Предсказательная точность целевых цен акций: сравнение прогнозов аналитиков и машинного обучения

Финансы и бизнес. 2025. Т. 21. № 3. С. 34–50.

Федоров Н. С.

Currently, the role of artificial intelligence is increasingly playing a significant role in various fields, including the increasing role of machine learning in finance. On the other hand, company valuation remains an important part of research due to its difficulty in correctly predicting the accuracy of target stock prices. This study provides an analysis of the comparison of the predictive accuracy of target stock prices using the cash flow discounting (DCF) model based on artificial intelligence algorithms, as well as forecasts from Zacks Investment Research analytical reports. Control variables are used to assess the sustainability of results in the study. As a result of the research, the effectiveness of the use of machine learning in predicting company stock prices has been confirmed. The study noted a slight difference in the accuracy of target prices and projected returns of the DCF model and Zacks analytical reports.

Keywords: искусственный интеллект, DCF, Stock prices, стоимость акций, artificial intelligence, DCF