NATIONAL BUREAU OF ECONOMIC RESEARCH
NATIONAL BUREAU OF ECONOMIC RESEARCH

Behavioral Finance Program Meeting

March 21, 2009
Markus Brunnermeier and Stefan Nagel, Organizers

Mark Dean, New York University
Status Quo Bias in Large and Small Choice Sets

Dean introduces models of status quo bias based on the concept of decision avoidance, by which a decisionmaker may select the status quo in order to avoid a difficult decision. These models capture the experimental finding that the status quo is more frequently chosen in larger choice sets. This phenomenon violates the predictions of current preference-based models of status quo bias that assume a decisionmaker with a fixed status quo will make consistent choices. Using laboratory experiments, Dean shows that subjects in large choice sets do exhibit behavior in line with decision avoidance, while in small choice sets, preference-based models offer a better explanation of behavior. These findings raise questions for advocated policies of “benign paternalism.”


Nicholas Barberis, Yale University and NBER
A Model of Casino Gambling

Casino gambling is a hugely popular activity around the world, but there are still very few models of why people go to casinos or of how they behave when they get there. In this paper, Barberis shows that prospect theory can offer a rich theory of gambling, one that captures many features of actual gambling behavior. First, he demonstrates that, for a wide range of parameter values, a prospect theory agent would be willing to gamble in a casino, even if the casino only offers bets with zero or negative expected value. Second, he shows that prospect theory predicts a plausible time inconsistency: at the moment he enters a casino, a prospect theory agent plans to follow one particular gambling strategy; but after he enters, he wants to switch to a different strategy. The model therefore predicts heterogeneity in gambling behavior: how a gambler behaves depends on whether he is aware of this time-inconsistency; and, if he is aware of it, on whether he is able to commit, in advance, to his initial plan of action.


Shimon Kogan, University of Texas, Anthony M. Kwasnic, Pennsylvania State University, and Roberto Wseber, Carnegie Mellon University
Coordination in the Presence of Asset Markets

Kogan and his co-authors explore how final prices and security holdings in an asset market influence and forecast behavior and outcomes in an affiliated coordination game. The researchers vary the incentives from the market relative to payoffs from the game, the number of players in a group, and whether traders' payoffs are influenced by outcomes in their own or another group. Markets lead to significantly less efficient group outcomes across all treatments, even when the market produces little or no distortion of incentives in the game. At the same time, the authors find that asset markets are informative about group outcomes and thereby reduce “wasted input." Their experiment therefore may shed light on how financial markets themselves contribute to economic crises.

Marianne Bertrand, University of Chicago and NBER, and Adair Morse, University of Chicago
Information Disclosure, Cognitive Biases and Payday Borrowing

If people face cognitive limitations or biases that lead to financial mistakes, how can lawmakers possibly help? One approach is to remove the option of the bad decision; another approach is to increase financial education, such that individuals can reason through choices when they arise. A third, less discussed, approach is to mandate disclosure of information in a form that enables people to overcome limitations or biases at the point of the decision. This third approach is the topic of the paper by Bertrand and Morse. They study whether and what information can be disclosed to payday loan borrowers to lower their use of high-cost debt. Their information comes from a field experiment at a national chain of payday lenders. They find that information that helps people think less narrowly (over time) about the cost of payday borrowing, and in particular information that reinforces the adding-up effect over pay cycles of the dollar fees incurred on a payday loan, reduces the take-up of payday loans. They also find substantial heterogeneity in the effectiveness of information disclosure across categories of borrowers: information disclosure appears more effective among more self-controlled individuals, individuals with some college education (but not a college degree) and individuals whose average borrowing-to-income ratio is low. Overall, their results suggest that consumer information regulations based on a deeper understanding of cognitive biases might be an effective policy tool when it comes to payday borrowing, and possibly other financial products.


Wei Xiong, Princeton University and NBER, and Jialin Yu, Columbia University
The Chinese Warrants Bubble

In 2005-8, over a dozen put warrants traded in China went so deep out of the money that they were certain to expire worthless. Nonetheless, these warrants attracted a speculative frenzy: for each warrant, billions of Yuan traded with an average daily turnover rate close to 300 percent, and at substantially inflated prices. The publicly observable underlying stock prices make the zero-warrant fundamentals common knowledge to all market participants. This warrants bubble thus presents a unique opportunity for studying bubble mechanisms, which previously had only been available in laboratory environments. Xiong and Yu find evidence supporting the resale option theory of bubbles: investors overpay for a warrant hoping to resell it at an even higher price to a greater fool.


Uday Rajan, University of Michigan, Amit Seru, University of Chicago, and Vikrant Vig, London Business School
The Failure of Models That Predict Failure: Distance, Incentives, and Defaults

Using data on securitized subprime mortgages issued in the period 1997-2006, Rajan and his co-authors demonstrate that, as the degree of securitization increases, interest rates on new loans rely increasingly on hard information about borrowers. As a result, a statistical default model fitted in a low securitization period breaks down in the high securitization period in a systematic manner: it under-predicts defaults among borrowers for whom soft information is more valuable (that is, borrowers with low documentation, low FICO scores, and high loan-to-value ratios). The researchers rationalize these findings in a theoretical model that highlights a reduction in lenders' incentives to collect soft information as securitization becomes common, resulting in worse loans being issued to borrowers with similar hard information characteristics. These results partly explain why statistical default models severely underestimated defaults during the subprime mortgage crisis, and imply that these models are subject to a Lucas critique. Regulations that rely on such models to assess default risk therefore may be undermined by the actions of market participants.

 
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