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

Tamara Teplova, Maksim Fayzulin and Aleksei Kurkin

Socio-Economic Planning Sciences, 2025, vol. 101, issue C

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.

Keywords: Stock market crisis; Early warning system; Time series classification; Deep learning; Hybrid models (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0038012125001417
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:soceps:v:101:y:2025:i:c:s0038012125001417

DOI: 10.1016/j.seps.2025.102292

Access Statistics for this article

Socio-Economic Planning Sciences is currently edited by Barnett R. Parker

More articles in Socio-Economic Planning Sciences from Elsevier
Bibliographic data for series maintained by Catherine Liu ().