摘要
针对金融波动性和市场风险,基于A股市场上70余只智能板块的股票近10年的四因子数据,从神经网络模型入手实证分析,利用随机梯度算法对收盘价预测,比较预测值与实际值的模型误差及损失函数,进行因子选取、算法改进及指标择优。结果表明,神经网络模型参数在批次为2、迭代次数为4150时,MSE(均方误差)、MAPE(平均绝对百分比误差)、MAE(平均绝对误差)分别为60.1911、30.7326、4.8032,收盘价的拟合效果最佳,该参数下的神经网络模型可用于探究股票市场价格趋势,为投资者、金融机构提供一定参考依据。
For financial volatility and market risk,starting the empirical analysis by neural network model based on four-factor data of more than 70 stocks in smart industry sector in the A-share market for the past ten years.In this model,stochastic gradient algorithm for closing price prediction was used,then the model error and loss function of predicted and actual values was compared,and optimizing from factor selection,algorithm improvement and indicator merit.The results show that closing price is best fitted when neural network model at batch=2 and epoch=4150,MSE,MAPE and MAE are 60.1911,30.7326,4.8032 respectively.The neural network model with these parameters can be used to explore the stock market price trend and provide some reference basis for investors or financial institutions.
作者
庄妍
王林萍
ZHUANG Yan;WANG Linping(College of Economics and Management,Fujian Agriculture and Forestry University,Fuzhou 350002,China)
出处
《科技和产业》
2023年第14期250-258,共9页
Science Technology and Industry
关键词
神经网络
智能产业板块
股票预测
随机梯度下降法
数据拟合
neural network
smart industry sector
stock prediction
stochastic gradient descent algorithm
data fitting
