Концепция модели прогнозирования признаков изменения цены акций, предназначенной для управления рисками нефинансовых организаций

V. A. Vostrov; M. A. Gorelikov; M. E. Amelin

Vol 6 No 3 (2025), Papers

Vol 6 No 3 (2025)

A Concept of a Model for Forecasting Indicators of Share Price Changes to Manage Risk in Non-Financial Organizations

Papers

Published September 30, 2025

V. A. Vostrov^∗⁻
M. A. Gorelikov^∗⁻
M. E. Amelin^∗⁻

V. A. Vostrov

Surgut Branch of Scientific Research Institute for System Analysis of the National Research Centre “Kurchatov Institute”, Surgut, Russian Federation

https://orcid.org/0009-0005-1647-4416

M. A. Gorelikov

Surgut Branch of Scientific Research Institute for System Analysis of the National Research Centre “Kurchatov Institute”, Surgut, Russian Federation; LETI Saint Petersburg Electrotechnical University, Saint Petersburg, Russian Federation

https://orcid.org/0009-0004-6196-5735

M. E. Amelin

Surgut State University, Surgut, Russian Federation

https://orcid.org/0009-0008-9831-2112

PDF (Russian)

Keywords

artificial intelligence methods
intelligent financial risk management models
AI-based investment management
artificial intelligence for finance

How to Cite

1.

Vostrov V.A., Gorelikov M.A., Amelin M.E. A Concept of a Model for Forecasting Indicators of Share Price Changes to Manage Risk in Non-Financial Organizations // Russian Journal of Cybernetics. 2025. Vol. 6, № 3. P. 20–26.

Abstract

we developed a framework for an intelligent model based on neural networks to support proactive management of financial risks in non-financial companies. We collected financial reporting data from disclosure websites, news feeds from verified sources, and market data through the Moscow Exchange (MOEX) API, which provides historical quotes, trades, positions of trading participants, and other information. We analyzed current practices and identified prerequisites for events that may influence the price dynamics of selected financial instruments. We designed and justified the model’s architecture and selected solutions, taking into account the specific characteristics of the Russian market. The framework integrates multiple data sources to improve risk prediction and support decision-making in financial risk management.

PDF (Russian)

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