Abstract
The stock index is an important indicator to measure stock market fluctuation, with a guiding role for investors’ decision-making, thus being the object of much research. However, the stock market is affected by uncertainty and volatility, making accurate prediction a challenging task. We propose a new stock index forecasting model based on time series decomposition and a hybrid model. Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the stock index into a series of Intrinsic Mode Functions (IMFs) with different feature scales and trend term. The Augmented Dickey Fuller (ADF) method judges the stability of each IMFs and trend term. The Autoregressive Moving Average (ARMA) model is used on stationary time series, and a Long Short-Term Memory (LSTM) model extracts abstract features of unstable time series. The predicted results of each time sequence are reconstructed to obtain the final predicted value. Experiments are conducted on four stock index time series, and the results show that the prediction of the proposed model is closer to the real value than that of seven reference models, and has a good quantitative investment reference value.
Highlights
The stock index is calculated based on some representative listed stocks
We propose a hybrid stock index forecasting model based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) [3]
It can be seen that the CAL model yields the closest prediction results, and CEEMDAN-Long Short-Term Memory (LSTM) is closer to the observed values in comparison with Empirical mode decomposition (EMD)-Autoregressive Moving Average (ARMA)-LSTM and ARIAM
Summary
The stock index is calculated based on some representative listed stocks To some extent, it can reflect price changes of the whole financial market, its use as an important indicator of the country’s future macroeconomic performance. Statistical models were first used to predict the stock market in finance, and have made some achievements. They assume a linear and stationary time series, which is inconsistent with the dynamic, non-linear characteristics of the real stock market, so they have great limitations. A deep learning model can overcome the defects of traditional statistical models in time series prediction but is affected by noise in some complex and dynamic financial systems, making it difficult to mine the hidden features of time series, resulting in poor learning ability and limited prediction accuracy
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