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題名 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
作者 宋鴻緯
Sung, Hong Wei
貢獻者 林士貴
Lin, Shih Kuei
宋鴻緯
Sung, Hong Wei
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
logistic regression
artificial neural networks
arbitrage
effective marketing
put call parity
日期 2015
上傳時間 1-Jun-2016 13:49:39 (UTC+8)
摘要 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).
參考文獻 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.
描述 碩士
國立政治大學
金融研究所
102352021
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0102352021
資料類型 thesis
dc.contributor.advisor 林士貴zh_TW
dc.contributor.advisor Lin, Shih Kueien_US
dc.contributor.author (Authors) 宋鴻緯zh_TW
dc.contributor.author (Authors) Sung, Hong Weien_US
dc.creator (作者) 宋鴻緯zh_TW
dc.creator (作者) Sung, Hong Weien_US
dc.date (日期) 2015en_US
dc.date.accessioned 1-Jun-2016 13:49:39 (UTC+8)-
dc.date.available 1-Jun-2016 13:49:39 (UTC+8)-
dc.date.issued (上傳時間) 1-Jun-2016 13:49:39 (UTC+8)-
dc.identifier (Other Identifiers) G0102352021en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/97101-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 金融研究所zh_TW
dc.description (描述) 102352021zh_TW
dc.description.abstract (摘要) 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。zh_TW
dc.description.abstract (摘要) 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).en_US
dc.description.tableofcontents 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		zh_TW
dc.format.extent 1885165 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0102352021en_US
dc.subject (關鍵詞) 套利zh_TW
dc.subject (關鍵詞) 效率市場zh_TW
dc.subject (關鍵詞) 類神經網路zh_TW
dc.subject (關鍵詞) 羅吉斯zh_TW
dc.subject (關鍵詞) 買權賣權平價等式zh_TW
dc.subject (關鍵詞) logistic regressionen_US
dc.subject (關鍵詞) artificial neural networksen_US
dc.subject (關鍵詞) arbitrageen_US
dc.subject (關鍵詞) effective marketingen_US
dc.subject (關鍵詞) put call parityen_US
dc.title (題名) 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zh_TW
dc.title (題名) Distinguishing valid arbitrage opportunities in Taiwan option and future market by logistic regression and artificial neural networksen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 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.		zh_TW