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Forecasting of S&P 500 ESG Index by Using CEEMDAN and LSTM Approach

Author

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  • Divya Aggarwal
  • Sougata Banerjee

Abstract

This study aims to forecast the S&P 500 ESG index using the mixture model of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and long short‐term memory (LSTM) prediction models. CEEMDAN enables decomposing the index's original return series into different intrinsic mode functions (IMFs) and a residual series. The decomposed IMFs are then regrouped into aggregate series depicting high frequency and medium frequency, while the residual series represent the trend component. LSTM algorithm is used on the aggregated series to obtain predicted values of the same. The study compares different prediction algorithms to identify their performance and explore the predictive power of the hybrid models.

Suggested Citation

  • Divya Aggarwal & Sougata Banerjee, 2025. "Forecasting of S&P 500 ESG Index by Using CEEMDAN and LSTM Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 339-355, March.
    • Handle: RePEc:wly:jforec:v:44:y:2025:i:2:p:339-355
    DOI: 10.1002/for.3201
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    References listed on IDEAS

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    2. Mao, Yaqi & Yu, Xiaobing & Liu, Jia & Wang, Feng & Zhang, Aixin & Zhu, Junhua, 2025. "Disentangling market drivers and macro uncertainty risks in crude oil futures pricing: A multi-scale quantile regression and causal forest approach," Energy, Elsevier, vol. 332(C).

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