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本文采用人类主体实验的方法获得符合本国投资者学习过程的股价序列,来校验计算实验金融里Roth-Erve学习模型中的遗忘参数和类比参数。通过在一定区间内改变学习模型参数的大小,得到拟合性相对高、适应本国投资者学习过程的模型参数。研究表明:人类主体实验得到收敛和阻尼振动的两类股价序列;用学习模型对两类股价序列进行模拟,找到拟合性高的解,并分析其理论意义;在给定的区间内,通过拟合性高低的比较,可以找到相对本国投资者适应性更高的参数值。这为计算实验金融的应用,提供了更适应本国投资者的学习模型。
This paper uses the method of human subjects to obtain the stock price series which accords with the investor’s learning process in our country to check the forgetting parameters and the analog parameters in Roth-Erve learning model of computational experiment finance. By changing the parameters of the learning model within a certain interval, the model parameters that have relatively good fitting properties and adapt to the learning process of their own investors are obtained. The research shows that two kinds of stock price series with convergence and damping vibration are obtained in the experiment of human subject, the two models of stock price series are simulated by learning model to find the solution with high fitting, and the theoretical significance is analyzed. In a given interval, Comparison of the level of fit, you can find a higher level of adaptability of domestic investors parameter values. This is to calculate the application of experimental finance, to provide a more suitable learning model for domestic investors.