Forecasting stock index price using the CEEMDAN-LSTM model

Yu Lin, Yan Yan, Jiali Xu, Ying Liao and Feng Ma

The North American Journal of Economics and Finance, 2021, vol. 57, issue C

Abstract: This paper uses a mixture model that Long Short-Term Memory (LSTM) combines with Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to forecast stock index price of Standard & Poor's 500 index (S&P500) and China Securities 300 Index (CSI300). CEEMDAN decomposes original data to obtain several IMFs and one residue. The LSTM forecasting model utilizes the decomposed data to obtain the prediction sequences. The prediction sequences are reconstructed to gain final prediction. The paper introduces contrast models such as Support Vector Machine (SVM), Backward Propagation (BP), Elman network, Wavelet Neural Networks (WAV) and their mixture models combined with the CEEMDAN. The MCS test is used as evaluation criterion and empirical results present that forecasting effects of CEEMDAN-LSTM is optimal in developed and emerging stock market.

Keywords: Stock index price forecasting; Long short-term memory; CEEMDAN; Mixture models; MCS test (search for similar items in EconPapers)
JEL-codes: C22 C53 C61 E37 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:57:y:2021:i:c:s1062940821000553

DOI: 10.1016/j.najef.2021.101421

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