Stock Price Prediction of China Merchants Bank Based on GWO-BP Neural Network Modeling

随着金融市场的不断发展，股票价格预测已成为学术界和实务界的重要研究领域。准确的股票价格预测对投资者制定合理的投资策略具有重要意义。然而，由于股票价格受多种因素影响且波动性较大，传统的预测方法往往难以捕捉其复杂的非线性特征。近年来，人工智能技术特别是神经网络模型在金融预测领域得到了广泛应用。BP神经网络凭借其卓越的非线性映射功能，在预测股票市场价格方面被广泛运用。然而，该网络模型常常面临陷入局部最优解的困境，加之训练速度缓慢，这些因素均限制了其在预测精度上进一步的提升。为了改善BP神经网络的预测效果，使用基于灰狼优化算法的GWO-BP神经网络模型，用于预测招商银行的股价，通过将GWO算法与BP神经网络相结合，GWO-BP模型能够在全局范围内优化BP神经网络的初始权重和偏置，避免陷入局部最优，并提高模型的收敛速度和预测精度。探究招商银行股价未来走势进行预测有利于降低银行系统金融性风险发生概率。本文以2022年1月4日至2024年8月31日招商银行日股价数据作为研究对象，基于GWO-BP神经网络模型模型对招商银行股价未来走势进行研究，文章详细阐述了如何将灰狼优化算法融入BP神经网络，以优化其权重，从而增强其学习速度和预测准确性。深入挖掘灰狼算法在改进BP网络初始化、权重调整及学习率调节方面的潜力，以克服传统BP网络在局部最优和收敛速度上的局限。研究伊始，对BP网络与灰狼算法的原理进行了系统梳理，随后详尽阐述了优化策略，并通过实证研究证实了该改进技术的优越性。研究最终揭示：引入灰狼算法的BP网络在众多应用领域均展现出卓越的性能进步。With the continuous development of the financial market, stock price forecasting has become an important research field in both academic and practical circles. Accurate stock price forecasting is important for investors to formulate reasonable investment strategies. However, since stock prices are affected by a variety of factors and are highly volatile, it is often difficult for traditional forecasting methods to capture their complex nonlinear characteristics. In recent years, artificial intelligence techniques, especially neural network models, have been widely used in the field of financial forecasting, and BP neural networks have been widely utilized in predicting stock market prices due to their excellent nonlinear mapping function. However, the network model often faces the dilemma of falling into the local optimal solution, coupled with the slow training speed, all these factors limit its further improvement in prediction accuracy. In order to improve the prediction effect of BP neural network, GWO-BP neural network model based on Gray Wolf Optimization algorithm is used to predict the stock price of China Merchants Bank. By combining GWO algorithm with BP neural network, GWO-BP model can optimize the initial weights and bias of the BP neural network globally to avoid falling into the local optimum, and to increase the convergence speed of the model and prediction accuracy. Exploring the future trend of China Merchants Bank’s stock price to predict is conducive to reducing the probability of financial risk in the banking system. This paper takes the daily stock price data of China Merchants Bank from January 4, 2022 to August 31, 2024 as the research object, and researches the future trend of China Merchants Bank’s stock price based on the GWO-BP neural network model. The article explains in detail how to integrate the Gray Wolf optimization algorithm into the BP neural network to optimize its weights, so as to enhance its learning speed and prediction accuracy. The potential of the Gray Wolf algorithm in improving the initialization, weight adjustment and learning rate regulation of BP networks is deeply explored to overcome the limitations of traditional BP networks in terms of local optimum and convergence speed. At the beginning of the study, the principles of the BP network and the gray wolf algorithm are systematically sorted out, followed by an exhaustive description of the optimization strategy, and the superiority of the improvement technique is confirmed through empirical studies. The study finally reveals that the BP network introducing the gray wolf algorithm shows excellent performance progress in many application areas.