Inteligência Artificial generativa e valor da empresa

Como a tecnologia de inteligência artificial generativa pode afetar o valor das empresas:

What are the effects of recent advances in Generative AI on the value of firms? Our study offers a quantitative answer to this question for U.S. publicly traded companies based on the exposures of their workforce to Generative AI. Our novel firm-level measure of workforce exposure to Generative AI is validated by data from earnings calls, and has intuitive relationships with firm and industry-level characteristics. Using Artificial Minus Human portfolios that are long firms with higher exposures and short firms with lower exposures, we show that higher-exposure firms earned excess returns that are 0.4% higher on a daily basis than returns of firms with lower exposures following the release of ChatGPT. Although this release was generally received by investors as good news for more exposed firms, there is wide variation across and within industries, consistent with the substantive disruptive potential of Generative AI technologies. 

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Qualidade dos modelos de avaliação

The key purpose of corporate finance is to provide methods to compute the value of projects. The baseline textbook recommendation is to use the Present Value (PV) formula of expected cash flows, with a discount rate based on the CAPM. In this paper, we ask what is, empirically, the best discounting method. To do this, we study listed firms, whose actual prices and expected cash flows can be observed. We compare different discounting approaches on their ability to predict actual market prices. We find that discounting based on expected returns (such as variants on the CAPM or multi-factor model), performs very poorly. Discounting with an Implied Cost of Capital (ICC), imputed from comparable firms, obtains much better results. In terms of pricing methods, significant, but small, improvements can be obtained by allowing, in a simple and actionable way, for a more flexible term structure of expected returns. We benchmark all of our results with flexible, purely statistical models of prices based on Random Forest algorithms. These models do barely better than NPV-based methods. Finally, we show that under standard assumptions about the production function, the value loss from using the CAPM can be sizable, of the order of 10%.

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