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研究生:張婷媛
研究生(外文):Chang, Ting-Yuan
論文名稱:利用集成學習及離散小波轉換進行股票預測
論文名稱(外文):Stock Prediction Using Ensemble Learning and Discrete Wavelet Transform
指導教授:黃泓智黃泓智引用關係
指導教授(外文):Huang, Hong-Chih
口試委員:黃泓智楊曉文柯士文
口試委員(外文):Huang, Hong-ChihYang, Sharon S.Ke, Shih-Wen
口試日期:2022-07-04
學位類別:碩士
校院名稱:國立政治大學
系所名稱:風險管理與保險學系
學門:商業及管理學門
學類:風險管理學類
論文種類:學術論文
論文出版年:2022
畢業學年度:110
語文別:中文
論文頁數:51
中文關鍵詞:股市漲跌集成學習小波轉換輕量化的梯度提升機決策樹極限梯度提升多層感知器支持向量機
外文關鍵詞:Stock predictionEnsemble learningDiscrete wavelet transformDecision treeXGBoostLightGBMSVMMLP
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本研究使用台灣上市公司股票之股價資訊、技術指標以及總體經濟指標以集成學習概念進行台灣股市個股漲跌預測、建立最適投資組合。本論文使用五個不同的機器學習模型:決策樹(Decision Tree)、極限梯度提升模型(XGBoost)、輕量化的梯度提升機(LightGBM)、支持向量機(SVM)以及多層感知器(MLP)進行個股的漲跌預測。為了使模型訓練結果更好,本研究利用集成學習(Ensemble Learning)的堆疊技巧(Stacking),將五個機器學習模型的預測結果整合並進行最終的漲跌預測,選出上漲機率較高的股票,接著組成股票投資清單。另外,本研究第二階段使用離散小波轉換(Discrete Wavelet Transform)去除股票收盤價之雜訊,並當作新的特徵加入模型,重新進行預測。實證結果發現,使用多種模型進行集成學習所建立的投資組合能夠獲得更好的績效,且加入小波轉換技術也有效提升模型的整體績效。
This research uses the stock price information, technical indicators, and macroeconomic indicators to predict the trend of individual stocks in the Taiwan stock market with ensemble learning and establish the optimal investment portfolio. This paper uses five different machine learning models: decision tree, XGBoost, LightGBM, SVM, and MLP. To make the model training results better, this study uses the stacking technique of ensemble learning to integrate the prediction results of five machine learning models and selects the stocks with high rising probability, then make up a stock investment list. In addition, in the second stage of this study, Discrete wavelet transform is used to remove the noise of stock closing price, and it is added to the model as a new feature. The empirical results show that the investment portfolio established using multiple models for ensemble learning can achieve better performance, and adding wavelet transform technology can also effectively improve the model's overall performance.
第一章 緒論 1
第一節 研究動機 1
第二節 研究目的 2
第三節 研究流程 3
第二章 文獻回顧 5
第一節 離散小波轉換文獻回顧 5
第二節 選用指標與股價預測文獻回顧 6
第三節 股價預測與機器學習模型文獻回顧 7
第四節 集成學習用於投資市場預測文獻回顧 8
第三章 研究方法 10
第一節 研究架構 10
第二節 指標變數選擇 12
第三節 離散小波轉換 16
第四節 資料預處理 17
第五節 機器學習模型 20
第六節 集成學習選股 23
第七節 績效指標說明 27
第四章 實證結果 30
第一節 離散小波轉換 30
第二節 集成學習 39
第三節 最終模型 42
第五章 結論與建議 49
參考文獻 50
1.Chang, T. S. (2011). A comparative study of artificial neural networks, and decision trees for digital game content stocks price prediction. Expert systems with applications, 38(12), 14846-14851.
2.Chen, Y., Liu, K., Xie, Y., & Hu, M. (2020). Financial trading strategy system based on machine learning. Mathematical Problems in Engineering, 2020.
3.Chhajer, P., Shah, M., & Kshirsagar, A. (2022). The applications of artificial neural networks, support vector machines, and long–short term memory for stock market prediction. Decision Analytics Journal, 2, 100015.
4.Hiransha, M., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2018). NSE stock market prediction using deep-learning models. Procedia computer science, 132, 1351-1362.
5.Hongjoong, K. I. M. (2021). MEAN-VARIANCE PORTFOLIO
OPTIMIZATION WITH STOCK RETURN PREDICTION USING XGBOOST. Economic Computation & Economic Cybernetics Studies & Research, 55(4).
6.Jiang, M., Liu, J., Zhang, L., & Liu, C. (2020). An improved Stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms. Physica A: Statistical Mechanics and its Applications, 541, 122272.
7.Liang, X., Ge, Z., Sun, L., He, M., & Chen, H. (2019). LSTM with wavelet transform based data preprocessing for stock price prediction. Mathematical Problems in Engineering, 2019.
8.Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020). A comprehensive evaluation of ensemble learning for stock-market prediction. Journal of Big Data, 7(1), 1-40.
9.Padhi, D. K., Padhy, N., Bhoi, A. K., Shafi, J., & Ijaz, M. F. (2021). A Fusion Framework for Forecasting Financial Market Direction Using Enhanced Ensemble Models and Technical Indicators. Mathematics, 9(21), 2646.
10.Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert systems with applications, 42(1), 259-268.
11.Shynkevich, Y., McGinnity, T. M., Coleman, S. A., Belatreche, A., & Li, Y. (2017). Forecasting price movements using technical indicators: Investigating the impact of varying input window length. Neurocomputing, 264, 71-88.
12.Tang, Q., Shi, R., Fan, T., Ma, Y., & Huang, J. (2021). Prediction of Financial Time Series Based on LSTM Using Wavelet Transform and Singular Spectrum Analysis. Mathematical Problems in Engineering, 2021.
13.Weng, B., Martinez, W., Tsai, Y. T., Li, C., Lu, L., Barth, J. R., & Megahed, F. M.(2018). Macroeconomic indicators alone can predict the monthly closing price of major US indices: Insights from artificial intelligence, time-series analysis and hybrid models. Applied Soft Computing, 71, 685-697.
14.Wu, D., Wang, X., & Wu, S. (2021). A hybrid method based on extreme learning machine and wavelet transform denoising for stock prediction. Entropy, 23(4), 440.
15.Ye, Z., Wu, Y., Chen, H., Pan, Y., & Jiang, Q. (2022). A Stacking Ensemble Deep Learning Model for Bitcoin Price Prediction Using Twitter Comments on Bitcoin. Mathematics, 10(8), 1307.
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