臺灣博碩士論文加值系統

本研究探討限價委託簿資訊與股價的關係，嘗試建立模型解釋股價變化，並分析各因子的特性與不同狀況下之解釋力變化。本研究先實證Cont, Kukanov and Stoikov (2014)提出的Order Flow Imbalance (OFI)模型在台灣證券市場上的結果，對股價變化具有解釋力且對各股票類型都當相當適用。再來對OFI模型中的變數因子分開做迴歸分析，發現OFI模型的變數設定組態已相當佳。本研究並改進模型加入第二階買賣量、第一二階量的差值變化、初始的委託量等因子，對股價變化的解釋力相對OFI單因子模型有提升；而第三階買賣量的資訊對股價變化解釋力較低。再做穩健性分析多因子模型在不同取樣時間長度下的情況，顯示取樣時間長度越長則解釋力越佳。最後分析此模型在其他不同狀況下的解釋力變化，發現當日價格升降變動總單位高、為價值股或為成長股，類別為電子股時此模型的解釋力較高，而對金融股解釋力較低。其他如日成交量、日成交總額，對此模型的解釋性變化不顯著。

This study investigates the relationship between the information of Limit Order Book and the stock price, trying to construct a model to explain the stock price change and analyze the characteristics of variables and the changes in explanatory power under different conditions. To begin with, this study verifies the theory of the Order Flow Imbalance (OFI) model (Cont, Kukanov and Stoikov, 2014) in Taiwan stock market, finding that this model not only has explanatory power for stock price change but also is fitting to all stock types. Regression analysis is then performed on the variables in the OFI model, and the variable setting of the OFI model is satisfactory. Furthermore, this study also enhances the model to make improvement on the explanatory power of stock price change compared with OFI single factor model by adding factors such as the change of second-order bid and ask volume, the change of the difference between the first and second-order bid and ask volume, and the initial bid and ask volume. The explanatory power of the stock price changes is improved compared with the OFI single-factor model. However, the information of third-order bid and ask volume has a relatively low explanatory power for stock price change. Additionally, the robustness analysis of the multi-factor model under different sampling duration shows that the longer the sampling duration, the better the explanatory power. Finally, this study analyzes the explanatory power changes of this model in different situations, finding that when the total ticks of price fluctuations on the day is high, when the stock is a value or a growth stock, or an electronic stock, the explanatory power of this model is higher, but the explanatory power for financial stocks are lower. Others factors such as daily trading volume and total daily trading turnover have no significant explanatory changes to this model.

口試委員會審定書 i
中文摘要 ii
英文摘要 iii
目錄 v
圖目錄 vii
表目錄 viii
第一章 緒論 1
第一節 研究動機 1
第二節 研究目的 2
第三節 研究架構 2
第二章 文獻回顧與相關模型 3
第一節 文獻回顧 3
第二節 相關模型 4
第三章 研究方法 6
第一節 資料範圍與定義 6
第二節 研究方法 6
第四章 實證結果 14
第一節 台灣市場套用OFI模型 14
第二節 OFI模型的變量是否為最佳組態 16
第三節 OFI模型加上更多因子 18
第四節 不同取樣時間長度解釋力變化 22
第五節 分析影響模型解釋力之因子 32
第六節 模型對下一段時間的股價變動預測能力 35
第五章 結論與建議 38
第一節 研究結論 38
第二節 研究限制 39
第三節 研究建議 39
參考文獻 41

吳承勳（2019）。基於高頻交易資料的模擬系統。國立中山大學應用數學系研究所碩士論文，高雄市。 取自https://hdl.handle.net/11296/d4236s
陳元豪（2016）。偵測股票市場中具有影響力的交易。國立中山大學應用數學系研究所碩士論文，高雄市。 取自https://hdl.handle.net/11296/2hh7a6
闕銘浚（2015）。委託簿動態之隨機模型量化分析與探討。國立中央大學統計研究所碩士論文，桃園縣。 取自https://hdl.handle.net/11296/5rt9k8
Bacry, E., Mastromatteo, I., & Muzy, J. F. (2015). Hawkes processes in finance. Market Microstructure and Liquidity, 1(01), 1550005.
Bouchaud, J. P., Farmer, J. D., & Lillo, F. (2009). How markets slowly digest changes in supply and demand. In Handbook of financial markets: dynamics and evolution (pp. 57-160). North-Holland.
Capponi, F., & Cont, R. (2019). Trade duration, volatility and market impact. Volatility and Market Impact (March 14, 2019).
Cartea, A., Donnelly, R., & Jaimungal, S. (2018). Enhancing trading strategies with order book signals. Applied Mathematical Finance, 25(1), 1-35.
Cont, R., Kukanov, A., & Stoikov, S. (2014). The price impact of order book events. Journal of financial econometrics, 12(1), 47-88.
Cont, R., & De Larrard, A. (2013). Price dynamics in a Markovian limit order market. SIAM Journal on Financial Mathematics, 4(1), 1-25.
Farmer, J. D., Gerig, A., Lillo, F., & Mike, S. (2006). Market efficiency and the long-memory of supply and demand: is price impact variable and permanent or fixed and temporary?. Quantitative finance, 6(02), 107-112.
Farmer, J. D., Patelli, P., & Zovko, I. I. (2005). The predictive power of zero intelligence in financial markets. Proceedings of the National Academy of Sciences, 102(6), 2254-2259.
Gabaix, X., Gopikrishnan, P., Plerou, V., & Stanley, H. E. (2003). A theory of power-law distributions in financial market fluctuations. Nature, 423(6937), 267-270.
Gomes, C., & Waelbroeck, H. (2015). Is market impact a measure of the information value of trades? Market response to liquidity vs. informed metaorders. Quantitative Finance, 15(5), 773-793.
Gould, M. D., Porter, M. A., Williams, S., McDonald, M., Fenn, D. J., & Howison, S. D. (2013). Limit order books. Quantitative Finance, 13(11), 1709-1742.
Gould, M. D., & Bonart, J. (2016). Queue imbalance as a one-tick-ahead price predictor in a limit order book. Market Microstructure and Liquidity, 2(02), 1650006.
Huang, W., Lehalle, C. A., & Rosenbaum, M. (2015). Simulating and analyzing order book data: The queue-reactive model. Journal of the American Statistical Association, 110(509), 107-122.
Kearns, M., & Nevmyvaka, Y. (2013). Machine learning for market microstructure and high frequency trading. High Frequency Trading: New Realities for Traders, Markets, and Regulators.
Lipton, A., Pesavento, U., & Sotiropoulos, M. G. (2013). Trade arrival dynamics and quote imbalance in a limit order book. arXiv preprint arXiv:1312.0514.
Moro, E., Vicente, J., Moyano, L. G., Gerig, A., Farmer, J. D., Vaglica, G., ... & Mantegna, R. N. (2009). Market impact and trading profile of large trading orders in stock markets. Gabriella and Lillo, Fabrizio and Mantegna, Rosario Nunzio, Market Impact and Trading Profile of Large Trading Orders in Stock Markets (August 3, 2009).
Rashid, A. (2007). Stock prices and trading volume: An assessment for linear and nonlinear Granger causality. Journal of Asian Economics, 18(4), 595-612.
Shen, D. (2015). Order Imbalance Based Strategy in High Frequency Trading (Doctoral dissertation, oxford university).
Torre, N. G., (1997). BARRA Market Impact Model Handbook. BARRA Inc., Berkeley, CA.
Torre, N. G., (1999). The Market Impact Model. BARRA Research Insights, BARRA Inc
Yang, T. W., & Zhu, L. (2016). A reduced-form model for level-1 limit order books. Market Microstructure and Liquidity, 2(02), 1650008.