摘要
股市的剧烈波动会影响金融市场的平稳运行进而影响经济增长,如何对股市的走势进行精准预测一直是学术界关注的焦点问题.由于股指收益率具有非平稳、非线性特征,仅利用历史序列作为影响因素将导致预测精度不佳.考虑到基金仓位变化对股市的信息增益作用及二者数据间存在混频关系,提出一种反向混频数据抽样模型(R-MIDAS)与机器学习算法结合的新模型,应用于27个行业股指收益率的预测及投资决策的研究中.实证结果表明:R-MIDAS-GWO-SVR模型在多数行业的预测效果优于基准模型;基于预测结果开展单一行业与多行业组合的投资策略,R-MIDAS-GWO-SVR模型的表现也更好,其风险调节的绩效指标显著优于其余模型.
Abstract
The violent fluctuations of the stock market pose a threat to financial stability and have a significant impact on a country's economic development.Therefore,understanding and predicting stock market fluctua-tions play a crucial role in evaluating a country's economic performance.Stock returns exhibit characteristics such as non-stationarity,nonlinearity,and volatility aggregation.As a result,stock return forecasting has garnered substantial interest among scholars.However,most existing studies solely rely on historical stock price sequences for prediction,which often leads to subpar results.The weekly frequency of fund position changes holds significant value in determining future market trends.Increasing fund positions can drive stock market upswings,while individual retail investors tend to follow and mimic these position changes,thereby influencing future stock market movements.Recognizing the information gain effect of fund position changes on the stock market and the intricate relationship between these two types of data,a novel model is proposed that combines the reverse mixed data sampling model(R-MIDAS)with machine learning algorithms.The model is applied to predict the index return rate and investment strategy for 27 industries.The empirical results demonstrate several key findings.Firstly,the performance of the R-MIDAS-GWO-SVR algorithm surpasses that of other benchmark models,such as R-MIDAS-SVR,R-MIDAS-CNN,and R-MIDAS-LSTM.In particular,the R-MIDAS-GWO-SVR model outperforms the LR model in 19 industries.Secondly,the proposed model exhibits excellent performance in single-industry investment strategies,as indicated by risk-adjusted performance indicators based on the forecasted results.Lastly,when considering multi-industry portfolio investments,the R-MIDAS-GWO-SVR model consistently outperforms other models for various values of k(specifically,5,7,and 9).The combination of the R-MIDAS model and machine learning methods shows promising potential in predicting mixed frequency data.These findings contribute to the literature by introducing a new approach to stock return forecasting and highlighting the importance of incorporat-ing fund position changes into prediction models.The proposed model has significant implications for investors,regulators,and policy makers in making informed decisions and formulating effective investment strategies in the stock market.
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