Research Seminars

Finance

Speaker: Xavier Gabaix

13 June 2019 - T104 - From 2:00 pm to 3:15 pm


Finance

6 June 2019 - T004 - From 2:00 pm to 3:15 pm


Finance

Speaker: Adriano Rampini

23 May 2019 - T105 - From 2:00 pm to 3:15 pm


Finance

Speaker: Luke Taylor

16 May 2019 - T105 - From 2:00 pm to 3:15 pm


Finance

Speaker: Jessica Jeffers

18 April 2019 - T104 - From 2:00 pm to 3:15 pm


Finance

Speaker: Emil Verner

4 April 2019 - T104 - From 2:00 pm to 3:15 pm


Finance

Speaker: Niels Gormsen

28 March 2019 - T104 - From 2:00 pm to 3:15 pm


Finance

Speaker: Ramona Dagostino

14 March 2019 - T104 - From 2:00 pm to 3:15 pm


The Lost Capital Asset Pricing Model

Finance

Speaker: Julien Cujean

6 December 2018 - T104 - From 2:00 pm to 3:15 pm

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A flat Securities Market Line is not evidence against the CAPM. In a rational-expectations economy in which markets are not informationally effcient, the CAPM holds but is rejected empirically (Type I Error). There exists an information gap between the empiricist and the average investor who clears the market. The CAPM holds unconditionally for the investor, but appears at to the empiricist who uses the correct unconditional market proxy. This distortion is empirically substantial and offers a new interpretation of why \Betting Against Beta" works: BAB really bets on
true beta. The empiricist retrieves a stronger CAPM on macroeconomic announcement days.

The Equilibrium Effects of Information Deletion: Evidence from Consumer Credit Markets

Finance

Speaker: Andres Liberman

29 November 2018 - T004 - From 2:00 pm to 3:15 pm

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This paper exploits a large-scale natural experiment to study the equilibrium effects of information restrictions in credit markets. In 2012, Chilean credit bureaus were forced to stop reporting defaults for 2.8 million individuals (21% of the adult population). We show that the effects of information deletion on aggregate borrowing and total surplus are theoretically ambiguous and depend on the pre-deletion demand and cost curves for defaulters and non-defaulters. Using panel data on the universe of bank borrowers in Chile combined with the deleted registry information, we implement machine learning techniques to measure changes in lenders’ cost predictions following deletion. Deletion reduces (raises) predicted costs the most for poorer defaulters (non-defaulters) with limited borrowing histories. Using a difference-in-differences design, we find that individuals exposed to increases in predicted costs reduce borrowing by 6.4%, while those exposed to decreases raise borrowing by 11.8% following the deletion, for a 3.5% aggregate drop in borrowing. Using the difference-in-difference estimates as inputs into the theoretical framework, we find evidence that deletion reduced aggregate welfare under a variety of assumptions about lenders’ pricing strategies.


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