The Impact of Asset Cross-sectional Related Information on Portfolio Construction

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题名:	The Impact of Asset Cross-sectional Related Information on Portfolio Construction
作者:	林祐陞;Lin, Yu-Sheng
贡献者:	數學系
关键词:	面板數據;機器學習;神經網絡;投資組合構建;Panel Data;Machine Learning;Neural Networks;Portfolio Construction
日期:	2024-07-22
上传时间:	2024-10-09 17:13:43 (UTC+8)
出版者:	國立中央大學
摘要:	本文探討了橫截面數據因素之間的關係，使用了三種數據排列方式：單一、平行和面板。我們採用三種機器學習模型——梯度提升（Gradient Boosting）、XGBoost和隨機森林（Random Forest），以及長短期記憶（LSTM）和隨機效應（RE）模型來進行股市預測。基本面、技術面和籌碼分析數據被選為特徵，對數收益率則作為目標變量。根據模型預測結果，我們檢驗了投資組合的投資績效，包括傳統的Sharpe比率、選定等權重（sEW）和選定Sharpe（sSharp）方法。分析顯示，數據排列方式對投資組合績效有顯著影響。某些模型和期間中，單一排列方式表現較好，而平行和面板排列方式更適合於其他情況。梯度提升在單一數據中表現良好，但在平行數據中效果較差。隨機森林在單一和平行排列方式中均表現出穩健的性能，且平行排列方式通常優於單一排列方式。XGBoost在短期單一排列方式中表現突出，但在其他配置中效果不佳。LSTM適用於平行排列方式的短期投資，而面板排列方式則適用於月度投資期。隨機效應方法在長期投資期比短期更為有效。我們的研究強調了數據排列方式和加權方法的重要性，以提升機器學習模型在金融市場中的預測準確性和績效。;In this paper, we consider the relationships among cross-sectional data factors with three types of data arrangement: single, parallel, and panel. We use three machine learning models—Gradient Boosting, XGBoost, and Random Forest—along with LSTM and Random Effects (RE) model for stock market predictions. Fundamental, technical, and chip analysis data are selected as features, with log returns as the target variable. Based on the results of the model prediction, we examine the investment performance of portfolios, including traditional Sharp, selected equal weight (sEW) and selected Sharp (sSharp) methods. The analysis shows that the data arrangement significantly impacts the portfolio performance. The single arrangement performs better for some models and periods, whereas the parallel and panel arrangements are more suitable for others. Gradient Boosting performs well with single data but less so with parallel data. Random Forest shows robust performance across both single and parallel arrangements, with parallel generally outperforming single. XGBoost excels in the short-term single arrangement but is less effective in other configurations. LSTM is better suited for the short-term investment period with the parallel arrangement, while the panel arrangement performs better for the monthly period. The Random Effect method proves more effective for long-term investment periods than short-term ones. Our study highlights the importance of data arrangements and weighting methods to improve the predictive accuracy and performance of machine learning models in financial markets.
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