Descripción del proyecto
Cuando hay demasiados datos para gestionar
El rápido crecimiento en la variedad y la cantidad de datos (abundancia de datos) ha sido muy superior al uso de tecnologías de inteligencia artificial (IA o AI, por sus siglas en inglés) para gestionarlos. El proyecto financiado con fondos europeos FIDAI estudiará cómo esta evolución influye en la calidad y el tipo de información producida en los mercados financieros. Por ejemplo, ¿influye en la relación señal/ruido del precio de los valores y la asignación de capital? FIDAI responderá a esta y otras preguntas mediante una investigación basada en métodos analíticos y empíricos. Creará así una teoría de la extracción de datos óptima dedicada a la calidad de los factores de predicción utilizados por los inversores. Analizará posibles implicaciones para la calidad y la naturaleza de la información sobre los mercados financieros y sus efectos en las inversiones corporativas.
Objetivo
"Thierry Foucault ERC ADG 2020
Recent years have witnessed a massive growth in the volume and variety of data (""Data Abundance""). In parallel, thanks to progress in computing power, new techniques (“Artificial Intelligence”) have emerged to harness the information in these data. How does this evolution affect the quality and nature of information produced in financial markets? Is there a risk that it induces investors to trade on noisier signals about economic fundamentals? How does it change the horizon at which information is produced? How does it affect the signal-to-noise ratio in securities prices and capital allocation? This project will break new ground on these unanswered, yet fundamental, questions using a combination of analytical and empirical analyses.
First, I will develop a theory of optimal data mining to disentangle the effect of Data Abundance (DA) from the effect of Artificial Intelligence (AI) on the quality of predictors used by investors. I will then (i) use this theory to make predictions about the effects of AI and DA on the average quality of investors’ predictors, the dispersion of this quality across investors, and the informativeness of securities prices about economic fundamentals and (ii) test some of these predictions.
Second, I will study theoretically and empirically (i) the effects of DA and AI on agents’ allocation of their resources for information production between two tasks: forecasting short-term cash-flows and forecasting long-term cash-flows and (ii) the consequence of these effects for the maturity of corporate investments. In particular, I will focus on whether there is a risk that DA and AI make securities prices more informative about conventional investment projects (whose cash-flows are realized quickly) rather than innovative investment projects (whose cash-flows are realized more slowly), thereby reducing capital available for innovation.
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Ámbito científico
- natural sciencescomputer and information sciencesartificial intelligence
- humanitiesphilosophy, ethics and religionphilosophyhistory of philosophycontemporary philosophy
- social scienceseconomics and businesseconomicsmonetary and finances
- natural sciencescomputer and information sciencesdata sciencedata mining
Programa(s)
Régimen de financiación
ERC-ADG - Advanced GrantInstitución de acogida
78350 Jouy-En-Josas
Francia