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系統識別號 U0026-0812200915303993
論文名稱(中文) 台灣加權股價指數極短線之預測
論文名稱(英文) Extreme Short-Term of Forecasting in Taiwan weighting stock price index
校院名稱 成功大學
系所名稱(中) 工業與資訊管理學系碩博士班
系所名稱(英) Department of Industrial and Information Management
學年度 97
學期 2
出版年 98
研究生(中文) 蘇瑞魁
研究生(英文) Jui-kuei Su
電子信箱 r3696112@mail.ncku.edu.tw
學號 r3696112
學位類別 碩士
語文別 中文
論文頁數 36頁
口試委員 口試委員-王清正
口試委員-李賢得
指導教授-利德江
中文關鍵字 趨勢力道函數  星期五行為  類神經網路  支撐向量迴歸  預測  小樣本  統計方法 
英文關鍵字 small data  forecasting  Friday behavior  neural network  regression  support vector regression 
學科別分類
中文摘要 針對股市價預測而言,利用過去大量的歷史資料去做分析並找出一個趨勢可能失去部份意義,故本研究不同於過去之研究,主要是針對每週的星期五去做行為模式的預測。根據實驗結果發現,星期五效應在過去三年間並不顯著,且若加上跨週之資訊,將會使得星期五之行為更難以捉摸。最後本研究測試了多種預測模式:傳統的線性回歸、計量模型中的ARIMMA、類神經網路的倒傳遞類神經(BPNN)、輻狀基底類神經(RBF)、支撐向量回歸(SVR)以及趨勢力道函數簡單型(CLTM-S)、複雜型(CLTM),在各種方法之測試下,不同於傳統之結果,針對星期五行為預測以類神經網路的倒傳遞類神經(BPNN)結果最佳,而在三種群組下,大盤預測績效為1.22%、電子類股為1.11%以及金融保險類股為1.1%皆優於其它模式。
英文摘要 This paper is to study using large data to analysis and find a tendency maybe lose some meanings in forecasting tendency of stock market price index. For the reason, different with traditional method, using small data to find the observations for forecasting Friday by Trend and potency function(CLTM-S) .
Experimental result reveal in that the best observations is four days in the same week in forecasting the stock price of Friday behavior. And using these observations to build establish prediction model with statistics method (REGRESSION)、ARIMA、Neural network(BPNN) 、radial base function(RBF)、 Support vector regression(SVR) and CLTM-S CLTM. The result reveal BPNN is better than other tradition method and the mean absolute percentages error are 1.22%、1.11% and 1.10% in Taiwan weighting stock price index Electric index Finance index.
論文目次 摘要.................................................I
Abstract ............................................II
誌謝.................................................III
目錄.................................................IV
表目錄...............................................VI
圖目錄...............................................VII
第一章 緒論..........................................1
1.1 研究動機與背景..............................1
1.2 研究目的....................................2
1.3 研究流程....................................3
第二章 文獻探討....................................4
2.1 價格預測.........................................4
2.1.1 預測模型.......................................4
2.1.2 混合式預測模型.................................5
2.2 理論基礎.........................................6
2.2.1 股票價格形成理論...............................6
2.2.2 分析股市價格變動之方法.........................7
2.3 小樣本相關概念 ...................................10
2.3.1 小樣本的相關問題................................10
2.3.2 小樣本資料學習..................................10
2.4 支撐向量機........................................11
2.4.1 最大邊際分類器..................................12
2.4.2 線性不可分割問題................................16
2.4.3 支撐向量迴歸 ....................................17
2.4.4 核心函數........................................19
第三章 研究方法.......................................21
3.1 研究方法架構......................................21
3.2 研究方法描述......................................22
3.2.1 資料收集與最適觀察數............................22
3.2.2 研究資料部分....................................24
3.2.3 星期五行為預測..................................24
第四章 實證分析.............................. ........25
4.1 資料分析..........................................25
4.2 模型評估及比較....................................29
第五章 結論與建議...................................31
5.1 研究結論..........................................31
5.2 後續研究建議......................................33
參考文獻 .............................................34
參考文獻 1.Bollerslev, T. (1990). MODELING THE COHERENCE IN SHORT-RUN NOMINAL EXCHANGE-RATES - A MULTIVARIATE GENERALIZED ARCH MODEL. Review of Economics and Statistics, 72(3), 498-505.
2.Cortes, C., & Vapnik, V. (1995). SUPPORT-VECTOR NETWORKS. Machine Learning, 20(3), 273-297.
3.Harman, G., Parameswaran, P., and Witt, Shares, bonds or cash? Asset Allocation in the new economy using CART, SALOMON SMITH BARNEDY Quantitative Analysis.
4.Harville, D. A. (1977). MAXIMUM LIKELIHOOD APPROACHES TO VARIANCE COMPONENT ESTIMATION AND TO RELATED PROBLEMS. Journal of the American Statistical Association, 72(358), 320-338.
5.Hsieh, D. A. (1989). MODELING HETEROSCEDASTICITY IN DAILY FOREIGN-EXCHANGE RATES. Journal of Business & Economic Statistics, 7(3), 307-317.
6.Huang, C. F., & Moraga, C. (2004). A diffusion-neural-network for learning from small samples. International Journal of Approximate Reasoning, 35(2), 137-161.
7.Huang, W., Lai, K. K., Nakamori, Y., Wang, S. Y., & Yu, L. (2007). Neural networks in finance and economics forecasting. International Journal of Information Technology & Decision Making, 6(1), 113-140.
8.Huang, W., Nakamori, Y., & Wang, S. Y. (2005). Forecasting stock market movement direction with support vector machine. Computers & Operations Research, 32(10), 2513-2522.
9.Jennrich, R. I., & Schluchter, M. D. (1986). UNBALANCED REPEATED-MEASURES MODELS WITH STRUCTURED COVARIANCE MATRICES. Biometrics, 42(4), 805-820.
10.Kim, K. J. (2003). Financial time series forecasting using support vector machines. Neurocomputing, 55(1-2), 307-319.
11.Kohzadi, N., Boyd, M. S., Kermanshahi, B., & Kaastra, I. (1996). A comparison of artificial neural network and time series models for forecasting commodity prices. Neurocomputing, 10(2), 169-181.
12.Kwon, Y. K., & Moon, B. R. (2007). A hybrid neurogenetic approach for stock forecasting. Ieee Transactions on Neural Networks, 18(3), 851-864.
13.Laird, N. M., & Ware, J. H. (1982). RANDOM-EFFECTS MODELS FOR LONGITUDINAL DATA. Biometrics, 38(4), 963-974.
14.Li, D. C., Chen, L. S., & Lin, Y. S. (2003). Using Functional Virtual Population as assistance to learn scheduling knowledge in dynamic manufacturing environments. International Journal of Production Research, 41(17), 4011-4024.
15.Li, D. C., & Lin, Y. S. (2008). Learning management knowledge for manufacturing systems in the early stages using time series data. European Journal of Operational Research, 184(1), 169-184.
16.Li, D. C., & Lin, Y. S. (2008). Learning management knowledge for manufacturing systems in the early stages using time series data. European Journal of Operational Research, 184(1), 169-184.
17.Li, T., Zhang, C. L., & Ogihara, M. (2004). A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics, 20(15), 2429-2437.
18.Maasoumi, E., & Racine, J. (2002). Entropy and predictability of stock market returns. Journal of Econometrics, 107(1-2), 291-312.
19.Niyogi, P., Girosi, F., & Poggio, T. (1998). Incorporating prior information in machine learning by creating virtual examples. Proceedings of the Ieee, 86(11), 2196-2209.
20.Pai, P. F., & Lin, C. S. (2005). A hybrid ARIMA and support vector machines model in stock price forecasting. Omega-International Journal of Management Science, 33(6), 497-505.
21.Roh, T. H. (2007). Forecasting the volatility of stock price index. Expert Systems with Applications, 33(4), 916-922.
22.Sorensen, E. H., Miller, K. L., & Ooi, C. K. (2000). The decision tree approach to stock selection - An evolving tree model performs the best. Journal of Portfolio Management, 27(1), 42-+.
23.Sun, Y. F., Liang, Y. C., Zhang, W. L., Lee, H. P., Lin, W. Z., & Cao, L. J. (2005). Optimal partition algorithm of the RBF neural network and its application to financial time series forecasting. Neural Computing & Applications, 14(1), 36-44.
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