AI-Based Decision Support for Asset Allocation Management as a Component of Business Models under Market Uncertainty

Abstract

Objectives The aim of the study was to identify and evaluate the possibilities of using artificial intelligence assistants to support decision-making processes related to asset allocation, treated as an important component of the business model of an enterprise operating under conditions of market uncertainty. Material and methods The study was conducted on the example of three companies from the construction sector listed on the Warsaw Stock Exchange: Budimex S.A., Polimex Mostostal S.A., and Mirbud S.A. The analysis covered the years 2020–2024 and was based on two groups of data: annual fundamental indicators and daily closing stock prices. In the fundamental part, return on equity, debt ratio, asset utilization ratio, and the Altman discriminant model were used to construct a synthetic fundamental index. In the market part, daily logarithmic rates of return and the Sharpe ratio were calculated. Results The results indicate that AI assistants can effectively support the analytical process by accelerating calculations and increasing the repeatability of the procedure. At the same time, differences in model performance were observed, indicating the risk of errors in data interpretation. Conclusions The study demonstrates that AI can be a useful tool supporting asset allocation management; however, its effectiveness depends on data quality, the precision of the analytical procedure, and human supervision. The findings highlight the importance of control mechanisms and model triangulation in AI-supported decision-making processes.