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Machine Learning Method for Return Direction Forecast of Exchange Traded Funds (ETFs) Using Classification and Regression Models

Author

Listed:
  • Raphael Paulo Beal Piovezan

    (Federal University of Santa Catarina
    Federal Institute of Santa Catarina)

  • Pedro Paulo Andrade Junior

    (Federal University of Santa Catarina)

  • Sérgio Luciano Ávila

    (Federal Institute of Santa Catarina)

Abstract

This article aims to propose and apply a machine learning method to analyze the direction of returns from exchange traded funds using the historical return data of its components, helping to make investment strategy decisions through a trading algorithm. In methodological terms, regression and classification models were applied, using standard data sets from five reference markets, in addition to algorithmic error metrics. In terms of research results, they were analyzed and compared to those of the Naïve forecast and the returns obtained by the buy & hold technique in the same period of time. In terms of risk and return, the models mostly performed better than the control metrics, with emphasis on the linear regression model and the classification models by logistic regression, support vector machine (using the LinearSVC model), Gaussian Naive Bayes and K-Nearest Neighbors, where in certain data sets the returns exceeded by two times and the Sharpe ratio by up to four times those of the buy & hold control model.

Suggested Citation

  • Raphael Paulo Beal Piovezan & Pedro Paulo Andrade Junior & Sérgio Luciano Ávila, 2024. "Machine Learning Method for Return Direction Forecast of Exchange Traded Funds (ETFs) Using Classification and Regression Models," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 1827-1852, May.
    • Handle: RePEc:kap:compec:v:63:y:2024:i:5:d:10.1007_s10614-023-10385-4
    DOI: 10.1007/s10614-023-10385-4
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    References listed on IDEAS

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    1. Jun Zhang & Lan Li & Wei Chen, 2021. "Predicting Stock Price Using Two-Stage Machine Learning Techniques," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1237-1261, April.
    2. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    3. Masaya Abe & Hideki Nakayama, 2018. "Deep Learning for Forecasting Stock Returns in the Cross-Section," Papers 1801.01777, arXiv.org, revised Jun 2018.
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