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Pairs-Trading Strategies with Recurrent Neural Networks Market Predictions

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • Andrea Flori

    (Department of Management, Economics and Industrial Engineering, Politecnico di Milano)

  • Daniele Regoli

    (Big Data Lab, Intesa Sanpaolo S.p.A.)

Abstract

We propose a deep learning approach to complement investor practices for identifying pairs trading opportunities. We refer to the reversal effect, empirically observed in many pairs of financial assets, consisting in the fact that temporarily market deviations are likely to correct and finally converge again, thereby generating profits. Our study proposes the use of Long Short-term Memory Networks (LSTM) to generate predictions on market performances of a large sample of stocks. We note that pairs trading strategies including such predictions can contribute to improve the performances of portfolios created according to gaps in either prices or returns.

Suggested Citation

  • Andrea Flori & Daniele Regoli, 2021. "Pairs-Trading Strategies with Recurrent Neural Networks Market Predictions," Springer Books, in: Marco Corazza & Manfred Gilli & Cira Perna & Claudio Pizzi & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 217-222, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-78965-7_32
    DOI: 10.1007/978-3-030-78965-7_32
    as

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