Neuroevolution Neural Architecture Search for Evolving RNNs in Stock Return Prediction and Portfolio Trading

Zimeng Lyu, Amulya Saxena, Rohaan Nadeem, Hao Zhang, Travis Desell
10/22/2024
q-fin.PMcs.AIcs.CE

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Abstract

Stock return forecasting is a major component of numerous finance applications. Predicted stock returns can be incorporated into portfolio trading algorithms to make informed buy or sell decisions which can optimize returns. In such portfolio trading applications, the predictive performance of a time series forecasting model is crucial. In this work, we propose the use of the Evolutionary eXploration of Augmenting Memory Models (EXAMM) algorithm to progressively evolve recurrent neural networks (RNNs) for stock return predictions. RNNs are evolved independently for each stocks and portfolio trading decisions are made based on the predicted stock returns. The portfolio used for testing consists of the 30 companies in the Dow-Jones Index (DJI) with each stock have the same weight. Results show that using these evolved RNNs and a simple daily long-short strategy can generate higher returns than both the DJI index and the S&P 500 Index for both 2022 (bear market) and 2023 (bull market).

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Cite this paper

@article{lyu2024neuroevolution,
  title  = {Neuroevolution Neural Architecture Search for Evolving RNNs in Stock Return Prediction and Portfolio Trading},
  author = {Zimeng Lyu and Amulya Saxena and Rohaan Nadeem and Hao Zhang and Travis Desell},
  year   = {2024},
  eprint = {2410.17212},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2410.17212v1}
}

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