用进化算法优化RNN预测股票收益，实现跨牛熊市超额回报
Neuroevolution Neural Architecture Search for Evolving RNNs in Stock Return Prediction and Portfolio Trading
- 通过进化算法逐代优化各股票独立的RNN模型
- 2022-2023年在道琼斯30只成分股上跑赢道指与标普500
- 适合量化金融、时序预测与自动模型设计研究者
股票收益预测是众多金融应用的核心。预测出的收益可融入投资组合交易策略，用于做出买入或卖出决策以优化收益。在此类投资组合交易应用中，时间序列预测模型的性能至关重要。本文提出使用进化探索增强记忆模型（EXAMM）算法，逐步演化用于股票收益预测的循环神经网络（RNN）。每个股票均独立演化RNN，投资组合决策基于预测的股票收益。测试所用投资组合包含道琼斯指数（DJI）的30家上市公司，每只股票权重相同。结果表明，使用这些演化出的RNN并结合简单的每日多空策略，在2022年（熊市）和2023年（牛市）均实现了高于道琼斯指数与标普500指数的收益。
原文摘要 · Abstract (English)
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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