Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/97100
|
Title: | 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 |
Authors: | 宋鴻緯 Sung, Hong Wei |
Contributors: | 林士貴 Lin, Shih Kuei 宋鴻緯 Sung, Hong Wei |
Keywords: | 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 |
Date: | 2015 |
Issue Date: | 2016-06-01 13:48:51 (UTC+8) |
Abstract: | 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). |
Reference: | 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. |
Description: | 碩士 國立政治大學 金融研究所 102352021 |
Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0102352021 |
Data Type: | thesis |
Appears in Collections: | [金融學系] 學位論文
|
Files in This Item:
File |
Size | Format | |
202101.pdf | 1840Kb | Adobe PDF2 | 99 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|