政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/118357

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/118357

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政大機構典藏 > 商學院 > 金融學系 > 學位論文 > Item 140.119/118357

請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/118357

題名:	金融壓力事件預警模型：類神經網路、支援向量機與羅吉斯迴歸之比較 Financial Stress Model：Comparison of Artificial Neural Network, Support Vector Machine and Logistic Regression
作者:	朱君亞 Chu, Chun-Ya
貢獻者:	林士貴蔡炎龍 Lin, Shih-Kuei Tsai, Yen-Lung 朱君亞 Chu, Chun-Ya
關鍵詞:	金融情勢指數金融壓力事件預警模型羅吉斯迴歸模型類神經網路模型支援向量機 Financial conditions Index Financial stress model Logistic regression Artificial neural network Support vector machine
日期:	2018
上傳時間:	2018-07-04 14:45:43 (UTC+8)
摘要:	全球化浪潮、科技進步與金融商品日趨複雜等原因，讓金融商品價格容易在短時間內發生大幅波動，也使得風險管理成為不可避免的議題。本文遂針對重大金融危機事件做出定義，稱之為「金融壓力事件」，以股市大跌作為「金融壓力事件」是否發生的判定標準，且分別針對6個月與3個月的兩種不同判定期間做出定義。再以利率、匯率、資產價格等變數，搭配羅吉斯迴歸模型、類神經網路模型與支援向量機，建立金融壓力事件預警模型，每日針對是否發生「金融壓力事件」進行預測。本研究以台灣加權股價指數實證，實證結果顯示，不論哪一種方法，判定期間為6個月的預測結果都優於判定期間為3個月；不論判定期間長短，都是支援向量機預測能力最好，其次為類神經網路模型，羅吉斯迴歸則較弱。 With globalization, new technology and more complicated financial instruments, the financial market become more volatile, making risk management an inevitable issue. In this paper, we define a major financial crisis event as a "financial stress event." It uses the stock market crash as a criterion for the occurrence of a "financial stress event," and define two different judgment periods of 6-months and 3-months respectively. Using the variables such as interest rate, exchange rate, and asset price, together with the Logistic Regression model, Artificial Neural Network model, and Support Vector Machine, a financial stress model was established to predict the occurrence of “financial stress events” everyday. By using Taiwan Capitalization Weighted Stock Index as empirical evidence, the result shows that regardless of the method, the predictability of 6-months judgment period is better than the 3-months period. Regardless of the length of the judgment period, Support Vector Machine has the highest predictability, followed by Artificial Neural Network model, and Logistic Regression is the weakest.
參考文獻:	中文文獻 [1] 王翎聿（2015），應用倒傳遞類神經網路與支援向量機預測加權股價指數，國防大學管理學院財務管理學系碩士班碩士論文。 [2] 呂奇傑、李天行、高人龍、黃敏菁（2009），支援向量機與支援向量迴歸於財務時間序列預測之應用，數據分析，第4卷第2期，35-56。 [3] 張天惠（2012），我國金融情勢指數與總體經濟預測，〈中央銀行季刊〉，第34卷第2期，11-42。 [4] 黃華山與邱一薰（2005）類神經網路預測台灣50 股價指數之研究，資訊、科技與社會學報，第5卷第2期，19-42。 [5] 葉怡成（2003），類神經網路模式應用與實作，臺北市：儒林。英文文獻 [1] Cortes, C. and Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273-297. [2] Gauthier, C., Graham, C., and Liu, Y. (2004). Financial Conditions Indexes for Canada. Bank of Canada Working Paper 2004, 22. [3] Goodhart, C. and Hofmann, B. (2001). Asset Prices, Financial Conditions, and the Transmission of Monetary Policy. Paper prepared for the conference on Asset Prices, Exchange rates, and Monetary Policy, Stanford University, March 2-3. [4] Hatzius, J., Hooper, P., Mishkin, F. S., Schoenholtz, K. L., and Watson, M. W. (2010). Financial Conditions Index: A Fresh Look after the Financial Crisis. NBER Working Paper 16150. [5] Hsieh, L. F., Hsieh, S. C., and Tai, P. H. (2011). Enhanced Stock Price Variation Prediction via DOE and BPNN-based Optimization. Expert Systems with Applications 38, 14178-14184. [6] Hsu, C. W., Chang, C. C., and Lin, C. J. (2003). A Practical Guide to Support Vector Classification. Available at http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf. [7] Huang, W., Nakamoria, Y., and Wang, S. Y. (2004). Forecasting Stock Market Movement Direction with Support Vector Machine. Computers & Operations Research 32, 2513-2522. [8] Kara, Y., Boyacioglu, M. A., and Baykan, Ö . K. (2011). Predicting Direction of Stock Price Index Movement using Artificial Neural Networks and Support Vector Machines: The Sample of the Istanbul Stock Exchange. Expert Systems with Applications 38, 5311-5319. [9] Kim, K. J. (2003). Financial Time Series Forecasting Using Support Vector Machines. Neurocomputing 55, 307-319. [10] Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). Learning Representations by Back-propagating Errors. Nature, 323, 533-536. [11] Silvers, D. and Slavkin, H. (2009). The Legacy of Deregulation and the Financial Crisis: Linkages Between Deregulation in Labor Markets, Housing Finance Markets, and the Broader Financial Markets. Journal of Business & Technology Law 4, 2, 301. [12] Skaarup, M., Duschek-Hansen, C., and Nielsen, S. (2010). A Financial Conditions Index for Denmark. Working Paper no 23/2010, The Danish Ministry of Finance. [13] Svozil, D., KvasniEka, V., and Pospichal, J. (1997). Introduction to Multi-layer Feed-Forward Neural Networks. Chemometrics and Intelligent Laboratory Systems 39, 43-62.
描述:	碩士國立政治大學金融學系 105352011
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0105352011
資料類型:	thesis
DOI:	10.6814/THE.NCCU.MB.013.2018.F06
顯示於類別:	[金融學系] 學位論文

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