Summary
"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.
"
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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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101018214 |
| Start date: | 01-07-2021 |
| End date: | 30-06-2026 |
| Total budget - Public funding: | 1 098 700,00 Euro - 1 098 700,00 Euro |
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Original description
"Thierry Foucault ERC ADG 2020Recent 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.
"
Status
SIGNEDCall topic
ERC-2020-ADGUpdate Date
27-04-2024
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