基于ARIMA与信息粒化SVR组合的股指预测研究

对于证券市场投资者而言,基于合理假设准确预测资产价格未来发展方向与趋势关乎投资成败。本文通过构建一个基于ARIMA与信息粒化SVR的组合预测模型,对股票市场指数价格和收益变化的趋势进行预测。实证研究结果表明:基于ARIMA与信息粒化SVR组合的股指预测模型相较于传统时间序列模型而言,在预测精度和效度方面有较大提升,能够在一定时间周期内对股票等风险资产的价格波动区间进行较为可靠地预测,但目前还只能大致确定时间序列波动的区间范围而不能精确地预测具体点位。未来仍需结合其他预测模型和预判技术进一步深入研究,以有效提升股指趋势预测的准确性和实际指导性。

Study on Stock Index Prediction Based on ARIMA and Information Granular SVR Combination

For investors in the securities market, accurate prediction of future development direction and trend of asset price based on reasonable assumptions is related to investment success or failure. This paper constructs a combination prediction model based on ARIMA and information granulation SVR to predict the trend of stock market index price and income change. The empirical results show that the index prediction model based on the combination of ARIMA and information granulation SVR has a great improvement in prediction accuracy and validity compared with the traditional time series model. It can predict the price fluctuation range of risk assets , such that it can be predicted reliably in a certain time period, but the range of time series fluctuation can not be accurately predicted. In the future, it is necessary to further study other prediction models and prediction techniques in order to effectively improve the accuracy and practical guidance of stock index trend prediction.