Year: 2024
Type: article
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.
Source: Computational Economics
Authors: Attila Rácz, Norbert Fogarasi
Institution: Budapest University of Technology and Economics
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,190
OpenAlex ID: W4404651434