Abstract
Artificial intelligence and machine learning are increasingly being used in many areas of economy and finance. Developments in technology and the increasing accessibility of computing facilities allow for the wider use of various programming tools. For many companies operating in the field of stock trading and other derivatives, availability of an effective forecasting mechanism is an important competitive advantage, and increasing this advantage is now possible by using neural network models such as LSTM. The authors present the results of testing the LSTM model on the basis of actual data (quotations of Gazprom shares at Moscow Exchange): quotation values were predicted and the trend of Gazprom stocks at Moscow Exchange, starting from September 2019, was revealed.
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