Affiliations: [a] Department of Economics and Management, Fuzhou University, Fuzhou, China
| [b] Department of Economics, Wuhan University of Technology, Wuhan, China
| [c] Department of Economics and Management, Fujian Agriculture and Forestry University, Fuzhou, China
Correspondence:
[*]
Corresponding author. Jinlu Guo, Department of Economics, Wuhan University of Technology, Wuhan 430070, China. E-mail: [email protected].
Abstract: The regularly issued low frequency data, such as the change of fund position (weekly), and Producer Price Index (monthly), can affect the subsequent trend of stock returns. However, the forecasting effect of low frequency data on high frequency has not been discussed amply. This paper proposes a new mixed frequency neural network that helps to fill this research gap. The original time series is decomposed into several components through ensemble empirical mode decomposition, then the frequency alignment method is applied to integrate the high frequency component with low frequency variable as inputs, and the CNN-BiLSTM-Attention network completes the remaining forecasting work. The empirical results show that compared with other benchmark models, the proposed procedures perform better when predicting the high frequency components and obtain a smaller statistical error in the final ensemble results. The proposed model has great potential for the forecasting of reverse mixed time series.
Keywords: Stock returns, Ensemble empirical mode decomposition, Deep learning, Attention mechanism, Mixed frequency prediction
