Scientists from the Higher School of Economics have created a neural network that can warn of an impending short-term stock market crisis with an accuracy of over 83% 24 hours before the event.
The work has high practical significance for the national financial sector: it offers effective tools for the timely detection of market shocks, which is especially important for an unstable macroeconomic environment.
A hybrid model was created to obtain such a forecast. It combines three machine learning architectures - an attention mechanism, temporal convolutional networks, and LSTM, which allows AI to have short-term memory. This innovation in the application of complex structures to Russian exchange data has not previously been used by economists.
To train the model, researchers analyzed data from 2014 to 2024, including market and macroeconomic indicators, such as the Moscow Exchange IMOEX index and investor sentiment indicators. Scientists had to take into account the rarity of crises and the influence of subjective sentiments on investor behavior.
To solve these problems, composite indices of internal and external investment sentiment were developed using the principal component method. These indices complement traditional macroeconomic and market variables, allowing us to capture the hidden emotional signals of traders over longer time horizons. Thanks to this, the model has achieved high forecasting accuracy, which makes it possible to predict a short-term crisis in the stock market one day before it begins.
The model effectively processes uneven data and achieves an accuracy of 78.70% when predicting crisis events on the day of observation and 78.85% on the next trading day. The use of monthly retraining and adaptive time windows made it possible to increase the accuracy to 83.87%. The key factors influencing predictions were exchange indicators, the capitalization of companies - stock issuers and market exchange rates.
Earlierwww1.ru reported that Smart Enginescreated a new bipolar morphological neural network.
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