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<p>Stock market prediction is a key area in financial time series analysis and quantitative finance. The emergence of deep learning models, e.g., LSTM (Long Short-Term Memory) and BiLSTM (Bidirectional LSTM), have brought breakthroughs to stock market prediction. Nevertheless, their performance is highly dependent on hyperparameter configuration (e.g., layer number and learning rate) by users. Evolutionary algorithms like GA (Genetic Algorithm) address this issue by optimizing neural network setups, but their high computational cost (mainly from fitness evaluation) limits application. In addition, LSTM variants have inherent limitations, e.g., information decay in long-sequence processing. Therefore, this work proposes GAds_saBiLSTM (GA with dynamic surrogate for self-attention enhanced BiLSTM optimization) for stock price forecasting. Specifically, BiLSTM architecture/hyperparameters are co-optimized via GA to replace manual tuning; a dynamic surrogate model is designed to cut GA computational overhead; a self-attention module is integrated into the baseline BiLSTM to mitigate information decay. On two benchmark stock datasets (CSI 300 and S&amp;P 500), the proposed method is compared against various time series prediction methods across evaluation metrics (MAE, MSE, and <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(R^2\)</EquationSource> </InlineEquation>). Results show that the evolved saBiLSTM models by the proposed method achieve the lowest prediction error and highest fitting degree (e.g., <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(R^2\)</EquationSource> </InlineEquation>=0.8240 on CSI 300 and <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(R^2\)</EquationSource> </InlineEquation>=0.9865 on S&amp;P 500). Ablation experiments confirm that the dynamic surrogate model drastically reduces computational overhead (e.g., cutting GPU training hours from 2.60 (GA-BiLSTM) to 1.03 (GAds-BiLSTM) on the CSI 300); while the integration of the self-attention module yields higher model accuracy with negligible efficiency costs.</p>

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GA-optimized BiLSTM with dynamic surrogate model for enhanced stock price prediction

  • 期刊论文

  • 发表日期：

    2026年7月21日

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  • Jiayu Liang,
  • Dongdong Ling,
  • Weisen Li

摘要

Stock market prediction is a key area in financial time series analysis and quantitative finance. The emergence of deep learning models, e.g., LSTM (Long Short-Term Memory) and BiLSTM (Bidirectional LSTM), have brought breakthroughs to stock market prediction. Nevertheless, their performance is highly dependent on hyperparameter configuration (e.g., layer number and learning rate) by users. Evolutionary algorithms like GA (Genetic Algorithm) address this issue by optimizing neural network setups, but their high computational cost (mainly from fitness evaluation) limits application. In addition, LSTM variants have inherent limitations, e.g., information decay in long-sequence processing. Therefore, this work proposes GAds_saBiLSTM (GA with dynamic surrogate for self-attention enhanced BiLSTM optimization) for stock price forecasting. Specifically, BiLSTM architecture/hyperparameters are co-optimized via GA to replace manual tuning; a dynamic surrogate model is designed to cut GA computational overhead; a self-attention module is integrated into the baseline BiLSTM to mitigate information decay. On two benchmark stock datasets (CSI 300 and S&P 500), the proposed method is compared against various time series prediction methods across evaluation metrics (MAE, MSE, and \(R^2\) ). Results show that the evolved saBiLSTM models by the proposed method achieve the lowest prediction error and highest fitting degree (e.g., \(R^2\) =0.8240 on CSI 300 and \(R^2\) =0.9865 on S&P 500). Ablation experiments confirm that the dynamic surrogate model drastically reduces computational overhead (e.g., cutting GPU training hours from 2.60 (GA-BiLSTM) to 1.03 (GAds-BiLSTM) on the CSI 300); while the integration of the self-attention module yields higher model accuracy with negligible efficiency costs.