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Aiding Long-Term Investment Decisions with XGBoost Machine Learning Model

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  • Ekaterina Zolotareva

Abstract

The ability to identify stock market trends has obvious advantages for investors. Buying stock on an upward trend (as well as selling it in case of downward movement) results in profit. Accordingly, the start and end-points of the trend are the optimal points for entering and leaving the market. The research concentrates on recognizing stock market long-term upward and downward trends. The key results are obtained with the use of gradient boosting algorithms, XGBoost in particular. The raw data is represented by time series with basic stock market quotes with periods labelled by experts as Trend or Flat. The features are then obtained via various data transformations, aiming to catch implicit factors resulting in a change of stock direction. Modelling is done in two stages: stage one aims to detect endpoints of tendencies (i.e. sliding windows), stage two recognizes the tendency itself inside the window. The research addresses such issues as imbalanced datasets and contradicting labels, as well as the need for specific quality metrics to keep up with practical applicability. The model can be used to design an investment strategy though further research in feature engineering and fine calibration is required.This paper is the full text of the research, presented at the 20th International Conference on Artificial Intelligence and Soft Computing Web System (ICAISC 2021)

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  • Ekaterina Zolotareva, 2021. "Aiding Long-Term Investment Decisions with XGBoost Machine Learning Model," Papers 2104.09341, arXiv.org.
    • Handle: RePEc:arx:papers:2104.09341
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    References listed on IDEAS

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    1. Jiang, Minqi & Liu, Jiapeng & Zhang, Lu & Liu, Chunyu, 2020. "An improved Stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    2. Ben Moews & Gbenga Ibikunle, 2020. "Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning," Papers 2002.10385, arXiv.org.
    3. Moews, Ben & Ibikunle, Gbenga, 2020. "Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
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    Cited by:

    1. Goutte, Stéphane & Le, Hoang-Viet & Liu, Fei & von Mettenheim, Hans-Jörg, 2023. "Deep learning and technical analysis in cryptocurrency market," Finance Research Letters, Elsevier, vol. 54(C).
    2. Jakub Drahokoupil, 2022. "Application of the XGBoost algorithm and Bayesian optimization for the Bitcoin price prediction during the COVID-19 period," FFA Working Papers 4.006, Prague University of Economics and Business, revised 09 May 2022.
    3. Yinheng Li & Shaofei Wang & Han Ding & Hang Chen, 2023. "Large Language Models in Finance: A Survey," Papers 2311.10723, arXiv.org, revised Jul 2024.

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