基于GD-FNN的股票市场泡沫模型

针对股票市场内部结构复杂性和外部因素多变性, 构建一种基于预测的股票市场泡沫模型. 以上证指数为研究对象, 在价格和成交量的基础上, 将与股票市场密切相关的宏观经济指标引入泡沫模型指标体系, 并对指标体系中各变量之间长期均衡关系和因果关系进行数量分析. 在此指标体系下, 构建向量自回归模型(VAR)模型衡量股票市场基础价值, 并据此分析宏观经济指标对市场的影响; 同时构建基于椭圆基函数且能够动态调整网络结构的广义动态模糊神经网络模型(GD-FNN)对上证指数进行拟合预测作为股票市场的市场价值, 并通过GD-FNN模型提取的模糊规则对股票非线性系统运行模式进行分析. 最后, 根据预测的股票市场市场价值与基础价值之间的偏差计算泡沫度, 并提出相应的预警策略.

Model of stock market bubble based on GD-FNN

For complexity of inside structure and variability of external factors of stock market which make stock market forecasting and identification complex problems,stock market bubble forecasting model was proposed with early-warning strategy.The forecasting index system of bubble model was set which involves price and volume of Shanghai Composite Index and macroeconomic indicators associated with stock market. The long-run equilibrium and cause and effect relationship among the index variables were analyzed.Based on the index system,vector auto regression model(referred to as VAR) was proposed to measure basic value of stock market and impact on stock market of macroeconomic indicators was analyzed.Generalized dynamic fuzzy neural network(referred to as GD-FNN) model was proposed to measure market value of stock market which is based on elliptic basis function and can dynamically adjust the network structure and fuzzy rules of financial system were extracted which can reveal financial nonlinear system operating mode.Finally,according to deviation between market value and basic value,bubble degree of stock market was calculated with corresponding early-warning strategy.