Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

Please note that all times are shown in the time zone of the conference. The current conference time is: 30th Sept 2023, 09:19:19am CEST

Session Overview
3B: Information in non-equity markets
Tuesday, 16/May/2023:
10:00am - 11:30am

Session Chair: Jessica Jeffers
Location: Room Z012

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The Value of Data to Fixed Income Investors

Jian Li1, Jennie Bai2, Asaf Manela3

1Columbia University, United States of America; 2Georgetown University; 3Reichman University and Washington University of St. Louis,

Discussant: Sean Seunghun Shin (Aalto University)

Using a structural model, we estimate the value of data to fixed income investors

and study its main drivers. In the model, data is more valuable for bonds that are

volatile and for which price-insensitive liquidity trades are more likely. Empirically, we

find that the value of data on corporate bonds increases with yield, time-to-maturity,

size, callability, liquidity, and uncertainty during normal times. However, these cross-

sectional differences vanish as the value of data falls during financial crises. Using a

regression discontinuity based on maturity, we provide causal evidence that investor

composition affects the value of data.

The Adoption of Artificial Intelligence by Venture Capitalists

Maxime Bonelli

HEC Paris, France

Discussant: Gilles Chemla (Imperial College Business School, CNRS, and CEPR)

I study how the adoption of artificial intelligence (AI) by venture capitalists (VCs) to screen startups affects the funding of early-stage companies. Using global data on VC investments, I show that after adopting AI, VCs tilt their portfolios towards startups whose business is similar to those already tested by past startups. Within this pool of startups, AI-empowered VCs become better at picking those that survive and receive follow-on funding. At the same time, these VCs' investments become 18% less likely to result in breakthrough success. I exploit plausibly exogenous variation in VCs' incentives to automate screening from the introduction of Amazon’s Web Services to establish causality between AI adoption and the above effects. Overall, my results are consistent with AI exploiting past data that are not informative about breakthrough companies. AI adoption by investors may therefore reduce the capital directed towards innovation.

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