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    题名: 基於股票分類與強勢行業指數的機器學習資產配置策略
    Machine Learning-Based Asset Allocation Strategy Incorporating Stock Classification and Strong Industry Index
    作者: 張彤
    Chang, Tung
    贡献者: 黃泓智
    曾毓英
    Huang, Hong-Chih
    Tzeng, Yu-Ying
    張彤
    Chang, Tung
    关键词: 圍繞中心點分類
    動態時間扭曲
    機器學習
    台灣行業別指數
    資產配置
    股票分類
    Partitioning Around Medoids
    Dynamic Time Warping
    Machine Learning
    Taiwan Industry Index
    Asset Allocation
    Stock Classification
    日期: 2023
    上传时间: 2023-08-02 14:24:23 (UTC+8)
    摘要: 本文使用了14年的台灣上市公司股價資料，計算出技術指標、訊號指標，並採用因子篩選技術進行資料預處理。接著，使用動態時間扭曲（Dynamic Time Warping, DTW）方法來衡量股票間的距離，並結合圍繞中心點分類（Partitioning Around Medoids, PAM）算法進行股票相似度分群。在股票漲跌趨勢預測方面，本論文採用了極限梯度提升模型（eXtreme Gradient Boosting, XGBoost）、多層感知器(Multilayer Perceptron, MLP)、支持向量回歸(Support Vector Regression, SVR)作為機器學習器，並使用集成學習方法。利用集成模型預測台灣股市個股的漲跌趨勢，並選出上漲趨勢較高的股票組成投資清單。在資產配置方面，本文開發了兩種方法去對Markowitz的切線投資組合(Tangency Portfolio)、最小風險模型(Global Minimum Risk)以及等權重資產配置框架進行資產配置權重的調整，過程中使用台灣33種行業別指數搭配技術指標與總經指標進行訓練與預測，挑選當月的強勢行業別指數，並找出與該強勢行業別指數具有最高相似性的股票群或單一個股，並對其進行權重加乘調整，進行回測分析。最後，本文使用統計評估指標對預測模型的性能進行評估。
    結果顯示在使用股票分類與集成學習後，可使回測績效提升，又本文開發的資產配置方法比起調整前的切線投資組合、最小風險模型以及等權重資產配置框架獲得了最佳的績效，也發現在訓練並預測出強勢行業別工業股票指數時搭配非即時性總經指標可以有更好的預測能力。
    In this paper, 14 years of stock price data of listed companies in Taiwan are used to calculate technical indicators and signal indicators, and the data are preprocessed using factor filtering techniques. Then, Dynamic Time Warping method is used to measure the distance between stocks, and Partitioning Around Medoids is combined to perform stock similarity clustering. For stock trend prediction, this paper adopts XGBoost, MLP, SVR as machine learners and uses integrated learning methods. The integrated model is used to predict the upward and downward trends of individual stocks in the Taiwan stock market and to select stocks with higher upward trends to form an investment list. In terms of asset allocation, two methods are developed to adjust the asset allocation weights of Markowitz`s Tangency Portfolio, Global Minimum Risk model, and Equal Weighted Asset Allocation framework, using 33 sectoral indices in Taiwan with technical indicators and aggregate indicators. In the process of training and forecasting, the strongest sector indices of the month are selected, and the stock groups or single stocks with the highest similarity to the strongest sector indices are identified, and their weights are adjusted for backtest analysis. Finally, the paper evaluates the performance of the forecasting model using statistical evaluation metrics, and shows that the use of stock classification and integrated learning leads to improved backtesting performance, and that the asset allocation approach developed in this paper achieves the best performance compared to the pre-tuning asset allocation framework.
    參考文獻: 參考文獻
    1. 林文修, & 陳仕哲. (2015). 遺傳演算法在台灣股價趨勢轉折點與波動訊號捕捉之應用. 輔仁管理評論, 22(3), 1-33.

    2. 張婷媛. (2022). 利用集成學習及離散小波轉換進行股票預測.

    3. Alsubaie, Y., El Hindi, K., & Alsalman, H. (2019). Cost-sensitive prediction of stock price direction: Selection of technical indicators. IEEE Access, 7, 146876-146892.

    4. Aqsari, H. W., Prastyo, D. D., & Rahayu, S. P. (2022, December). Clustering Stock Prices of Financial Sector Using K-Means Clustering With Dynamic Time Warping. In 2022 6th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE) (pp. 503-507). IEEE.

    5. Aithal, P. K., Geetha, M., Dinesh, U., Savitha, B., & Menon, P. (2023). Real-Time Portfolio Management System Utilizing Machine Learning Techniques. IEEE Access, 11, 32595-32608.

    6. Clarke, R., De Silva, H., & Thorley, S. (2006). Minimum-variance portfolios in the US equity market. Journal of Portfolio Management, 33(1), 10.

    7. Ding, X., Hao, K., Cai, X., Tang, X. S., Chen, L., & Zhang, H. (2020). A novel similarity measurement and clustering framework for time series based on convolution neural networks. Ieee Access, 8, 173158-173168.

    8. Li, M., Zhu, Y., Shen, Y., & Angelova, M. (2023). Clustering-enhanced stock price prediction using deep learning. World Wide Web, 26(1), 207-232.

    9. Markowitz, H. (1952). The utility of wealth. Journal of political Economy, 60(2),151-158.
    10. Manjoro, W. S., Dhakar, M., & Chaurasia, B. K. (2016, April). Operational analysis of k-medoids and k-means algorithms on noisy data. In 2016 International conference on communication and signal processing (ICCSP) (pp. 1500-1505). IEEE.

    11. Medarhri, I., Hosni, M., Nouisser, N., Chakroun, F., & Najib, K. (2022, October). Predicting Stock Market Price Movement using Machine Learning Techniques. In 2022 8th International Conference on Optimization and Applications (ICOA) (pp. 1-5). IEEE.

    12. Sinatrya, N. S., & Wardhani, L. K. (2018, August). Analysis of K-Means and K-Medoids’s Performance Using Big Data Technology. In 2018 6th International Conference on Cyber and IT Service Management (CITSM) (pp. 1-5). IEEE.

    13. Zhang, X., & Chen, W. (2019, July). Stock Selection Based on Extreme Gradient Boosting. In 2019 Chinese Control Conference (CCC) (pp. 8926-8931). IEEE.
    描述: 碩士
    國立政治大學
    風險管理與保險學系
    110358028
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0110358028
    数据类型: thesis
    显示于类别:[風險管理與保險學系] 學位論文

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