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    题名: 使用機器學習選股之投資績效研究—台灣股票市場之實證
    A Study on the Investment Performance of Machine Learning-Based Stock Selection — Empirical Evidence from the Taiwan Stock Market
    作者: 許雅筑
    Hsu, Ya-Chu
    贡献者: 吳啟銘
    許雅筑
    Hsu, Ya-Chu
    关键词: 機器學習
    選股能力
    股價預測
    隨機森林模型
    五分位數投資組合策略
    投資組合績效
    Machine Learning
    Stock Selection Ability
    Stock Price Prediction
    Random Forest Model
    Quintile Investment Portfolio Strategy
    Portfolio Performance
    日期: 2024
    上传时间: 2024-09-04 15:25:43 (UTC+8)
    摘要: 近年來機器學習已廣泛應用於金融領域，如何使用機器學習來幫助分析師捕捉隱含的訊息，已是許多學術及實務上的發展方向。因此，本文主要研究以人工智慧的機器學習法，應用於台灣股票市場的交易。本文運用個股過去的股票報酬率、交易周轉率與月營收年增率，應用隨機森林演算法預測未來可能上漲的股票作為投資標的，再搭配模型的特徵重要性來建構五分位數投資組合，並分為等權重與市值加權來進行分析。

    實證顯示，依照排名的升高，投資組合的報酬跟風險也越大，風險調整後均有較佳的報酬，且可建構的年平均報酬率均高於市場投資組合的報酬率。透過三因子模型分析顯示，模型傾向於選擇投資小型股及成長股，而在排名最高的兩組等權重投資組合，均能獲得顯著正的超額報酬。

    本研究實證顯示，機器學習對於投資策略與投資組合建構具有正面的影響，能夠幫助識別影響股價的重要因子，尋找隱含的訊息。這項研究能幫助投資人探索如何利用機器學習建構投資策略與投資組合。
    In recent years, machine learning has been widely applied in the financial sector. How to use machine learning to help analysts capture implicit information has become a major direction for both academic and practical development. This study primarily investigates the application of machine learning, specifically artificial intelligence methods, in trading on the Taiwan stock market. By utilizing historical stock returns, turnover rates, and monthly revenue growth rates, this paper employs the random forest algorithm to predict stocks that are likely to rise in the future as investment targets. It further constructs quintile investment portfolios based on feature importance from the model, and analyzes them under both equal-weighted and market-cap-weighted schemes.

    Empirical results show that as the portfolio ranking increases, both returns and risks also increase. Adjusted for risk, the portfolios exhibit superior returns, with annual average returns outperforming the market portfolio. Analysis using the three-factor model indicates that the model tends to select small-cap and growth stocks. The top two quintile equal-weighted portfolios achieve significant positive excess returns.

    This study empirically demonstrates that machine learning has a positive impact on investment strategies and portfolio construction. It effectively aids in identifying key factors influencing stock prices and uncovering hidden information. This research can assist investors in exploring how to utilize machine learning to develop investment strategies and portfolios.
    參考文獻: 一、中文部分
    田鈜元 (2022)。以隨機森林法建構投資組合績效—以台灣股票市場為例。清華大學財務金融在職專班碩士論文。
    沈佩璉 (2022)。機器學習於投資組合報酬率之影響。臺灣大學資訊管理學系碩士論文。
    林生華 (2020)。機器學習因子擇時模型結合 Black-Litterman 模型之投資組合建構。政治大學金融學系碩士論文。
    孫嘉蔚 (2021) 。運用機器學習模型分析影響公司風險的 ESG 因子：以台灣市場為例。政治大學金融學系碩士論文。
    劉俞含 (2018)。XGBoost 模型、隨機森林模型、彈性網模型 於股價指數趨勢之預測—以台灣、日本、美國為例。中山大學財務管理學系碩士論文。
    鄭仁杰 (2018)。利用隨機森林模型建構台灣指數期貨交易策略。政治大學金融學系碩士論文。
    簡清源 (2022)。機器學習於投資組合之表現—隨機森林法的應用。元智大學管理學院博士班學位論文。
    二、英文部分
    Ahmed, S., Alshater, M. M., El Ammari, A., & Hammami, H. (2022). Artificial intelligence and machine learning in finance: A bibliometric review. Research in International Business and Finance, 61, 101646.
    Ballings, M., Van den Poel, D., Hespeels, N., & Gryp, R. (2015). Evaluating multiple classifiers for stock price direction prediction. Expert systems with Applications, 42(20), 7046-7056.
    Bartram, S. M., Branke, J., De Rossi, G., & Motahari, M. (2021). Machine learning for active portfolio management. Journal of Financial Data Science, 3(3), 9-30.
    Breitung, C. (2023). Automated stock picking using random forests. Journal of Empirical Finance, 72, 532-556.
    Bustos, O., & Pomares-Quimbaya, A. (2020). Stock market movement forecast: A systematic review. Expert Systems with Applications, 156, 113464.
    Cao, K., & You, H. (2024). Fundamental Analysis via Machine Learning. Financial Analysts Journal, 80(2), 74-98.
    Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of financial economics, 33(1), 3-56.
    Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273.
    Huang, Y., Capretz, L. F., & Ho, D. (2021, December). Machine learning for stock prediction based on fundamental analysis. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 01-10). IEEE.
    Kumar, D., Sarangi, P. K., & Verma, R. (2022). A systematic review of stock market prediction using machine learning and statistical techniques. Materials Today: Proceedings, 49, 3187-3191.
    Kumbure, M. M., Lohrmann, C., Luukka, P., & Porras, J. (2022). Machine learning techniques and data for stock market forecasting: A literature review. Expert Systems with Applications, 197, 116659.
    Leippold, M., Wang, Q., & Zhou, W. (2022). Machine learning in the Chinese stock market. Journal of Financial Economics, 145(2), 64-82.
    Min, L., Dong, J., Liu, J., & Gong, X. (2021). Robust mean-risk portfolio optimization using machine learning-based trade-off parameter. Applied Soft Computing, 113, 107948.
    Pinelis, M., & Ruppert, D. (2022). Machine learning portfolio allocation. The Journal of Finance and Data Science, 8, 35-54.
    Plachel, L. (2019). A unified model for regularized and robust portfolio optimization. Journal of Economic Dynamics and Control, 109, 103779.
    Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The journal of finance, 19(3), 425-442.
    Tan, Z., Yan, Z., & Zhu, G. (2019). Stock selection with random forest: An exploitation of excess return in the Chinese stock market. Heliyon, 5(8).
    Vuong, P. H., Phu, L. H., Van Nguyen, T. H., Duy, L. N., Bao, P. T., & Trinh, T. D. (2024). A bibliometric literature review of stock price forecasting: from statistical model to deep learning approach. Science Progress, 107(1), 00368504241236557.
    Wolff, D., & Echterling, F. (2024). Stock picking with machine learning. Journal of Forecasting, 43(1), 81-102.
    Yuan, X., Yuan, J., Jiang, T., & Ain, Q. U. (2020). Integrated long-term stock selection models based on feature selection and machine learning algorithms for China stock market. IEEE Access, 8, 22672-22685.
    描述: 碩士
    國立政治大學
    財務管理學系
    107357012
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0107357012
    数据类型: thesis
    显示于类别:[財務管理學系] 學位論文

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