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XGBoost for Smart Portfolio Management Based on Multi Factor Stock Selection

Published: 20 August 2023 Publication History

Abstract

Stock selection is an important part of the investing process. However, stock prices are affected by many factors and the relationships among these factors are complex. In addition to the traditional mathematical models, machine learning models have been widely used for understanding these complex relationships to predict the stock prices. This paper thus presents an ensemble decision tree learning model, XGBoost, for stock selection based on multi factors. The model has been trained with Thailand large-mid capitalization using twenty-seven factors from different categories including value, growth, momentum, liquidity, quality, dividend, and size. Backtesting was carried out to evaluate the model performance for portfolio management. The result showed that the portfolio constructed with the XGBoost strategy yielded the highest return on investment compared to the use of SET TRI Index and Equal-Weighted Index that are widely used for stock selection.

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    ICCTA '23: Proceedings of the 2023 9th International Conference on Computer Technology Applications
    May 2023
    270 pages
    ISBN:9781450399579
    DOI:10.1145/3605423
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    Published: 20 August 2023

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    Author Tags

    1. XGBoost
    2. multi-factor
    3. smart portfolio management
    4. stock selection

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