Individual Differences in Investor Decision-making: Examining Representativeness Heuristics and Cognitive Reflection

Document Type : Original Article

Authors

• Ruhollah Amareh ¹
• Esfandyar Malekian ²
• Hossein Fakhari ²

¹ Department of Accounting, Faculty of Economics and Administrative Sciences, University of Mazandaran, Babolsar, Iran.

² Department of Accounting, Faculty of Economics and Administrative Sciences, University of Mazandaran, Babolsar, Iran

10.22067/ijaaf.2023.43844.1310

Abstract

Due to limited cognitive resources, investors often utilize mental shortcuts to make quick judgments. This study examines the impact of representativeness heuristics (Conjunction Fallacy, Gambler's Fallacy, and Stereotypes) and the Cognitive Reflection Test (CRT) on investor decision-making. The population of this study consists of a sample of investors in the Tehran Stock Exchange. The study employs a Chi-Square test (χ^2) to explore the relationship between heuristics and CRT, along with T-tests, one-way ANOVA, and correlation analyses to identify individual differences. Results indicate that proper utilization of cognitive resources can partially prevent the Conjunction Fallacy from occurring. Moreover, investors tend to consider the high probability of consecutive results for an event regardless of cognitive resource usage. Interestingly, this study also found that investors with lower CRT scores made decisions less influenced by stereotypes. We conclude that reducing the impact of representativeness heuristics can be achieved through knowledge and experience gained from similar situations and appropriately utilizing cognitive resources.

Keywords

• Representativeness Heuristic and Conjunction Fallacy
• Gambler’s Fallacy and Stereotypes
• and CRT

Main Subjects

• Finance

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©2023 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0).

References

1. Achtziger, A., Alós-Ferrer, C., Hügelschäfer, S. and Steinhauser, M. (2014). The neural basis of belief updating and rational decision making. Social cognitive and affective neuroscience, 9(1), pp. 55-62. https://doi.org/10.1093/scan/nss099
2. Besedeš, T., Deck, C., Sarangi, S. and Shor, M. (2012). Age effects and heuristics in decision making. Review of Economics and Statistics, 94(2), pp. 580-595. https://doi.org/10.1162/REST_a_00174
3. Bordalo, P., Coffman, K., Gennaioli, N. and Shleifer, A. (2016). Stereotypes. The Quarterly Journal of Economics, 131(4), pp. 1753-1794. https://doi.org/10.1093/qje/qjw029
4. Campitelli, G. and Labollita, M. (2010). Correlations of cognitive reflection with judgments and choices. Judgment and Decision making, 5(3), pp. 182-191. https://doi.org/10.1017/S1930297500001066
5. Cokely, E. T. and Kelley, C. M. (2009). Cognitive abilities and superior decision making under risk: A protocol analysis and process model evaluation. Judgment and Decision making, 4(1), pp. 20-33. https://doi.org/10.1017/S193029750000067X
6. Dilchert, S., Ones, D. S., Davis, R. D. and Rostow, C. D. (2007). Cognitive ability predicts objectively measured counterproductive work behaviors. Journal of Applied Psychology, 92(3), pp. 616. https://doi.org/10.1037/0021-9010.92.3.616
7. Donaldson, L. (2017). When the media misrepresents Black men, the effects are felt in the real world. The Guardian. https://www.theguardian.com/commentisfree/2015/aug/12/media-misrepresents-black-men-effects-felt-real-world
8. Engle, R. W., Tuholski, S. W., Laughlin, J. E. and Conway, A. R. (1999). Working memory, short-term memory, and general fluid intelligence: a latent-variable approach. Journal of experimental psychology: General, 128(3), p. 309. https://doi.org/10.1037//0096-3445.128.3.309
9. Finn, P. R. and Hall, J. (2004). Cognitive ability and risk for alcoholism: short-term memory capacity and intelligence moderate personality risk for alcohol problems. Journal of Abnormal Psychology, 113(4), p. 569. https://doi.org/10.1037/0021-843x.113.4.569
10. Fortune, E. E. and Goodie, A. S. (2012). Cognitive distortions as a component and treatment focus of pathological gambling: a review. Psychology of Addictive Behaviors, 26(2), p. 298. https://psycnet.apa.org/doi/10.1037/a0026422
11. Frederick, S. (2005). Cognitive reflection and decision making. Journal of Economic Perspectives, 19(4), pp. 25-42. https://doi.org/10.1257/089533005775196732
12. Fromlet, H. (2001). Behavioral finance theory and practical application: Systematic analysis of departures from the homo oeconomicus paradigm are essential for realistic financial research and analysis. Business economics, 36(3), pp. 63-69. https://www.jstor.org/stable/23488166
13. Gervais, W. M. and Norenzayan, A. (2012). Analytic thinking promotes religious disbelief. Science, 336(6080), pp. 493-496. https://doi.org/10.1126/science.1215647
14. Gilovich, T. and Savitsky, K. (2002). Like goes with like: The role of representativeness in erroneous and pseudo-scientific beliefs. In T. Gilovich, D. Griffin, and D. Kahneman (Eds.), Heuristics and biases: The psychology of intuitive judgment (pp. 617–624). Cambridge University Press. https://doi.org/10.1017/CBO9780511808098.036
15. Griliches, Z. (1979). Issues in assessing the contribution of research and development to productivity growth. The Bell Journal of Economics, 10(1), pp. 92-116. https://doi.org/10.2307/3003321
16. Griliches, Z. and Mason, W. M. (1972). Education, income, and ability. Journal of Political Economy, 80(3, Part 2), pp. S74-S103. https://doi.org/10.1086/259988
17. Heckman, J. J., Stixrud, J. and Urzua, S. (2006). The effects of cognitive and noncognitive abilities on labor market outcomes and social behavior. Journal of Labor Economics, 24(3), pp. 411-482. https://doi.org/10.1086/504455
18. Hoppe, E. I. and Kusterer, D. J. (2011). Behavioral biases and cognitive reflection. Economics Letters, 110(2), pp. 97-100. https://doi.org/10.1016/j.econlet.2010.11.015
19. Kahneman, D. and Tversky, A. (1972). Subjective probability: A judgment of representativeness. Cognitive psychology, 3(3), pp. 430-454. https://doi.org/10.1016/0010-0285(72)90016-3
20. Kahneman, D. and Tversky, A. (1981). The simulation heuristic. Stanford Univ CA Dept of Psychology. Stanford, California. https://doi.org/10.1017/CBO9780511809477.015
21. Kahneman, D. and Tversky, A. (1983). Can irrationality be intelligently discussed. Behavioral and Brain Sciences, 6(3), pp. 509-510. https://doi.org/10.1017/S0140525X00017246
22. Kirsch, I. S. (1993). Adult literacy in America: A first look at the results of the National Adult Literacy Survey. US Government Printing Office, Superintendent of Documents, Washington, DC 20402 (Stock No. 065-000-00588-3).
23. Kokis, J. V., Macpherson, R., Toplak, M. E., West, R. F. and Stanovich, K. E. (2002). Heuristic and analytic processing: Age trends and associations with cognitive ability and cognitive styles. Journal of Experimental Child Psychology, 83(1), pp. 26-52. https://doi.org/10.1016/S0022-0965(02)00121-2
24. Liberali, J. M., Reyna, V. F., Furlan, S., Stein, L. M. and Pardo, S. T. (2012). Individual differences in numeracy and cognitive reflection, with implications for biases and fallacies in probability judgment. Journal of behavioral decision making, 25(4), pp. 361-381. https://doi.org/10.1002/bdm.752
25. McGloin, J. M. and Pratt, T. C. (2003). Cognitive ability and delinquent behavior among inner-city youth: A life-course analysis of main, mediating, and interaction effects. International Journal of Offender Therapy and Comparative Criminology, 47(3), pp. 253-271. https://doi.org/10.1177/0306624x03047003002
26. Moritz, B. B., Hill, A. V. and Donohue, K. L. (2013). Individual differences in the newsvendor problem: Behavior and cognitive reflection. Journal of Operations Management, 31(1-2), pp. 72-85. https://doi.org/10.1016/j.jom.2012.11.006
27. Murray, C. and Herrnstein, R. J. (1994). Race, genes, and IQ–An apologia. The New Republic, 211(18), pp. 27-37. https://newrepublic.com/article/120887/race-genes-and-iq-new-republics-bell-curve-excerpt
28. Oechssler, J., Roider, A. and Schmitz, P. W. (2009). Cognitive abilities and behavioral biases. Journal of Economic Behavior & Organization, 72(1), pp. 147-152. https://doi.org/10.1016/j.jebo.2009.04.018
29. Pallant, J. (2020). SPSS survival manual: A step by step guide to data analysis using IBM SPSS. Routledge. Oxfordshire, England, UK. https://doi.org/10.4324/9781003117452
30. Parker, A. M. and Fischhoff, B. (2005). Decision‐making competence: External validation through an individual‐differences approach. Journal of Behavioral Decision Making, 18(1), pp. 1-27. https://doi.org/10.1002/bdm.481
31. Paxton, J. M., Ungar, L. and Greene, J. D. (2012). Reflection and reasoning in moral judgment. Cognitive science, 36(1), pp. 163-177. https://doi.org/10.1111/j.1551-6709.2011.01210.x
32. Pennycook, G., Cheyne, J. A., Barr, N., Koehler, D. J. and Fugelsang, J. A. (2015a). On the reception and detection of pseudo-profound bullshit. Judgment and Decision making, 10(6), pp. 549-563. https://doi.org/10.1017/S1930297500006999
33. Pennycook, G., Cheyne, J. A., Seli, P., Koehler, D. J. and Fugelsang, J. A. (2012). Analytic cognitive style predicts religious and paranormal belief. Cognition, 123(3), pp. 335-346. https://doi.org/10.1016/j.cognition.2012.03.003
34. Pennycook, G., Fugelsang, J. A. and Koehler, D. J. (2015). What makes us think? A three-stage dual-process model of analytic engagement. Cognitive psychology, 80, pp. 34-72. https://doi.org/10.1016/j.cogpsych.2015.05.001
35. Peters, E. and Levin, I. P. (2008). Dissecting the risky-choice framing effect: Numeracy as an individual-difference factor in weighting risky and riskless options. Judgment and Decision making, 3(6), pp. 435-448. https://doi.org/10.1017/S1930297500000012
36. Shamosh, N. A. and Gray, J. R. (2008). Delay discounting and intelligence: A meta-analysis. Intelligence, 36(4), pp. 289-305. https://doi.org/10.1016/j.intell.2007.09.004
37. Sirota, M., Juanchich, M. and Hagmayer, Y. (2014). Ecological rationality or nested sets? Individual differences in cognitive processing predict Bayesian reasoning. Psychonomic Bulletin & review, 21(1), pp. 198-204. https://doi.org/10.3758/s13423-013-0464-6
38. Stanovich, K. E. and West, R. F. (1998). Individual differences in rational thought. Journal of experimental psychology: general, 127(2), p. 161. https://psycnet.apa.org/doi/10.1037/0096-3445.127.2.161
39. Stanovich, K. E. and West, R. F. (1999). Discrepancies between normative and descriptive models of decision making and the understanding/acceptance principle. Cognitive psychology, 38(3), pp. 349-385. https://doi.org/10.1006/cogp.1998.0700
40. Stanovich, K. E. and West, R. F. (2000). Advancing the rationality debate. Behavioral and brain sciences, 23(5), pp. 701-717. https://doi.org/10.1017/S0140525X00623439
41. Toplak, M. E., West, R. F. and Stanovich, K. E. (2011). The Cognitive Reflection Test as a predictor of performance on heuristics-and-biases tasks. Memory & Cognition, 39(7), pp. 1275-1289. https://doi.org/10.3758/s13421-011-0104-1
42. Toplak, M. E., West, R. F. and Stanovich, K. E. (2014). Assessing miserly information processing: An expansion of the Cognitive Reflection Test. Thinking & Reasoning, 20(2), pp. 147-168. https://doi.org/10.1080/13546783.2013.844729
43. Tversky, A. and Kahneman, D. (1974). Judgment under Uncertainty: Heuristics and Biases: Biases in judgments reveal some heuristics of thinking under uncertainty. Science, 185(4157), pp. 1124-1131. https://doi.org/10.1126/science.185.4157.1124