Effective Convergence Trading of Sparse, Mean Reverting Portfolios

Attila Rácz () and Norbert Fogarasi ()
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Attila Rácz: Budapest University of Technology and Economics
Norbert Fogarasi: Budapest University of Technology and Economics

Computational Economics, 2025, vol. 66, issue 3, No 19, 2367-2381

Abstract: 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.

Keywords: Mean reversion; Portfolio optimization; Sequence-to-sequence; LSTM; trading (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10614-024-10770-7

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