Using neural networks to predict the value of stocks based on news data

Georgy Borisenko ()
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Georgy Borisenko: Department of Economics, Lomonosov Moscow State University

No 55, Working Papers from Moscow State University, Faculty of Economics

Abstract: This paper is devoted to forecasting the value of shares of large Russian companies traded on the Moscow Stock Exchange based on news. Neural networks transformers are used as models for forecasting. Moreover, classical machine learning methods are also involved in the analysis for comparison with the neural network approach. Major Russian news sources and Telegram channels are used as news data. Models trained on different sources are also compared. As a result of the study, it was found that classical machine learning methods cope better with this task in the general case, but neural networks also show good quality. The paper also provides recommendations on the choice of a news source and the choice of a task statement.

Keywords: shape price; news; network approach; Telegr?m (search for similar items in EconPapers)
JEL-codes: C63 G14 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2023-05
New Economics Papers: this item is included in nep-big, nep-cis, nep-cmp and nep-dcm
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Persistent link: https://EconPapers.repec.org/RePEc:upa:wpaper:0055

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