Neural Network Trained to Predict Crises in Russian Stock Market
Economists from HSE University have developed a neural network model that can predict the onset of a short-term stock market crisis with over 83% accuracy, one day in advance. The model performs well even on complex, imbalanced data and incorporates not only economic indicators but also investor sentiment. The paper by Tamara Teplova, Maksim Fayzulin, and Aleksei Kurkin from the Centre for Financial Research and Data Analytics at the HSE Faculty of Economic Sciences has been published in Socio-Economic Planning Sciences.
How can a stock market storm be predicted? Financial analysts and investors worldwide are eager to find the answer. A study by Tamara Teplova, Maxim Fayzulin, and Aleksei Kurkin from the HSE Centre for Financial Research and Data Analytics presents a novel approach to predicting short-term crises in the domestic stock market. The hybrid deep learning model developed by the researchers combines three architectures—Temporal Convolutional Network (TCN), Long Short-Term Memory (LSTM), and an attention mechanism—marking the first use of such a complex structure for Russian stock data.
The authors analysed data from 2014 to 2024, incorporating market and macroeconomic indicators—primarily the Moscow Stock Exchange IMOEX index—along with measures of investor sentiment. To predict the likelihood of a crisis within the next one to five trading days, the researchers first had to address several methodological challenges. First, market crises are relatively rare—accounting for at most a quarter of all events—which makes the training sample imbalanced and risks the model learning to ignore these infrequent signals. Second, investor behaviour is influenced not only by objective economic factors but also by subjective sentiments, which are difficult to formalise. To address these challenges, the researchers created composite indices of internal and external investor sentiment using the principal component method. These indices complement traditional macroeconomic and market variables, making it possible to capture hidden investor sentiment over longer forecasting horizons.
Tamara Teplova
'We present a hybrid TCN-LSTM-Attention model that combines deep learning with attention mechanisms. The model effectively handles imbalanced data, achieving an accuracy of 78.70% for same-day forecasts and 78.85% for predictions on the following trading day. Monthly retraining and the use of adaptive time windows have increased accuracy to 83.87%. Key factors influencing the forecasts include stock index values (similar to those used in technical analysis), total company capitalisation, and exchange rates,' explains Tamara Teplova, Professor at the HSE Faculty of Economic Sciences.
The developed system can be a valuable tool for investors, financial analysts, and regulators. It not only enables retrospective analysis of crisis periods but also allows reliable prediction of potential threats one to two days in advance. When combined with regular updates using new data, such a system can serve as the foundation for a dynamic risk-monitoring framework tailored to the specifics of the Russian market.
'This work is highly relevant for the national financial sector, providing effective tools for timely detection of market shocks—a critical need in an unstable macroeconomic environment,' Prof. Teplova emphasises.
The study was conducted with support from HSE University's Basic Research Programme within the framework of the Centres of Excellence project.
See also:
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
Researchers Rank Recommendation Algorithms Using Sports Tournament Model
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed an approach for selecting recommendation algorithms more effectively. Their approach uses pairwise comparisons of algorithms to create a tournament table, with the overall ranking based on their performance across all datasets in the tournament. This can reduce the number of algorithms that need to be tested when developing new services, saving both time and money. The study was presented at the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026).
Researchers Develop Method for Direct Generation of Regulatory DNA
Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’
Researchers at HSE University and Sber Train Neural Networks to Better Predict User Preferences
The HSE FCS AI and Digital Science Institute and Sber have introduced a new architecture for recommendation systems that combines two classes of models, enabling algorithms to better predict users’ interests and needs. A preprint of the paper has been published on arxiv.org and presented at Urban ML.
Social Integration: At the Crossroads of Knowledge and Values
The International Laboratory for Social Integration Research (ILSIR) at HSE University studies the challenges faced by vulnerable groups and explores ways to help them participate fully in everyday life. To develop effective solutions, the laboratory’s researchers combine cutting-edge methods with practical fieldwork. In this interview with the HSE News Service, Laboratory Head Elena Iarskaia-Smirnova discusses the laboratory’s work.
Physicists Discover What Happens Inside a Stable Vortex
Large vortices with characteristic spiral arms are often observed in the atmosphere and the ocean. Physicists from HSE University have explained how these structures form and why they retain their shape. The researchers found that velocities at points located along the same vortex arc remain correlated even over long distances. At the same time, this correlation weakens rapidly with increasing distance from the vortex centre. These differences help explain the formation of spiral arms and may improve models of atmospheric and oceanic currents. The findings have been published in Physical Review Fluids.
HSE Computer Science Researchers Win Gold Medal at International Machine Learning Competition
A team comprising HSE International Laboratory of Statistical and Computational Genomics researchers Aleksei Shmelev and Nikita Chervov, 2025 graduate of the HSE Faculty of Computer Science’s Master’s programme in Data Analysis in Biology and Medicine Ivan Gevorkov, and two students from the United States achieved an outstanding result at the 2026 NeuroGolf international machine learning championship. The team won a gold medal and placed seventh overall.
‘Working with AI Solves a Wide Range of Engineering Problems’
Artificial intelligence is a working tool based on a balanced combination of algorithms and engineering. Experts and doctoral students from the HSE Moscow Institute of Electronics and Mathematics explain how AI technologies can improve an application, device, or system, and what engineering tasks are solved in the process.
‘The Peak of Stupidity’ and ‘The Valley of Despair’: HSE Economists Propose an Explanation for the Dunning–Kruger Effect
The Dunning–Kruger effect, which describes a sharp surge in self-confidence among beginners followed by an equally rapid decline as they gain experience, can be explained by the nature of the learning process and the acquisition of new knowledge. This conclusion was reached by Andrey Vorchik of the HSE Faculty of Economic Sciences together with independent researcher Murat Mamyshev. They developed a mathematical model of learning and demonstrated how subjective confidence is formed and changes as knowledge accumulates, as well as how teachers can reduce the ‘valley of despair’ experienced by learners.
Toffee and Risk: Scientists Discover Why People Who Crave Sweets Make More Impulsive Choices
Having a sweet tooth may be linked not only to eating habits but also to the way people make decisions. Researchers at HSE University have found that people with a preference for sweet foods tend to behave more impulsively—not because they want immediate rewards, but because they are less willing to tolerate uncertainty. These findings may help improve treatments for addiction. The study findings have been published in Frontiers in Psychology.