中国股市投资者预测交易到达率的GARCH学习行为

The GARCH Learning Behavior of Investors’ Forecasting Transaction Arrival Rate of Chinese Stock Market

在投资者具有学习能力的假设基础上,构建了一个允许知情和未知情交易到达率时变且可预测的GARCH结构信息模型。应用该模型,基于沪深300成分股2006—2009的高频分笔交易数据,研究了中国股票市场投资者学习市场交易信息并调整其交易行为的动态过程。发现投资者能够根据前期的预测到达率以及订单数据推测的当期到达率,优化其下期的交易决策;此外,预测误差对后期预测的影响是一个二阶导数大于零的递减函数。

Based on the assumption that investors have the learning ability ,a GARCH structure information model allowing the informed and uninformed arrival rate to be time-varying and predictable was built. Applying the model and basing on the Shanghai ＆ Shenzhen 300 Index component stocks from 2006 to 2009 high-frequency tick by tick transaction data, we investigated the dynamic process how Chinese stock market investors learned from market transaction information and adjusted their trade behavior. We found that investors could optimize their next period transaction decision-making based on previous forecasting arrival rate and current period arrival rate derived from order data. In addition, the impact of prediction error on next period forecast is a decreasing function whose second derivative is zreater than zero.