Hybrid stochastic-agent method for forecasting stock prices for the Russian market

In this work, we propose a new approach to forecasting stock prices for Russian companies based on a combination of two methods: stochastic modeling, which accounts for random market fluctuations, and an agent-based approach that reflects the behavior of various market participants. This hybrid method enables a more effective consideration of the unique characteristics of the Russian market, where volatility, external economic and political factors, and interactions among investors play a significant role. The developed model is designed to reduce forecasting errors, making it more precise and useful for making investment decisions under conditions of market instability. Moreover, the approach is particularly relevant in today’s economic climate, where rapidly changing conditions require prompt and flexible responses. The significance of this work lies in its potential to create a tool that provides a more reliable assessment of risks and opportunities, thereby enhancing investor confidence and promoting sustainable growth in the financial market. Additionally, the proposed model can aid in more informed policy-making and strategic planning, ensuring that market participants are better equipped to adapt to evolving market dynamics.