Стас Худяков, Dinets Daria Aleksandrovna, Sergey Barykin
This paper presents a methodology for forming an adaptive investment portfolio structure based on a multi-module architecture implemented using an ensemble of neural network models. The developed system comprises five specialized analytical modules, each responsible for processing a specific class of factors: macroeconomic, fundamental, technical, and credit. The central element of the architecture is a neural network model for forecasting the Bank of Russia key interest rate, which defines scenario conditions for the valuation of all asset classes. Fundamental equity analysis, technical analysis of market dynamics, credit risk assessment of debt instruments, and an adapted Markowitz portfolio optimization model are integrated within a digital twin that reconciles the outputs of individual modules. The digital twin performs the functions of coordinating and aggregating the ensemble results, identifying inconsistencies between partial recommendations, and generating a coherent investment decision. The system produces probabilistic investment recommendations, thereby enabling scenario analysis and quantitative risk assessment. The proposed approach can be used as a decision-support tool in both individual and institutional asset management, taking into account changes in the macroeconomic environment.