Séminaires de Recherche

Finance

Intervenant : Xavier Gabaix

13 juin 2019 - T104 - De 14h00 à 15h15


Finance

Intervenant : Adriano Rampini

23 mai 2019 - T105 - De 14h00 à 15h15


Finance

Intervenant : Luke Taylor

16 mai 2019 - T105 - De 14h00 à 15h15


Finance

Intervenant : Jessica Jeffers

18 avril 2019 - T104 - De 14h00 à 15h15


Finance

Intervenant : Emil Verner

4 avril 2019 - T104 - De 14h00 à 15h15


Finance

Intervenant : Niels Gormsen

28 mars 2019 - T104 - De 14h00 à 15h15


Finance

Intervenant : Ramona Dagostino

14 mars 2019 - T104 - De 14h00 à 15h15


The Lost Capital Asset Pricing Model

Finance

Intervenant : Julien Cujean

6 décembre 2018 - T104 - De 14h00 à 15h15

Télécharger

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

Intervenant : Andres Liberman

29 novembre 2018 - T004 - De 14h00 à 15h15

Télécharger

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.

Finance

Intervenant : John Zhu

22 novembre 2018 - T004 - De 14h00 à 15h15



JavaScriptSettings