Modal decomposition-based hybrid model for stock index prediction

Abstract

Stock index prediction is considered one of the most challenging issues in the financial sector owing to its noise, volatility, and instability. Traditional stock index prediction methods, such as statistical and machine learning methods, cannot achieve a high denoising effect, and also cannot mine enough data features from the stock data, resulting in a poor prediction performance. Deep learning has become an effective tool to predict non-stationary and nonlinear stock indices with strong learning ability. However, there is still room for prediction accuracy improvement if a single deep learning prediction model is replaced with a hybrid model. Therefore, this study proposes a novel deep learning hybrid model for stock index prediction named CEEMDAN-DAE-LSTM. In this hybrid model, the stock index is first decomposed using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) into a series of intrinsic mode functions (IMFs) arranged from high to low frequency. Next, the deep autoencoder (DAE) is applied to remove redundant data and extract deep-level features. Then, high-level abstract features are separately fed into long short-term memory (LSTM) networks to predict the stock returns of the next trading day. Finally, the final predicted value is obtained by synthesizing the value of each component. Empirical research results on six stock indices representing both developed and emerging markets showed that our model is superior to other reference models in terms of prediction accuracy and stock index trends; furthermore, it has higher prediction performance for stock indices with greater volatility. In general, this model could be applicable to various stock markets with different degrees of development.