Skip to main content
SpringerLink
Log in

Overconfidence and risk seeking in credit markets: an experimental game

  • Original Paper
  • Published:
Review of Managerial Science Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  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.

References

  • Abdellaoui M, Bleichrodt H, L’Haridon O (2008) A tractable method to measure utility and loss aversion under prospect theory. J Risk Uncertain 36:245–266

    Article  Google Scholar 

  • Abdellaoui M, Bleichrodt H, Kammoun H (2013) Do financial professionals behave according to prospect theory? An experimental study. Theory Decis 74(3):411–429

    Article  Google Scholar 

  • Acharya V, Naqvi H (2012) The seeds of a crisis: a theory of bank liquidity and risk taking over the business cycle. J Financ Econ 106:349–366

    Article  Google Scholar 

  • Altman EI (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J Finance 23(4):589–609

    Article  Google Scholar 

  • Argenti J (1976) Corporate collapse: the causes and symptoms. McGraw-Hill, New York

    Google Scholar 

  • Asch SE (1952) Social psychology. Prentice Hall, New York

    Book  Google Scholar 

  • Barber BM, Odean T (2001) Boys will be boys: gender, overconfidence, and common stock investment. Q J Econ 116(1):261–292

    Article  Google Scholar 

  • Barber BM, Odean T (2002) Online investors: do the slow die first? Review of Financial Studies 15:455–489

    Article  Google Scholar 

  • Barberis N, Huang M (2008) Stocks as lotteries: the implications of probability weighting for security prices. Am Econ Rev 98(5):2066–2100

    Article  Google Scholar 

  • Beaver W (1966) Market prices, financial ratios and the prediction failure. Journal of Accounting Research 6:179–192

    Article  Google Scholar 

  • Benartzi S, Thaler RH (1995) Myopic loss aversion and the equity premium puzzle. Q J Econ 110(1):73–92

    Article  Google Scholar 

  • Berger AN, Mester LJ (1997) Inside the black box: what explains differences in the efficiencies of financial institutions? J Bank Finance 21:895–947

    Article  Google Scholar 

  • Berger AN, Kick T, Schaeck K (2012), Executive board composition and bank risk taking, Deutsche Bundesbank Discussion Paper No. 03/2012

  • Biais B, Hilton D, Mazurier K, Pouget S (2005) Judgmental overconfidence, self-monitoring and trading performance in an experimental financial market, Rev Econ Stud 287–312

  • Boyer BH, Mitton T, Vorkink K (2010) Expected idiosyncratic skewness. Rev Financ Stud 23(1):169–202

    Article  Google Scholar 

  • Burnside C, Han B, Hirsleifer D, Wang TY (2011) Investor overconfidence and the forward premium puzzle. Rev Econ Stud 78:523–558

    Article  Google Scholar 

  • Camerer C, Lovallo D (1999) Overconfidence and excess entry: an experimental approach. Am Econ Rev 89(1):306–318

    Article  Google Scholar 

  • Daniel KD, Hirshleifer D, Subrahmanyam A (1998) Investor psychology and security market under- and overreactions. J Finance 53(6):1839–1885

    Article  Google Scholar 

  • De Freitas S, Oliver M (2006) How can exploratory learning with games and simulations within the curriculum be most effectively evaluated? Comput Educ 46(3):249–264

    Article  Google Scholar 

  • Deaves R, Luders E, Luo GY (2009) An experimental test of the impact of overconfidence and gender on trading activity. Rev Finance 13(3):555–575

    Article  Google Scholar 

  • Demsetz H (1973) Industry structure, market rivalry and public policy. J Law Econ 16(1):1–9

    Article  Google Scholar 

  • Deshmukh S, Goel AM, Howe KM (2010) CEO overconfidence and dividend policy. SSRN eLibrary

  • Djankov S, McLiesh C, Shleifer A (2005) Private credit in 129 countries, NBER Working Paper Series, w11078. Available at SSRN: http://ssrn.com/abstract=652366

  • Fahlenbrach R, Stulz RM (2011) Bank CEO incentives and the credit crisis. J Financ Econ 99(1):11–26

    Article  Google Scholar 

  • Forsythe R, Nelson F, Neumann GR, Wright J (1992) Anatomy of an experimental political stock market. Am Econ Rev 82(5):1142–1161

    Google Scholar 

  • Glaser M, Weber M (2007) Overconfidence and trading volume. Geneva Risk Insur Rev 32:1–36

    Article  Google Scholar 

  • Glaser M, Langer T, Weber M (2013) True overconfidence in interval estimates: evidence based on a new measure of miscalibration. J Behav Decis Mak 26:405–417

    Article  Google Scholar 

  • Graham JR, Harvey CR, Huang H (2009) Investor competence, trading frequency, and home bias. Manage Sci 55(7):1094–1106

    Article  Google Scholar 

  • Gredler ME (2004) Games and simulations and their relationships to learning. In: Jonassen DH (ed) Handbook of research on educational communications and technology, vol 2. Erlbaum, Mahwah, pp 571–581

    Google Scholar 

  • Grinblatt M, Keloharju M (2009) Sensation seeking, overconfidence and trading activity. J Finance 64(2):549–578

    Article  Google Scholar 

  • Hens T, Bachmann K (2008) Behavioural finance for private banking. Wiley, New York

    Google Scholar 

  • Hilton D, Régner I, Cabantous L, Charalambides L, Vautier S (2011) Do positive illusions predict overconfidence in judgment? A test using interval production and probability evaluation measures of miscalibration. J Behav Decis Mak 24:117–139

    Article  Google Scholar 

  • Huang S-Y, Lambertides N, Steeley JM (2012) Cash hoards and managerial overconfidence. In: 19th Annual Conference of the Multinational Finance Society, Krakow, June 2012

  • Kahneman D, Lovallo D (1993) Timid choices and bold forecasts: a cognitive perspective on risk taking. Manage Sci 39(1):17–31

    Article  Google Scholar 

  • Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometrica 47(2):263–291

    Article  Google Scholar 

  • Keen S (2011) A monetary Minsky model of the great moderation and the great recession. J Econ Behav Organ. doi:10.1016/j.jebo.2011.01.010

    Google Scholar 

  • Keys BJ, Mukherjee T, Seru A, Vig V (2010) Did securitization lead to lax screening? Evidence from subprime loans. Q J Econ 125(1):307–362

    Article  Google Scholar 

  • Knetsch JL (1989) The endowment effect and evidence of nonreversible indifference curves. Am Econ Rev 79(5):1277–1284

    Google Scholar 

  • Levitt SD, List JA (2007) What do laboratory experiments measuring social preferences tell us about the real world? J Econ Perspect 21(2):153–174

    Article  Google Scholar 

  • Levitt SD, List JA (2009) Field experiments in economics: the past, the present, and the future. Eur Econ Rev 53:1–18

    Article  Google Scholar 

  • List JA (2003) Does market experience eliminate market anomalies? Q J Econ 118:41–71

    Article  Google Scholar 

  • List JA (2009) An introduction to field experiments in economics. J Econ Behav Organ 70:439–442

    Article  Google Scholar 

  • Lunenburg FC (2010) Group decision making: the potential for groupthink. Int J Manag Bus Adm 13(1):1–6

    Google Scholar 

  • Malmendier U, Tate G (2005a) CEO overconfidence and corporate investment. J Finance 60(6):2661–2700

    Article  Google Scholar 

  • Malmendier U, Tate G (2005b) Does overconfidence affect corporate investment? CEO Overconfidence measures revisited. Eur Financ Manag 11(5):649–659

    Article  Google Scholar 

  • Milgram S (1963) Behavioral study of obedience. J Abnorm Soc Psychol 67(4):371–378

    Article  Google Scholar 

  • Milgram S (1974) Obedience to authority: an experimental view. Harpercollins, New York

    Google Scholar 

  • Moore PG (1977) The manager’s struggle with uncertainty. J R Stat Soc Series A 149:129–165

    Article  Google Scholar 

  • Moore DA, Healy PJ (2008) The trouble with overconfidence. Psychol Rev 115(2):502–517

    Article  Google Scholar 

  • Odean T (1998) Volume, volatility, price and profit when all traders are above average. J Finance 53(6):1887–1934

    Article  Google Scholar 

  • Odean T (1999) Do investors trade too much? Am Econ Rev 89(5):1279–1298

    Article  Google Scholar 

  • Peón D, Calvo A (2012) Using behavioral economics to analyze credit policies in the banking industry. Eur Res Stud 15(3):135–144

    Google Scholar 

  • Peón D, Calvo A, Antelo M (2014) A short test for overconfidence and prospect theory: an experimental validation, MPRA working paper no. 57899. Available at http://mpra.ub.uni-muenchen.de/57899/

  • Peón D, Antelo M, Calvo A (2015) On informational efficiency of the banking sector: a behavioral model of the credit boom. Stud Econ Finance 32(2), forthcoming

  • Prelec D (1998) The probability weighting function. Econometrica 66(3):497–527

    Article  Google Scholar 

  • Presbitero AF, Udell GF, Zazzaro A (2012) The home bias and the credit crunch: a regional perspective, MoFiR workshop on banking, VoxEu.org 12-feb-2012. http://www.voxeu.org/index.php?q=node/7614

  • Rabin M (2000) Risk aversion and expected-utility theory: a calibration theorem. Econometrica 68(5):1281–1292

    Article  Google Scholar 

  • Rieger MO, Wang M (2008) Prospect theory for continuous distributions. J Risk Uncertain 36:83–102

    Article  Google Scholar 

  • Rodríguez M (2000) Métodos y modelos de pronóstico del fracaso empresarial: Una aproximación empírica a la realidad empresarial de la CC.AA de Galicia, Doctoral thesis, Universidade da Coruña

  • Roll R (1986) The hubris hypothesis of corporate takeovers. J Bus 59:197–216

    Article  Google Scholar 

  • Rötheli TF (2012) Oligopolistic banks, bounded rationality, and the credit cycle. Econ Res Int. doi:10.1155/2012/961316

    Google Scholar 

  • Samuelson W, Zeckhauser R (1988) Status quo bias in decision making. J Risk Uncertain 1(1):7–59

    Article  Google Scholar 

  • Scheinkman JA, Xiong W (2003) Overconfidence and speculative bubbles. J Polit Econ 111(6):1183–1219

    Article  Google Scholar 

  • Shefrin H (2008) Ending the management illusion: How to drive business results using the principles of behavioral finance, 1st edn. Mc-Graw Hill, New York

    Google Scholar 

  • Shefrin H, Cervellati E (2011) BP’s failure to debias: underscoring the importance of Behavioral Corporate Finance, WP available at SSRN. http://ssrn.com/abstract=1769213

  • Shefrin H, Statman M (1985) The disposition to sell winners too early and ride losers too long: theory and evidence. J Finance 40:777–790

    Article  Google Scholar 

  • Shiller RJ (1984) Stock prices and social dynamics. Brookings papers on economic activity, Fall, pp 457–498

    Google Scholar 

  • Smith VL (2001) Experimental economics. In: Smelser NJ, Baltes PB (eds) International Encyclopedia of the Social & Behavioral Sciences. Elsevier, Amsterdam, pp 5100–5108

  • Soll JB, Klayman J (2004) Overconfidence in interval estimates. J Exp Psychol Learn Mem Cogn 30(2):299–314

    Article  Google Scholar 

  • Stiglitz JE, Weiss A (1981) Credit rationing in markets with imperfect information. Am Econ Rev 71:393–410

    Google Scholar 

  • Thaler RH (1980) Toward a positive theory of consumer choice. J Econ Behav Organ 1(1):39–60

    Article  Google Scholar 

  • Thaler RH, Johnson EJ (1990) Gambling with the house money and trying to break even: the effects of prior outcomes on risky choice. Manag Sci 36(6):643–660

    Article  Google Scholar 

  • Townsend R (1979) Optimal contracts and competitive markets with costly state verification. J Econ Theory 21:265–293

    Article  Google Scholar 

  • Tversky A, Kahneman D (1991) Loss aversion in riskless choice: a reference-dependent model. Q J Econ 106(4):1039–1061

    Article  Google Scholar 

  • Tversky A, Kahneman D (1992) Advances in prospect theory: cumulative representation of uncertainty. J Risk Uncertain 5(4):297–323

    Article  Google Scholar 

  • van de Kuilen G, Wakker PP (2006) Learning in the Allais paradox. J Risk Uncertain 33:155–164

    Article  Google Scholar 

  • Viera AJ, Bangdiwala SI (2007) Eliminating bias in randomized controlled trials: importance of allocation concealment and masking. Fam Med 39(2):132–137

    Google Scholar 

Download references

Acknowledgments

The authors wish to thank the Editor of Review of Managerial Science and two anonymous referees for their insightful comments and suggestions. They also thank Andrea Ceschi and Paulino Martínez for very valuable support in the experiment design, and Enrico Cervellati, Xosé Manuel M. Filgueira and Rafael García for technical assistance. M.A. appreciates the financial aid received from the Galician Autonomous Government through project GPC 2013-045.

Author information

Authors and Affiliations

  1. Grupo BBVA, A Coruña, Spain

    David Peón

  2. Department of Economics, University of Santiago de Compostela, Campus Norte, 15782, Santiago de Compostela, Spain

    Manel Antelo

  3. Department of Financial Economics and Accountancy, University of A Coruna, Campus Elviña s/n, 15071, A Coruña, Spain

    David Peón & Anxo Calvo

Authors
  1. David Peón
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manel Antelo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anxo Calvo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David Peón.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 527 kb)

Appendix

Appendix

IBM SPSS Statistics version 21 was used for the statistical analysis. Technical specifications for the analyses and raw data for the strategies implemented by participants in the experiment are described in the Supplementary Material to this paper.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Peón, D., Antelo, M. & Calvo, A. Overconfidence and risk seeking in credit markets: an experimental game. Rev Manag Sci 10, 511–552 (2016). https://doi.org/10.1007/s11846-015-0166-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11846-015-0166-8

Keywords

JEL Classification

Search

Navigation