Behavioral economics and its related field, behavioral finance (BF), has been able to explain a wide array of anomalies, observed phenomena that mainstream economics is not able to explain. This field, based in insights of other social areas like psychology or sociology, has succeeded so much to improve our understanding of empirical data and real behavior of market participants (particularly in financial markets), that BF itself is becoming mainstream today. An evidence for that are the recent Nobel prizes by Kahneman (prospect theory) and Smith (experimental economics) in 2002, and Robert Shiller (asset pricing and behavioral finance) in 2013.
The main objective of my PhD investigation is to analyze the behavioral microfoundations of retail credit markets. Most behaviorist researchers come from the US where market-based financial systems move the economy. However, in Europe (and Eurozone in particular) most financial operations come through the banking system. The financial crisis of 2008-9 was, at least in Europe, mostly a result of a credit boom fostered by the banking industry. The classic explanations for this misbehavior (with which I agree) are moral hazard, the effect of incentives and a deficient regulation. In my PhD investigation I want to go further: analyzing the effects that behavioral biases of participants in the industry (CEOs, employees, authorities…) could have over credit policies implemented. In particular, whether just behavioral biases could be able to explain a credit boom -though we agree the alternative explanations would be still relevant.
For that purpose, I have already developed a model of banking competition (here a brief description) where the only presence of a biased bank (biased in terms of excessive optimism and/or overconfidence) is a sufficient condition for a bubble to be generated. The dynamic extension of this model predicts pessimism is not as pervasive as excessive optimism (i.e., financial crises are seeded during good times, when overconfident agents make too risky decisions, rather than during recessions) and that low-quality niche markets are more exposed to potential pervasive effects of bounded rationality. Both results of the model are validated by empiric observation.
The second section of the thesis is devoted to an experimental research. In particular, the purpose is to analyze the behavioral profile of participants in the experiment according to two fields: Prospect theory (theory and tests by Tversky and Kahneman, 1992; tests by Abdellaoui et al., 2008) and Overconfidence (theory and tests by Moore and Healy, 2008; tests by Soll and Klayman, 2004). Then, once participants have been described in terms of their risk profile (prospect theory) and different measures of overconfidence, we test the effects that different PT and OC profiles might have had over the policies implemented by the same participants in a strategy game.
The strategy game they played consists of an experimental simulation of a retail credit market. The basics of the game are as follows. Each participant runs a bank that grants credit to costumers. They play an individual, multiperiod game (same information but no interaction) with the purpose of setting a strategy defined as “price and volume of credit granted” to several costumers, given information available. Information about economic perspectives and quality of the new customer is updated every period.
The goal for participants in the experiment was to implement the best strategy (in terms of profit maximization). The winner of each experimental session (5 in total) was granted a prize of 60 eur. The goal of the experimenter, instead, was to analyze their decisions in a context of uncertainty and risk, and determine whether their profiles (in terms of PT and OC) are able to explain some decisions they made in terms of three types of indicators (seven indicators in total, which measure different aspects of prize, volume and quality of credit).
In subsequent and separate posts we will describe some of the basic results we obtained in terms of priors, risk profile and overconfidence of respondents, as well as in terms of the credit policies they implemented in the game.