高频数据是否能改善股票价格预测?——基于函数型数据的实证研究

Can High Frequency Data Improve Stock Prices Forecasting?——Empirical Evidence Based on Functional Data Analysis

股票价格预测一直是学术界和投资者关注的一个重要问题,但由于股票价格走势非常复杂,同时取决于宏观经济环境、个股基本面和投资者情绪等诸多因素,寻找一个合适的模型来准确预测股票价格极具挑战性.传统时间序列预测模型往往基于日度、周度或月度历史数据对股价走势进行预测,但是预测效果一般.近年来,随着金融市场的飞速发展,借助现代信息收集技术,我们可以收集到时间间隔很小的高频交易数据,而高频数据包含大量低频交易数据之外的信息,因此有可能改善股价预测.本文基于非参数函数型数据分析方法从高频交易数据中提取预测因子,并与传统时间序列预测模型构成混合预测模型来对股价走势进行预测.我们的模型对高频预测因子的构造不作任何参数形式的设定,从而具有很高的灵活性.实证研究方面,我们将模型用于预测沪深300指数,分析结果表明,基于高频数据的新预测模型较之传统时间序列模型在预测表现上有显著改善,说明高频交易数据的确有助于改善短期股价预测.

Forecasting stock prices has been regarded as one of the most challenging tasks,because stock prices are determined by many factors,including macroeconomic indicators,fundamental factors and investors sentiment etc.Traditional time series models forecast stock returns only based on daily,weekly or monthly historical observations.Recently,the fast development of the financial markets and data collection technology make high-frequency data more and more available.As high-frequency data may contain some extra information compared to low-frequency data,it may be able to improve stock prices forecasting.In this paper,we propose a new method based on nonparametric functional data analysis,which is used to extract predictors from high-frequency data.Then we combine the high frequency predictors with traditional time series predictors to be a mixed prediction model for forecasting stock prices.The construction method of predictors is very flexible,without any parametric assumption.For empirical studies,we apply the method to CSI300 index,and find that the new method after incorporating the useful information from high-frequency data performs better than the traditional autoregressive model.Our results provide an empirical evidence that high-frequency data could help improve stock prices forecasting.