Project description
When data is too much to handle
Today’s fast growth of volume and variety of data (data abundance) has not been matched by the use of AI technologies to harness this data. The EU-funded FIDAI project will study how this evolution affects the quality and character of information produced in financial markets. For instance, does it impact the signal-to-noise ratio in securities prices and capital allocation? FIDAI will answer this question, among others, based on combined analytical and empirical research. It will create a theory of optimal data mining for the quality of predictors used by investors. It will analyse possible implications for the quality and nature of information on financial markets and their effects on corporate investment.
Objective
"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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Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
ERC-ADG - Advanced Grant
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2020-ADG
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
78350 Jouy-En-Josas
France
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.