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    題名: 以機器學習模型建構多空投資組合策略
    Constructing Long-Short Investment Portfolio Strategies Using Machine Learning Models
    作者: 黃紀豪
    Huang, Ji-Hao
    貢獻者: 鍾令德
    Chung, Ling-Tak
    黃紀豪
    Huang, Ji-Hao
    關鍵詞: 機器學習
    股價報酬預測
    投資組合選擇
    Machine Learning
    Return Predictability
    Portfolio Choice
    日期: 2024
    上傳時間: 2024-08-05 11:57:07 (UTC+8)
    摘要: 本研究比較 18 個機器學習模型預測台灣股市上市公司報酬的能力，並回測以 10 種不同多空比例及加權比重建構之 180 種機器學習投資策略。結果顯示 11 種機器學習模型能有效預測個股超額報酬，神經網路模型、決策樹模型預測表現較佳，其中 XGBoost 模型建構之多空投資組合策略績效最為優異，能在樣本外期間獲得 3.58% 之月均報酬，並達到 3.56 之年化夏普比率，顯示機器學習模型確實能捕捉股票特徵與下期報酬的非線性關係，產生有價值之交易訊號，進而為投資人帶來顯著的報酬。另外，本研究發現 10-1 分位多空策略相較全市場多空策略能有效提升夏普比率，而 130/30 策略雖然能創造比淨零投資組合策略更高的報酬，卻因波動性更高而無法有效提升夏普比率。
    This study evaluates 18 machine learning models in predicting stock returns of listed companies in Taiwan. Through 10 combinations of long-short ratios and weighting schemes, I backtest 180 investment strategies based on machine learning predictions. The results show that 11 machine learning models can effectively predict individual stock excess returns. Neural network models and decision tree models exhibit better predictive performance, with the XGBoost model constructing the best performing long-short investment portfolio strategy. This strategy achieves an average monthly return of 3.58\% and an annualized Sharpe ratio of 3.56 during the out-of-sample period. Machine learning models can capture non-linear relationships between stock characteristics and future returns, generating valuable trading signals that bring significant Alphas for investors.
    Furthermore, this study finds that the 10-1 long-short strategy effectively improves the Sharpe ratio compared to full market long-short strategies. Although the 130/30 strategy can generate higher returns than net-zero investment strategies, it fails to effectively improve the Sharpe ratio due to its higher volatility.
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    蔣佳穎, 2022, 以財務指標預測台股橫斷面期望報酬 ，未出版之博 (碩) 士論文， 國立政治大學，國際經營與貿易學系，台北市.
    描述: 碩士
    國立政治大學
    國際經營與貿易學系
    111351024
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0111351024
    資料類型: thesis
    顯示於類別:[國際經營與貿易學系 ] 學位論文

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