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:
HSE Researchers Make Aldehydes Perform Dual Function
Chemists from HSE University have discovered a way to carry out a reductive addition reaction without using an external reducing agent. Instead, the required 'resource' is supplied by the aldehyde itself, one of the reaction participants. This approach helps prevent unwanted side reactions, reduces toxicity, and simplifies the production and synthesis of organic molecules, including those used in the manufacture of medicines. The study has been published in Journal of Catalysis.
HSE Scientists Explain Why Findings in Autism Research Differ
Researchers from the Cognitive Health and Intelligence Centre at HSE University conducted the first-ever systematic review of studies on the specifics of emotion-from-motion perception in autism. The review showed that differences found between autistic and non-autistic individuals are largely associated with the experimental design and the types of tasks given to study participants. The review findings have been published in Research in Autism.
Tremors: Scientists Develop Method for Real-Time Tracking of Hazardous Underground Vibrations
Researchers from HSE MIEM and IPKON RAS have developed a new mathematical monitoring model that can identify the source of hazardous underground vibrations in real time. The technology could help reduce the risk of damage to buildings, roads, and other infrastructure located near quarries and mining sites. The paper has been published in Russian Mining Industry.
HSE Researchers Determine Which Internet Users Are More Likely to Fact-Check
Researchers at HSE University examined the strategies employed by Russian internet users to verify unreliable information and the factors that motivate them to do so. The study found that more than half of users who encounter potentially false information online attempt to verify it by locating the original source. The likelihood of fact-checking is influenced by several factors, including age, place of residence, social status, information literacy skills, and the use of AI. The findings have been published in Monitoring of Public Opinion: Economic and Social Changes.
Tabular Data Anonymisation Solution for Safe Use in AI Systems Developed at HSE University
The AI and Digital Science Institute at the HSE Faculty of Computer Science has developed a tabular data anonymisation service designed to prepare corporate datasets for use in analytics and AI applications. The solution can identify personal data in structured datasets, apply consistent and reproducible anonymisation rules, and generate the artifacts required for quality control, auditing, and subsequent use of data in secure environments.
Population Lifespan Is Governed by Mathematical Laws
Researchers at HSE University and MSU have established a universal law governing the time to extinction of a population in a random environment. Their analysis of the evolution of branching processes—complex probabilistic systems—shows that, regardless of the initial population size, extinction follows strict mathematical laws. The results have been published in the Journal of Applied Probability.
Sociologists: Conservative Consumers Dominate Russian Middle Class
The Russian middle class cannot be regarded as a homogeneous and uniformly stable social group. Similar income levels often mask significant differences in financial strategies, lifestyles, and levels of economic security. This is the conclusion reached by sociologists at HSE University. The study has been published in Voprosy Ekonomiki.
Neurolinguists Assist in Awake Surgery on 11-Year-Old Patient with Epilepsy
Researchers at the HSE Centre for Language and Brain took part in a rare awake neurosurgical procedure performed on an 11-year-old patient with drug-resistant epilepsy. Working alongside surgeons at the Voyno-Yasenetsky Centre of Specialised Medical Care for Children in Solntsevo, they monitored the resection of a portion of the left temporal lobe, where the epileptic focus had been identified.
Scientists Explain How Emotions Shape Attitudes Toward Digital Governance
Today, interactions between citizens and government increasingly take place through digital governance platforms, including digital public services, AI-powered systems, and algorithmic decision-making tools. Until now, however, these technologies have largely been viewed as technical instruments, with their effectiveness assessed primarily in terms of efficiency and user-friendliness. The authors of a new study propose a broader perspective, arguing that digital governance should also be understood as an emotional experience that directly shapes citizens' trust in public institutions.
Neural Network Maps as a Method for Constructing Mathematical Models
Scientists from HSE University–Nizhny Novgorod and the Institute of Physics Belgrade, Serbia, are jointly exploring the application of machine learning techniques and neural networks to the study of nonlinear dynamics. Natalya Stankevich, Leading Research Fellow at the Laboratory of Topological Methods in Dynamics of the Faculty of Informatics, Mathematics, and Computer Science at HSE University–Nizhny Novgorod, spoke to the HSE News Service about this international project.