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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.
Aiming at the complexity of the internal structure of the stock market and the variability of external factors, a forecasting model of the stock market bubble is constructed. Taking the Shanghai Stock Index as the research object, on the basis of price and volume, the macroeconomic indicators closely related to the stock market The bubble model index system is introduced and the long-term equilibrium relationship and causal relationship among variables in the index system are quantitatively analyzed.Under this index system, a vector autoregressive model (VAR) model is constructed to measure the basic value of the stock market and is analyzed accordingly Macro-economic indicators on the market; at the same time, a generalized dynamic fuzzy neural network model (GD-FNN) based on elliptic basis function and network architecture can be dynamically adjusted to fit and predict the Shanghai Composite Index as the market value of the stock market, and through the GD- FNN model to analyze the non-linear stock system’s operating mode.Finally, according to the forecast deviation between the market value and the basic value of the stock market, the paper calculates the degree of foam and puts forward the corresponding early-warning strategy.