政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/124742

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/124742

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政大典藏 > College of Commerce > Department of Money and Banking > Theses > Item 140.119/124742

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/124742

Title:	基於支持向量回歸的選股模型實證研究 —以台股市場爲例 An Empirical Study of Stock Selection Model Based on Support Vector Regression - Taiwan Stock Market as An Example
Authors:	卓越 Zhuo, Yue
Contributors:	廖四郎 Liao, Szu-Lang 卓越 Zhuo, Yue
Keywords:	機器學習支持向量回歸支持向量機量化交易選股模型 Machine learning Support vector regression Support vector machine Quant trade Stock selection model
Date:	2019
Issue Date:	2019-08-07 16:13:19 (UTC+8)
Abstract:	當今世界的發展下，隨著信息技術的快速發展，計算機進行資料處理越來越流行。近幾年來機器學習技術的火爆，更加催生了將機器學習用在金融、經濟等領域的熱潮。本文選擇了機器學習領域的成熟算法支持向量機的分支——支持向量回歸，當作基礎的算法，搭配以網格搜索、主成分分析法對模型進行參數尋優和對資料進行降維處理。選取2009年第一季度到2018年第四季度的財報資料共170個指標和收盤價資料，利用2009年到2015年的資料對上述的支持向量回歸模型進行訓練，再利用2016年到2018年的資料進行回測。回測結果表明，支援向量回歸模型對於台股市場有一定的預測能力，當使用主成分分析法提取特徵個數為十個的時候，整體模型的報酬率表現最好，當特徵個數增加或者減少時，一定程度上可以增加模型的擬合程度，但是會增加樣本內和樣本外的R^2的差，導致模型的一般化能力減弱。 Since the great development of today`s world, computer data processing has become more and more popular with the rapid development of information technology. In recent years, the popularity of Machine Learning technology has spawned a boom in applications of finance and economy. This paper chooses the Support Vector Regression (SVR) as the basic algorithm, a branch of the mature Machine Learning approach, Support Vector Mchine (SVM). With the application of grid search and principal component analysis method, the parameters of the model are optimized and the data dimension is reduced. 170 indicators and close price data from Q1 2009 to Q4 2018 are selected, when data from 2009 to 2015 is used to train the SVR model, and data from 2016 to 2018 is used for back testing. The back test result shows that SVR model has predictive ability for the Taiwan stock market. When ten features are extracted by principal component analysis, the return of overall model reaches the best. When the number of features increases or reduces, the fitting degree of the model can be increased to some extent, but the difference of R^2 between sample and out of sample increases, resulting in a weakness of the model generalization ability.
Reference:	全林，姜秀珍，趙俊和，汪東，（2009）。基於SVM分類算法的選股研究。上海交通大學學報，43（9）：1412-1416 。李航，（2012）。統計學習方法。北京：清華大學出版社。周漸，（2017）。基於SVM算法的多因子選股模型實證研究。未出版之碩士論文，浙江工商大學，金融，杭州。高雯，（2018）。基於支持向量機參數優化算法的股票智能投顧策略研究。未出版之碩士論文，上海師範大學，金融，上海。周志華，（2016）。機器學習。北京：清華大學出版社。張玉川，張作泉，黃珍，（2008）。支持向量機在選擇優質股票中的應用。統計與決策，（4）：163-165。趙佳藝，（2019）。量化投資發展及我國現狀分析。現代商貿工業，2019（8）：116-117 。謝東東，（2018）。量化投資的特點、策略和發展探討。時代金融，709：245、252 。蘇治，傅曉媛，（2013）。核主成分遺傳算法與SVR選股模型改進。統計研究，30（5）：54-62 。魏妹金（2015）。支持向量机多因子选股模型。未出版之碩士論文，華僑大學，統計學系，泉州。 Alex J.Smola, Bernhard Schölkopf, 2004. A tutorial on Support Vector Regression, Statistics and Computing, 14(3):199-222. A.Fan, M.Palaniswami, 2001. Stock Selection using Support Vector Machines. NeuralNetworks, 2001. Proceedings. IJCNN `01. International Joint Conference on. IEEE,2001:1793 - 1798. Chien-Feng Huang, 2012. A hybrid Stock Selection Model using Genetic Algorithms and Support Vector Regression. Applied Soft Computing,12(2):807-818. Chi-Yuan Yeh, Chi-Wei Huang, Shie-Jue Lee, 2011. A Multiple-Kernel Support Vector Regression approach for Stock Market price forecasting. Expert Systems with Applications,38(3):2177-2186. Dennis Olson and Charles Mossman, 2003. Neural Netword forecasts of Canadian Stock Returns using accounting ratios. International Journal of Forecasting, 19(3):453-465 F.S.Wong, P.Z.Wang, T.H.Goh, B.K.Quek, 1992. Fuzzy Neural Systems for Stock Selection. Financial Analysts Journal, 48(1):47-52. Huanhuan Yu, Rongda Chen, Guoping Zhang,2014. A SVM Stock Selection Model whithin PCA, Procedia Computer Science, 31:406-412. R.J.Kuo, C.H.Chen, Y.C.Hwang, 2001. An Intelligent Stock Trading Decision Support System through Integration of Genetic Algorithm Based Fuzzy Neural Network and Artificial Neural Network. Fuzzy Sets and Systems, 118(1):21-45 Tong-Seng Quah and Bobby Srinivasan, 1999. Improving returns on Stock Investment through Neural Network Selection. Expert Systems with Applications, 17(4):295-301
Description:	碩士國立政治大學金融學系 106352044
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0106352044
Data Type:	thesis
DOI:	10.6814/NCCU201900250
Appears in Collections:	[Department of Money and Banking] Theses

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