With an accuracy of 83%: A neural network predicting crises in the stock market was created at the Higher School of Economics

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.

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.

Earlierwww1.ru reported that Smart Enginescreated a new bipolar morphological neural network.

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