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Early warning system for Russian stock market crises: TCN-LSTM-Attention model using imbalanced data and attention mechanism

Author

Listed:
  • Teplova, Tamara
  • Fayzulin, Maksim
  • Kurkin, Aleksei

Abstract

This research is devoted to the development and evaluation of the effectiveness of machine learning and deep learning models for forecasting crisis phenomena in the Russian stock market. The work covers the period from the beginning of 2014 to June 2024, using the IMOEX index as the main indicator of the market condition. Special attention is paid to the problem of the imbalanced data structure and accounting for investor sentiment.

Suggested Citation

  • Teplova, Tamara & Fayzulin, Maksim & Kurkin, Aleksei, 2025. "Early warning system for Russian stock market crises: TCN-LSTM-Attention model using imbalanced data and attention mechanism," Socio-Economic Planning Sciences, Elsevier, vol. 101(C).
    • Handle: RePEc:eee:soceps:v:101:y:2025:i:c:s0038012125001417
    DOI: 10.1016/j.seps.2025.102292
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