簡易檢索 / 詳目顯示
| 研究生： |
宋鴻緯 Sung, Hong Wei |
|---|---|
| 論文名稱： |
以羅吉斯與類神經模型辨別台灣選擇權與期貨市場間的有效套利機會 Distinguishing valid arbitrage opportunities in Taiwan option and future market by logistic regression and artificial neural networks |
| 指導教授： |
林士貴
Lin, Shih Kuei |
| 學位類別： |
碩士
Master |
| 系所名稱： |
商學院 - 金融學系 Department of Money and Banking |
| 論文出版年： | 2015 |
| 畢業學年度： | 103 |
| 語文別： | 中文 |
| 論文頁數： | 56 |
| 中文關鍵詞： | 套利 、效率市場 、類神經網路 、羅吉斯 、買權賣權平價等式 |
| 外文關鍵詞： | logistic regression, artificial neural networks, arbitrage, effective marketing, put call parity |
| 相關次數： | 點閱：45 下載：17 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
本研究在考慮交易成本的情況下，利用羅吉斯模型、類神經模型以及其兩者的混合模型建立一分類器，用以識別台灣選擇權與期貨市場中違反買權賣權平價等式的套利訊號。由逐筆成交資料的實證結果顯示，無論在金融海嘯（2007）、景氣復甦（2008）或是平穩時期（2012~2014）時，就識別率來說三種模型相差不大，但就獲利性而言混合模型有略優於其他兩者的表現。
Considering the transaction cost, we establish a binary classifier system by logistic regression, artificial neural networks and hybird model with aboves. The system is used for distinguishing valid arbitrage opportunities which violated put call parity in Taiwan option and future market. By tickdata, we find that, although three models has same accuracy on classification almostly, hybird model is grater then the others in profitability no matter in depression(2007), boom(2008) or business steady state(2012~2014).
第1章 緒論 1
第2章 文獻回顧 4
2.1 套利相關研究 4
2.2 統計模型配適相關研究 5
2.3 人工智慧模型配適相關研究 6
2.4 小結 7
第3章 研究方法 8
3.1 交易策略 8
3.1.1 套利理論 8
3.1.2 交易成本 9
3.1.3 配對方式 11
3.2 模型配適 12
3.2.1 反應變數 12
3.2.2 解釋變數 14
3.2.3 羅吉斯配適 15
3.2.4 類神經配適 16
3.2.5 混合模型配適 17
3.3 模型評估 18
3.3.1 ROC曲線 18
3.3.2 Wald test 19
3.3.3 相對閥值 19
第4章 實證分析 20
4.1 資料來源與組成 20
4.2 變數選取 21
4.3 模型評估 22
第5章 結論 26
參考文獻 27
中文文獻 27
外文文獻 27
附件A 手續費計算方式 31
附件B 樣本敘述性統計 32
附件C 實證結果 35
中文文獻
[1] 尹相志. SQL Server 2008 Data Mining資料採礦. 2009.
[2] 邱一薰; 黃華山. 類神經網路預測台灣 50 股價指數之研究. 國立彰化師範大學資訊管理學系所碩士論文, 2005.
[3] 吳秋練. 以盒型價差策略探討台指選擇權市場之效率性與套利機會. 臺北大學統計學系學位論文, 2011, 1-49.
[4] 余適安. 衍生性金融商品百問. 2010, 3-4.
[5] 周恆志; 杜玉振. 臺指選擇權市場之套利效率. 銘傳大學財務金融學系學為論文, 2005.
[6] 姜林杰祐; 鐘芳玫. 台指選擇權套利機會分析. 高雄應用科技大學學報, 2006
[7] 陳秀萍. 多種價差策略與台指選擇權套利機會之研究. 高雄應用科技大學金融資訊學系碩士論文, 2007.
外文文獻
[8] ACKERT, Lucy F.; TIAN, Yisong S. Efficiency in index options markets and trading in stock baskets. Journal of Banking & Finance, 2001, 25.9: 1607-1634.
[9] Agresti, Alan. An Introduction to Categorical Data Analysis, 2nd Edition. March 2007.
[10] A.M. Legendre. Nouvelles méthodes pour la détermination des orbites des comètes, Firmin Didot, Paris, 1805.
[11] BAE, Kee-Hong; CHAN, Kalok; CHEUNG, Yan-Leung. The profitability of index futures arbitrage: Evidence from bid-ask quotes. Journal of Futures Markets, 1998, 18.7: 743-763.
[12] BENZION, Uri; ANAN, Shmuel D.; YAGIL, Joseph. Box spread strategies and arbitrage opportunities. The Journal of Derivatives, 2005, 12.3: 47-62.
[13] Bliss, C. I. "The Method of Probits". Science, 1934, 79 (2037): 38–39
[14] Black, F., and Scholes, M. The pricing of options and corporate liabilities. The Journal of Political Economy, 1973, 81, 3, 637-654.
[15] CAPELLE-BLANCARD, Gunther; CHAUDHURY, Mo. Do market and contract designs matter? Evidence from the CAC 40 index options market. Cahiers de la MSE, 2003.
[16] Cox, DR. "The regression analysis of binary sequences (with discussion)". J Roy Stat Soc B, 1958, 20: 215–242.
[17] DEMPSTER, M. A. H.; JONES, C. M. A real-time adaptive trading system using genetic programming. Quantitative Finance, 2001, 1.4: 397-413.
[18] Dutta, A., Bandopadhyay, G., & Sengupta, S. (2012). Prediction of stock performance in the Indian stock market using logistic regression. International Journal of Business and Information, 7(1), 105-136.
[19] Gong, J., & Sun, S. (2009, June). A New Approach of Stock Price Prediction Based on Logistic Regression Model. In New Trends in Information and Service Science, 2009. NISS'09. International Conference on (pp. 1366-1371). IEEE.
[20] Holthausen, R. W., & Larcker, D. F. (1992). The prediction of stock returns using financial statement information. Journal of Accounting and Economics, 15(2), 373-411.
[21] Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of accounting research, 109-131.
[22] REFENES, Apostolos Nicholas; ZAPRANIS, Achileas; FRANCIS, Gavin. Stock performance modeling using neural networks: a comparative study with regression models. Neural networks, 1994, 7.2: 375-388.
[23] KING, Gary; ZENG, Langche. Logistic regression in rare events data. Political analysis, 2001, 9.2: 137-163.
[24] Minsky, M.; S. Papert. An Introduction to Computational Geometry. MIT Press. 1969.
[25] Rosenblatt, F. "The Perceptron: A Probabilistic Model For Information Storage And Organization In The Brain". Psychological Review, 1958, 65 (6): 386–408.
[26] Rumelhart, D.E; James McClelland. Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Cambridge: MIT Press. 1986.
[27] SAS Institute Inc. SAS Enterprise Miner 13.2: Reference Help. 2013.
[28] Tsai, C. F., and S. P. Wang. "Stock price forecasting by hybrid machine learning techniques." Proceedings of the International MultiConference of Engineers and Computer Scientists. Vol. 1. No. 755. 2009.
[29] TUCKER, Jon; TUCKER, Dr Jon. Neural networks versus logistic regression in financial modelling: A methodological comparison. In: in Proceedings of the 1996 World First Online Workshop on Soft Computing (WSC1. 1996.
[30] TUNÇ, Taner. A new hybrid method logistic regression and feedforward neural network for lung cancer data. Mathematical Problems in Engineering, 2012, 2012.
[31] Turing, Alan M. “Computing machinery and intelligence” Mind LIX (238): 433–460. 1950.
