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

TL;DR

This paper introduces an evolutionary algorithm to automatically design RNNs for stock return prediction, leading to improved trading strategies that outperform major indices in different market conditions.

Contribution

It presents a novel application of the EXAMM algorithm to evolve RNN architectures specifically for stock return forecasting and portfolio trading.

Findings

Evolved RNNs outperform DJI and S&P 500 indices in 2022 and 2023.

The approach achieves higher returns using a simple daily long-short trading strategy.

Evolving RNNs independently for each stock enhances prediction accuracy.

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).