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Effective Convergence Trading of Sparse, Mean Reverting Portfolios

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  • Attila Rácz

    (Budapest University of Technology and Economics)

  • Norbert Fogarasi

    (Budapest University of Technology and Economics)

Abstract

This paper introduces an effective convergence trading algorithm for mean reverting portfolios using Long Short Term Memory (LSTM) neural networks. Utilizing known techniques for selection of sparse, mean reverting portfolios from asset dynamics following the VAR(1) model, we introduce a 2-step technique to effectively trade the optimal portfolio. Sequence-to-sequence (Seq2Seq) LSTM architecture is implemented to make longer term prediction of future portfolio values and establish a trading range. In addition, a simple LSTM network is applied to predict very precisely one time step ahead. Combining these two constructions, a sophisticated convergence trading algorithm is implemented which produced Sharpe ratios around 1.0 on optimal portfolios selected from historical $$ S[NONSPACE] \& P500$$ S [ N O N S P A C E ] & P 500 stocks during $$2015-2022$$ 2015 - 2022 . This represents a very significant improvement compared to the previous convergence trading algorithms on the same set of portfolios by around $$141\%$$ 141 % on average.

Suggested Citation

  • Attila Rácz & Norbert Fogarasi, 2025. "Effective Convergence Trading of Sparse, Mean Reverting Portfolios," Computational Economics, Springer;Society for Computational Economics, vol. 66(3), pages 2367-2381, September.
    • Handle: RePEc:kap:compec:v:66:y:2025:i:3:d:10.1007_s10614-024-10770-7
    DOI: 10.1007/s10614-024-10770-7
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    References listed on IDEAS

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    1. Faisal Khalil & Gordon Pipa, 2022. "Is Deep-Learning and Natural Language Processing Transcending the Financial Forecasting? Investigation Through Lens of News Analytic Process," Computational Economics, Springer;Society for Computational Economics, vol. 60(1), pages 147-171, June.
    2. Shahzad Zaheer & Nadeem Anjum & Saddam Hussain & Abeer D. Algarni & Jawaid Iqbal & Sami Bourouis & Syed Sajid Ullah, 2023. "A Multi Parameter Forecasting for Stock Time Series Data Using LSTM and Deep Learning Model," Mathematics, MDPI, vol. 11(3), pages 1-24, January.
    3. Lan Wu & Xin Zang & Hongxin Zhao, 2020. "Analytic value function for a pairs trading strategy with a Lévy-driven Ornstein–Uhlenbeck process," Quantitative Finance, Taylor & Francis Journals, vol. 20(8), pages 1285-1306, August.
    4. Kenneth A. Tah, 2018. "Random walk and structural break in exchange rates," International Journal of Monetary Economics and Finance, Inderscience Enterprises Ltd, vol. 11(4), pages 384-393.
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