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A Bayesian-based classification framework for financial time series trend prediction

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Abstract

Financial time series have been extensively studied within the past decades; however, the advent of machine learning and deep neural networks opened new horizons to apply supercomputing techniques to extract more insights from the underlying patterns of price data. This paper presents a tri-state labeling approach to classify the underlying patterns in price data into up, down and no-action classes. The introduction of a no-action state in our novel approach alleviates the burden of denoising the dataset as a preprocessing task. The performance of our labeling algorithm is experimented with using machine learning and deep learning models. The framework is augmented by applying the Bayesian optimization technique for the selection of the best tuning values of the hyperparameters. The price trend prediction module generates the required trading signals. The results show that the average annualized Sharpe ratio as the trading performance metric is about 2.823, indicating the framework produces excellent cumulative returns.

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Data availability

The authors confirm that the data analyzed in this study are openly available in yahoo finance at finance.yahoo.com and are included in supplementary information files. The authors also confirm that the data generated during the study are included in this published article and can be available upon request from the authors.

Notes

  1. Efficient market hypothesis.

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Authors and Affiliations

  1. Computer Engineering Department, Sharif University of Technology, Tehran, Iran

    Arsalan Dezhkam, Mohammad Taghi Manzuri, Ahmad Aghapour, Afshin Karimi & Ali Rabiee

  2. Department of Computing and Software, McMaster University, Hamilton, ON, Canada

    Shervin Manzuri Shalmani

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Correspondence to Mohammad Taghi Manzuri.

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Dezhkam, A., Manzuri, M.T., Aghapour, A. et al. A Bayesian-based classification framework for financial time series trend prediction. J Supercomput 79, 4622–4659 (2023). https://doi.org/10.1007/s11227-022-04834-4

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