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).

翻译：股票收益预测是众多金融应用的核心组成部分。预测的股票收益可被整合至投资组合交易算法中，以制定基于信息的买入或卖出决策，从而优化收益。在此类投资组合交易应用中，时间序列预测模型的预测性能至关重要。本研究提出使用增强记忆模型进化探索算法，逐步演化循环神经网络以进行股票收益预测。针对每只股票独立演化RNN，并基于预测的股票收益制定投资组合交易决策。测试所用的投资组合由道琼斯工业平均指数中的30家公司构成，每只股票权重相同。结果表明，在2022年熊市和2023年牛市期间，使用这些演化RNN配合简单的每日多空策略，能够产生高于道琼斯指数和标准普尔500指数的收益。