V.S. Gavrilov1

1 Financial University under the Government of the Russian Federation (Moscow, Russia)
1 232114@edu.fa.ru

In the modern financial industry, a pressing issue is the analysis of large volumes of news streams for predicting the dynamics of the stock market. Traditional analysis methods struggle with the speed and volume of incoming information, necessitating the development of automated systems based on machine learning.

Objective – to develop a software suite based on a machine learning model for the automatic analysis of news streams in the financial sector, capable of effectively predicting news topics and their impact on the stock market.

The work presents methods of news scraping, natural language processing (NLP), and statistical analysis used to create the machine learning model. The developed model demonstrates high accuracy in predicting news topics with an ROC-AUC score of over 96%. The effectiveness of the software suite is confirmed by computational experiments and practical implementation in Python 3.

The developed software suite significantly accelerates the process of analyzing news streams, reduces the costs of quantitative analysis, and enhances the quality of decision-making on the stock market. The model can be adapted for analyzing news streams in other areas such as economics, politics, and social studies, thereby expanding its application scope and potential audience.

Gavrilov V.S. Development of a Bundled Software and Machine Learning Models to Automate Analysis of News Streams in the Financial Industry. Nonlinear World. 2025. V. 23. № 3. P. 6–14. DOI: https:// doi.org/10.18127/ j20700970-202503-02 (In Russian)

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