委託對成交比例高、平均持倉時間短，來做為本研究定義快速交易之條件。最後利用篩選出的快速交易者，探討其對台灣期貨市場流動性之影響。基於台灣市場為委託單驅動之特性，本文不使用歐美所常用流動性代理變數-價差，而採用交易成本(CTT)來衡量市場流動性。
本文研究發現快速交易與交易成本呈現顯著負相關，即快速交易有利於市場流動性。本文進一步將快速交易依交易者身份別分解，發現散戶快速交易者為流動性的供給者，外資、本地法人快速交易者為流動性的需求者。最後通過對快速交易者以積極度分類，發現積極類型與中性類型的散戶快速交易者對於市場流動性改善貢獻最大。
本文也通過選用不同研究樣本與篩選比例、高波動區間進行了穩健性檢驗，都取得了類似結果。

This study explores the impact of fast trading in Taiwan's futures market liquidity. Lin et al.(2017) believe that Taiwan is an emerging market, the role of retail investors is very important, so this article will also explore the differences between different identity investors. Brogaard et al.(2014) believe that different enthusiasm traders have different messages that have different effects on market liquidity, so this article will further explore the impact of different positive investors on market liquidity.According to the high-frequency trader characteristics summarized by The European Securities and Markets Authority, the conditions for defining fast trading for this study are that the order-to-trade ratio is high and meantime is low . Finally, the use of selected fast traders to explore their impact on Taiwan’s futures market liquidity .Because of the order driven market of the Taiwan’s futures market, this paper apply the cost-to-trade(CTT) to measure market liquidity.
The study found that fast trading and CTT are significantly negatively correlated, that is, fast trading can improve market liquidity. In this paper, the fast trading are further decomposed according to the investors, and it is found that retail ' fast trading are the suppliers of liquidity, Foreign and Domestic Institutional Investors' fast trading
III
are the requester of liquidity. And we found that the aggressive and netural retail investors contribute the most to the improvement of market liquidity.
Finally, the paper uses different samples and high fluctuation intervals to carry out the robustness test, and all have achieved similar results.

目錄
第一章 緒論 1
第一節 研究動機與目的 1
第二節 研究目的 3
第三節 研究架構 4
第四節 研究流程 5
第二章 文獻探討6
第一節 高頻交易之相關文獻 6
第二節 流動性相關文獻9
第三節 積極度相關文獻10
第三章 研究方法11
第一節 樣本資料說明11
第二節、樣本篩選.15
第三節 理論與實證方法19
第四章 實證結果與分析29
第一節 敘述統計29
第二節 迴歸分析32
第三節 穩健性檢驗38
第五章 結論與建議50
參考文獻52
表目錄
【表3-1】2009年至2012年各項商品年成交量統計表 11
【表3-2】台灣證券交易所股價指數期貨契約規格 12
【表3-3】台指期貨每日各契約月份成交量平均比重 15
【表3-4】台股期貨委託至成交所需時間統計 16
【表3-5】台股期貨市場交易者交易特征敘述統計 18
【表3-6】台股期貨最佳一檔買賣價差敘述統計 20
【表3-7】流動性需求與供給區分 23
【表3-8】全市場積極度敘述統計 24
【表3-9】全市場各積極度交易者數量及佔總交易量之敘述統計(本文標準)25
【表3-10】快速交易者積極度敘述統計 26
【表3-11】各積極度快速交易者數量及佔總交易量之敘述統計 26
【表4-1】全市場交易者總交易量佔比敘述統計表 29
【表4-2】主要變數敘述統計表 30
【表4-3】快速交易變數敘述統計表 30
【表4-4】快速交易積極度變數敘述統計表 31
【表4-5】快速交易者對期貨市場流動性影響 33
【表4-6】不同積極度下快速交易者對流動性影響 35
【表4-7】不同積極度下不同身份別快速交易者對流動性影響 37
【表4-8】快速交易者對期貨市場流動性影響(比例10%) 39
【表4-9】不同積極度下快速交易者對流動性影響(比例10%)  40
【表4-10】不同積極度下不同身份別快速交易者對流動性影響(比例10%) 41
【表4-11】快速交易者對期貨市場流動性影響(選用小台)43
【表4-12】不同積極度下快速交易者對流動性影響(選用小台)44
【表4-13】不同積極度下不同身份別快速交易者對流動性影響(選用小台) 45
【表4-14】快速交易者對期貨市場流動性影響(高波動) 47
【表4-15】不同積極度下快速交易者對流動性影響(高波動) 48
【表4-16】不同積極度下不同身份別快速交易者對流動性影響(高波動)  49
圖目錄
【圖1-1】研究流程圖  5

參考文獻
1.	Bagehot, W. (1971), “The only game in Town.” Financial Analysis Journal, 27, 12-14. 
2.	Bloomfield, R., O’hara, M., & Saar, G. (2005). The “make or take” decision in an electronic market: Evidence on the evolution of liquidity. Journal of Financial Economics, 75(1), 165-199.
3.	Benos, E.,& Sagade, S (2016). Price discovery and the cross-section of highfrequency. Journal of Financial Markets, 30, 54-77
4.	Brogaard, J., Hendershott, T., & Riordan, R. (2014). High-frequency trading and price discovery. Review of Financial Studies, 27(8), 2267-2306.
5.	Brogaard, J., Hendershott, T., & Riordan, R. (2017). High frequency trading and the 2008 short-sale ban. Journal of Financial Economics, 124(1), 22-42.
6.	Boehmer, E., Fong, K., & Wu, J. (2015). International evidence on algorithmic trading. In AFA 2013 San Diego Meetings Paper.
7.	Chordia, T., Roll, R., & Subrahmanyam, A. (2005), Evidence on the speed of convergence to market efficiency, Journal of Financial Economics, 76, 271-292.
8.	Chordia, T., Roll, R., & Subrahmanyam, A. (2008). Liquidity and market efficiency. Journal of Financial Economics, 87(2), 249-268.
9.	Chang, C. C., Hsieh, P. F., & Wang, Y. H. (2010). Information content of options trading volume for future volatility: Evidence from the Taiwan options market. Journal of Banking & Finance, 34(1), 174-183.
10.	Carrion, A. (2013). Very fast money: High-frequency trading on the NASDAQ. Journal of Financial Markets, 16(4), 680-711.
11.	Diebold, F. X., & Strasser, G. (2013). On the correlation structure of microstructure noise: A financial economic approach. Review of Economic Studies, 80(4), 1304-1337.
12.	ESMA(2014),”High-frequency trading activity in EU equity markets”Available at: https://www.esma.europa.eu/sites/default/files/library/2015/11/esma20141_-_hft_activity_in_eu_equity_markets.pdf.
13.	Foucault, T., Kadan, O., & Kandel, E. (2013). Liquidity cycles and make/take fees in electronic markets. The Journal of Finance, 68(1), 299-341.
14.	Hasbrouck, J., & Saar, G. (2013). Low-latency trading. Journal of Financial Markets, 16(4), 646-679.
15.	Hendershott, T., & Riordan, R. (2013). Algorithmic trading and the market for liquidity. Journal of Financial and Quantitative Analysis, 48(4), 1001-1024.
16.	Hirschey, N. (2018). Do high-frequency traders anticipate buying and selling pressure?. Available at SSRN 2238516.
17.	Irvine, P. J., Benston, G. J., & Kandel, E. (2000). Liquidity beyond the inside spread: Measuring and using information in the limit order book. Available at SSRN 229959
18.	Kang, W., & Yeo, W. Y. (2008). Liquidity beyond the best quote: A study of the NYSE limit order book.，working paper
19.	Kirilenko, A., Kyle, A. S., Samadi, M., & Tuzun, T. (2017). The Flash Crash: High‐frequency trading in an electronic market. The Journal of Finance, 72(3), 967-998.
20.	Lee, C., &Ready, M. J. (1991). Inferring trade direction from intraday data. The Journal of Finance, 46(2), 773-746.
21.	Locke, P. R., & Mann, S. C. (2005). Professional trader discipline and trade disposition. Journal of Financial economics, 76(2), 401-444.
22.	Lin, W., S.C. Tsai and D. Sun (2012), “Search costs and investor trading activity: Evidence from limit order books,” Emerging Markets Finance & Trade, Vol. 48, No. 3, 4-30. 
23.	Lin, W. T., Tsai, S. C., & Chiu, P. (2016). Do foreign institutions outperform in the Taiwan options market?. The North American Journal of Economics and Finance, 35, 101-115.
24.	Lin, W. T., Tsai, S., Chiu, P. Zheng., & Qiao, S.(2017)."Retrieving Aggregate Information from Option Volume." (accepted by) International Review of Economics and Finance.
25.	O’Hara, M. (2015). High frequency market microstructure. Journal of Financial Economics, 116(2), 257-270.
26.	Rosu, I. (2016). Liquidity and information in order driven markets. Working paper
27.	SEC.(2014), “Concept release on equity market structure literature review,” Available at: 
https://www.sec.gov/marketstructure/research/hft_lit_review_march_2014.pdf.
28.	Subrahmanyam, A., & Zheng, H. (2016). Limit order placement by high-frequency traders. Borsa Istanbul Review, 16(4), 185-209.
29.	Van Ness, B. F., Van Ness, R. A., & Watson, E. D. (2015). Canceling liquidity. Journal of Financial Research, 38(1), 3-33.
30.	Zhang, F. (2010). High-frequency trading, stock volatility, and price discovery. Available at SSRN 1667991