Principles of Constructing a DCF Model in the Age of Artificial Intelligence

The purpose of this work is to formulate the principles for constructing a high-quality DCF model using artificial intelligence and defining the role of a fundamental analyst in the era of AI development. To assess the sophistication of DCF models and determine the place of a fundamental analyst in stock market analysis, data on the valuation of 20 different companies were collected, and documentation on 5 artificial intelligences was analyzed and analyzed. The degree of sophistication and quality of the DCF model was assessed by how reliable the source of financial reporting data is, what type of model and what specifications are used by artificial intelligence, whether the beta coefficient and terminal value are calculated correctly, and whether there is verification. As a result, the following requirements were formulated that artificial intelligence must meet: a set of initial information must regularly come from reliable sources, there must be a uniform methodology for constructing DCF for all companies, and changes can be made to the model. Thus, in the future, when artificial intelligence takes over routine tasks, the analyst will solve creative ones - search for unique information. Key words: discounted cash flow; fundamental analyst; fundamental analysis; artificial intelligence; forecasting; bias