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Review of Managerial Science
Published in: Review of Managerial Science 3/2016

01-07-2016 | Original Paper

Overconfidence and risk seeking in credit markets: an experimental game

Authors: David Peón, Manel Antelo, Anxo Calvo

Published in: Review of Managerial Science | Issue 3/2016

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Abstract

Behavioral biases may influence bank decisions when granting credit to their customers. This paper explores this possibility in an experimental setting, contributing to the literature in two ways. First, we designed a business simulation game that replicates the basic decision-making processes of a bank granting credit to clients under conditions of risk and uncertainty. Second, we implemented a series of short tests to measure participants’ overconfidence and risk profile according to prospect theory and then conduct an experimental implementation of the simulation game. We find that higher levels of overprecision and risk seeking for gains (mostly attributable to distortion of probabilities) foster lower prices and higher volumes of credit, and reduce quality. The most consistent result is that distortion of probabilities affects the ability to discriminate between the quality of borrowers according to objective information, fostering strategies of lower loan prices to lower quality clients. The external validity of the results is also discussed.
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Appendix
Available only for authorised users
Footnotes
1
This literature starts with the seminal articles by Beaver (1966), Altman (1968) and (Argenti 1976). See Rodríguez (2000) for a survey.
 
2
Note that niche clients were different at different periods, but all participants faced an identical niche in the same period. We considered six periods to be enough for the two purposes we implemented a multi-period game: to set a feedback system that allows participants to learn how economic perspectives evolved, and to have a larger data set of strategies implemented by them. Finally, we considered potential borrowers to be applying for a three-year loan in order to make decision-making more complex; thus, in order to grant credit to a niche, participants had to consider the possible economic scenarios for the next three periods, not just one.
 
3
Confidence intervals were given in three-point format: average values and high and low boundaries. Boundaries were explicitly said to be absolute limits that could not be surpassed. That meant, for instance, that if the expected default rate of a niche is (15, 5, 1 %), the highest (lowest) default rate in all possible states of the world is 15 % (1 %). That also meant, for instance, that if in period 1 we said that expected GDP growth for period 5 could range between (−1, 1, 3 %), updated information in periods 2–4 could not say that the expected GDP growth for period 5 might go higher than 3 % or lower than −1 %.
 
4
It is a six-period game, but in every period a three-year loan is granted.
 
5
The demand function was not provided to players, but they were obviously given the outcome Vmax.
 
6
More specifically, they were instructed as follows: “Please note the computer application helps you to calculate the expected profits given the inputs being set (E[m], p, V). Be aware that these are the inputs that you set; the expected profits may be fulfilled or not depending on whether (a) the economic scenario follows the path you anticipated; and (b) the strategy you consequently implemented is indeed optimal. Therefore, be advised, when setting your strategies, that the expected profits are just an aid. On one hand, not granting credit, \(V = 0\), when you think a niche may not generate profits allows you to save a fixed cost of 3 euros. On the other hand, if you decide to give credit, granting \(V = V_{max}\) or a lower volume should depend on how sure you are this niche client is going to render you profits rather than losses”.
 
7
For indicator estimates (see “Game Indicators” in this Sect. 2.2), when a subject sets \(V = 0\) we set \(p = 20\,\%\), i.e., the price that should be offered to have zero demand, disregarding the actual value the participant set. We did so in order to have indicators that were homogeneous across participants: judges set \(V = 0\) after they tried different prices (sometimes even providing no answer for \(p\)), so the last price they set may not be representative. This correction did not apply to any other case since, as explained, we wanted to observe both price and volume strategies that might not clear the market.
 
8
That income function makes two implicit assumptions. First, a default means the bank recuperates neither interest nor capital from that proportion me of the loan. Second, for simplicity sake and easier interpretation by the players, we assumed the total credit granted to a niche in all 3 years the credit was active to be equal to the initial \(V\) granted. That may be interpreted as a line of credit to a niche of clients that is renewed annually for the total amount, independent of the default rate incurred in any previous year(s). Participants were explicitly informed of both assumptions.
 
9
The original refinement of M, already described, takes estimates of MEAD and MAD based on the beta function that best fits the three point estimates by the respondent. Alternatively, Soll and Klayman (2004) suggest measuring MAD by assuming the median is in the middle of the distribution, denoted M2. A third measure is where both MEAD and MAD computations assume a normal distribution, denoted MN. Only median estimations of these two alternatives were considered since, given the nature of the reliability problem observed in our test, average estimates were shown to be less reliable than medians.
 
10
The fourfold pattern of preferences in prospect theory implies that risk aversion depends on curvature of the value function and probability weighting simultaneously. Additionally, given the inverse S-shape of the weighting function, a given probability distortion implies different risk profiles for low and medium/high probabilities. Consequently, in this paper, whenever we make a statement like “the higher α+ the more risk seeking” we are ignoring the effect of probability weightings, and the reverse.
 
11
We checked the robustness of the effects of overprecision comparing the results we obtained under the alternative refinement methods: the estimator M2 supported that overprecision reduced quality performance (hypothesis 3d) with statistical significance (p < .1), while the estimator that assumes normality, MN, supported that the higher the overprecision the lower the price of credit (hypothesis 3a), but with weak statistical significance (p = .13).
 
12
For simplicity sake, for overprecision in the factorial analysis we only considered the median measure Mmed.
 
13
Again for simplicity sake, for loss aversion in this analysis we only considered the median measure βmed.
 
14
Note that higher γ+ implies more risk seeking only for medium/high probabilities.
 
15
Again, higher γ− implies more risk aversion only for medium/high probabilities.
 
16
We used 125 observations as we excluded one outlier from variable Qvol (which loads on Quality). Additionally, one more observation is lost for OC, since we had a missing value for M ratios from the beginning.
 
17
Having only 6 observations (clusters) would itself invalidate the statistical significance of any correlations or regression analyses. Moreover, much information is lost when we use average values to be representative of all individual observations in a cluster.
 
18
Participants in the experimental sessions spent an average of three hours completing the overconfidence and prospect theory tests and the simulation game, instructions included.
 
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Metadata
Title
Overconfidence and risk seeking in credit markets: an experimental game
Authors
David Peón
Manel Antelo
Anxo Calvo
Publication date
01-07-2016
Publisher
Springer Berlin Heidelberg
Published in
Review of Managerial Science / Issue 3/2016
Print ISSN: 1863-6683
Electronic ISSN: 1863-6691
DOI
https://doi.org/10.1007/s11846-015-0166-8

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