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Quantum-Like Model of Subjective Expected Utility: A Survey of Applications to Finance

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 809))

Abstract

In this survey paper we review the potential financial applications of quantum probability (QP) framework of subjective expected utility formalized in [2]. The model serves as a generalization to the classical probability (CP) scheme and relaxes the core axioms of commutativity and distributivity of events. The agents form subjective beliefs via the rules of projective probability calculus and make decisions between prospects or lotteries by employing utility functions and some additional parameters given by a so called ‘comparison operator’. Agents’ comparison between lotteries involves interference effects that denote their risk perceptions from the ambiguity about prospect realisation when making a lottery selection. The above framework that builds upon the assumption of non-commuting lottery observables can have a wide class of applications to finance and asset pricing. We review here a case of an investment in two complementary risky assets about which the agent possesses non-commuting price expectations that give raise to a state dependence in her trading preferences. We summarise by discussing some other behavioural finance applications of the QP based selection behaviour framework.

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Notes

  1. 1.

    A deviation from classical information processing and other instances of ‘non-optimization’ in a vNM sense are not universally considered as an exhibition of ‘low intelligence’, but as a mode of a faster and more efficient decision making process that is built upon using mental shortcuts and heuristics, in a given decision making situation, also known through Herbert Simon’s notion of ‘bounded rationality’ that is reinforced in the work by [12].

  2. 2.

    Johnson-Laird and Shafir, [20], separate choice theories into three categories: normative, descriptive and prescriptive. The descriptive accounts have as their goal to capture the real process of decision formation, see e.g. Prospect Theory and its advances. Prescriptive theories are not easy to fit into either category (normative, or descriptive). In a sense, prescriptive theories would provide a prognosis on how a decision maker ought to reason in different contexts.

  3. 3.

    This assumption is also central for a satisfaction of the independence axiom and the reduction axiom of compound lotteries, in addition to other axioms establishing the preference rule, such as completeness and transitivity.

  4. 4.

    A theoretical analysis in [36] in a similar vein shows an existence of a negative welfare effect from agents’ ambiguity averse beliefs about the idiosyncratic risk component of some asset classes that also yields under-pricing of these assets and a reduced diversification with these assets.

  5. 5.

    We note that ‘state dependence’ that we can also allude to as ‘context dependence’, as coined in [26], indicates that agents can be affected by other factors besides, e.g., previous losses or levels of risk in the process of their preference and belief formation. As we indicated earlier, agents beliefs and value perception can be interconnected in their mind, whereby shifts in their welfare level can also transform their beliefs. This more broad based type of impact of the current decision making state of the agent upon her beliefs and risk preferences is well addressed by the ‘mental state’ wave function in QP models see, e.g., detailed illustration in [8, 17, 39].

  6. 6.

    Some psychological factors that can contribute to the particular parameter values are further explored in [57].

  7. 7.

    We stress one important distinction of the utility computation in the QP framework, where utility value is depending on the particular lottery observable, and not only to the monetary outcome.

  8. 8.

    The splitting of the composite comparison operator into two sub-operators that generate the reflection dynamics of the agents’ indeterminate preference state is a mathematical construct that aims to illustrate the process behind lottery evaluation.

  9. 9.

    In the simple setup with two types of discrete price movements, we fix only two eigenvectors \(\vert \alpha _+\rangle \) and \(\vert \alpha _-\rangle \), corresponding to eigenvalues \(a=\pm 1\).

  10. 10.

    The model can be generalized to include the actual trading behaviour, i.e., where the agent does not only observe the price dynamics of the assets between the trading periods that feeds back into her beliefs about the complimentary assets’ future price realizations, but also actually trades the assets, based on the perceived utility of each portfolio holding. In this setting the agent’s mental state in relation to the future price expectations is also affected by the realized losses and gains.

  11. 11.

    Order effects can exist for: (i) information processing related to the order effect for the observation of some sequences of signals; (ii) preference formation related to the sequence of asset evaluation or actual asset trading that we described now. Non-commuting observables allow to depict agents’ state dependence in preference formation. As noted, when state dependence is absent, the observable operators are commuting.

References

  1. Allais, M.: Le comportement de l’homme rationnel devant le risque: critique des postulats et axiomes de l’Ecole americaine. Econometrica 21, 503–536 (1953)

    Article  MathSciNet  Google Scholar 

  2. Asano, M., Basieva, I., Khrennikov, A., Ohya, M., Tanaka, Y.: A quantum-like model of selection behavior. J. Math. Psych. 78, 2–12 (2017)

    Article  MathSciNet  Google Scholar 

  3. Banz, R.W.: The relationship between return and market value of common stocks. J. Fin. Econ. 9(1), 3–18 (1981)

    Article  Google Scholar 

  4. Basu, S.: Investment performance of common stocks in relation to their price-earning ratios: a test of the Efficient Market Hypothesis. J. Financ. 32(3), 663–682 (1977)

    Article  Google Scholar 

  5. Basieva, I., Pothos, E., Trueblood, J., Khrennikov, A., Busemeyer, J.: Quantum probability updating from zero prior (by-passing Cromwell’s rule). J. Math. Psych. 77, 58–69 (2017)

    Article  MathSciNet  Google Scholar 

  6. Basieva, I., Khrennikova, P., Pothos, E., Asano, M., Khrennikov, A.: Quantum-like model of subjective expected utility. J. Math. Econ. (2018). https://doi.org/10.1016/j.jmateco.2018.02.001

    Article  MathSciNet  Google Scholar 

  7. Busemeyer, J.R., Wang, Z., Townsend, J.T.: Quantum dynamics of human decision making. J. Math. Psych. 50, 220–241 (2006)

    Article  MathSciNet  Google Scholar 

  8. Busemeyer, J., Bruza, P.: Quantum models of Cognition and Decision. Cambridge University Press (2012)

    Google Scholar 

  9. Costello, F., Watts, P.: Surprisingly rational: probability theory plus noise explains biases in judgment. Psych. Rev. 121(3), 463–480 (2014)

    Article  Google Scholar 

  10. Ellsberg, D.: Risk, ambiguity and the Savage axioms. Q. J. Econ. 75, 643–669 (1961)

    Article  Google Scholar 

  11. Epstein, L.G., Schneider, M.: Ambiguity, information quality and asset pricing. J. Finance LXII(1), 197–228 (2008)

    Article  Google Scholar 

  12. Gigerenzer, G., Selten, R.: Bounded Rationality: The Adaptive Toolbox. MIT Press (2002)

    Google Scholar 

  13. Gilboa, I., Schmeidler, D.: Maxmin expected utility with non-unique prior. J. Math. Econ. 18, 141–153 (1989)

    Article  Google Scholar 

  14. Gilboa, I.: Theory of decision under uncertainty. Econometric Society Monographs (2009)

    Google Scholar 

  15. Gonzales, R., Wu, G.: On the shape of the probability weighting function. Cogn. Psych. 38, 129–166 (1999)

    Article  Google Scholar 

  16. Harrison, M., Kreps, D.: Speculative investor behaviour in a stock market with heterogeneous expectations. Q. J. Econ. 89, 323–336 (1978)

    Article  Google Scholar 

  17. Haven, E., Khrennikov, A.: Quantum Social Science. Cambridge University Press, Cambridge (2013)

    Book  Google Scholar 

  18. Haven, E., Sozzo, S.: A generalized probability framework to model economic agents’ decisions under uncertainty. Int. Rev. Financ. Anal. 47, 297–303 (2016)

    Article  Google Scholar 

  19. Haven, E., Khrennikova, P.: A quantum probabilistic paradigm: non-consequential reasoning and state dependence in investment choice. J. Math. Econ. (2018). https://doi.org/10.1016/j.jmateco.2018.04.003

    Article  MathSciNet  Google Scholar 

  20. Johnson-Laird, P.M., Shafir, E.: The interaction between reasoning and decision making: an introduction. In: Johnson-Laird, P.M., Shafir, E.: Reasoning and Decision Making. Blackwell Publishers, Cambridge (1994)

    Google Scholar 

  21. Karni, E.: Axiomatic foundations of expected utility and subjective probability. In: Machina, M.J., Kip Viscusi, W. (eds.) Handbook of Economics of Risk and Uncertainty, pp. 1–39. Oxford, North Holland (2014)

    Google Scholar 

  22. Kahneman, D., Tversky, A.: Subjective probability: a judgement of representativeness. Cogn. Psych. 3(3), 430–454 (1972)

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. Kahneman, D., Knetch, J.L., Thaler, R.H.: Experimental tests of the endowment effect and the coarse theorem. J. Polit. Econ. 98(6), 1325–1348 (1990)

    Article  Google Scholar 

  25. Kahneman, D.: Maps of bounded rationality: psychology for behavioral economics. Am. Econ. Rev. 93(5), 1449–1475 (2003)

    Article  Google Scholar 

  26. Kahneman, D., Thaler., R.: Utility maximization and experienced utility. J. Econ. Persp. 20, 221–234 (2006)

    Google Scholar 

  27. Khrennikov, A.: Classical and quantum mechanics on information spaces with applications to cognitive, psychological, social and anomalous phenomena. Found. Phys. 29, 1065–1098 (1999)

    Article  MathSciNet  Google Scholar 

  28. Khrennikov, A.: Quantum-like formalism for cognitive measurements. Biosystems 70, 211–233 (2003)

    Article  Google Scholar 

  29. Khrennikov, A., Basieva, I., Dzhafarov, E.N., Busemeyer, J.R.: Quantum models for psychological measurements : An unsolved problem. PLoS ONE 9 (2014). Article ID: e110909

    Article  Google Scholar 

  30. Khrennikov, A.: Quantum version of Aumann’s approach to common knowledge: sufficient conditions of impossibility to agree on disagree. J. Math. Econ. 60, 89–104 (2015)

    Article  MathSciNet  Google Scholar 

  31. Khrennikova, P.: Application of quantum master equation for long-term prognosis of asset-prices. Physica A 450, 253–263 (2016)

    Article  MathSciNet  Google Scholar 

  32. Klibanoff, P., Marinacci, M., Mukerji, S.: A smooth model of decision making under ambiguity. Econometrica 73, 1849–1892 (2005)

    Article  MathSciNet  Google Scholar 

  33. Knutson, B., Samanez-Larkin, G.R., Kuhnen, C.M.: Gain and loss learning differentially contribute to life financial outcomes. PLoS ONE 6(9), e24390 (2011)

    Article  Google Scholar 

  34. Kolmogorov, A.N.: Grundbegriffe der Warscheinlichkeitsrechnung, Springer, Berlin (1933). English translation: Foundations of the Probability Theory. Chelsea Publishing Company, New York (1956)

    Google Scholar 

  35. Machina, M.J.: Choice under uncertainty: problems solved and unsolved. J. Econ. Perspect. 1(1), 121–154 (1987)

    Article  Google Scholar 

  36. Mukerji, S., Tallan, J.M.: Ambiguity aversion and incompleteness of financial markets. Rev. Econ. Stud. 68, 883–904 (2001)

    Article  MathSciNet  Google Scholar 

  37. Nau, R.F.: Uncertainty aversion with second-order utilities and probabilities. Manag. Sci. 52, 136–145 (2006)

    Article  Google Scholar 

  38. Pothos, M.E., Busemeyer, J.R.: A quantum probability explanation for violations of rational decision theory. Proc. Roy. Soc. B 276(1665), 2171–2178 (2009)

    Article  Google Scholar 

  39. Pothos, E.M., Busemeyer, J.R.: Can quantum probability provide a new direction for cognitive modeling? Behav. Brain Sc. 36(3), 255–274 (2013)

    Article  Google Scholar 

  40. Prelec, D.: The probability weighting function. Econometrica 60, 497–528 (1998)

    Article  MathSciNet  Google Scholar 

  41. Roca, M., Hogarth, R.M., Maule, A.J.: Ambiguity seeking as a result of the status quo bias. J. Risk and Uncertainty 32, 175–194 (2006)

    Article  Google Scholar 

  42. Sarin, R.K., Weber, M.: Effects of ambiguity in market experiments. Manag. Sci. 39, 602–615 (1993)

    Article  Google Scholar 

  43. Savage, L.J.: The Foundations of Statistics. Wiley, US (1954)

    MATH  Google Scholar 

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

    Article  Google Scholar 

  45. Schemeidler, D.: Subjective probability and expected utility without additivity. Econometrica 57(3), 571–587 (1989)

    Article  MathSciNet  Google Scholar 

  46. Shafir, E.: Uncertainty and the difficulty of thinking through disjunctions. Cognition 49, 11–36 (1994)

    Article  Google Scholar 

  47. Shiller, R.: Speculative asset prices. Amer. Econ. Rev. 104(6), 1486–1517 (2014)

    Article  Google Scholar 

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

    Article  Google Scholar 

  49. Thaler, R.: Misbehaving. W.W. Norton & Company (2015)

    Google Scholar 

  50. Thaler, R.: Quasi-Rational Economics. Russel Sage Foundations (1994)

    Google Scholar 

  51. Trautman, S.T.: Shunning uncertainty: the neglect of learning opportunities. Games Econ. Behav. 79, 44–55 (2013)

    Article  MathSciNet  Google Scholar 

  52. Trueblood, J.S., Busemeyer, J.R.: A quantum probability account of order effects in inference. Cogn. Sci. 35, 1518–1552 (2011)

    Article  Google Scholar 

  53. Tversky, D., Kahneman, D.: Advances in prospect theory: cumulative representation of uncertainty. J. Risk Uncertainty 5, 297–323 (1992)

    Article  Google Scholar 

  54. von Neumann, J., Morgenstern, O.: Theory of Games and Economic Behaviour. Princeton University Press, Princeton (1944)

    MATH  Google Scholar 

  55. Wang, Z., Busemeyer, J.R.: A quantum question order model supported by empirical tests of an a priori and precise prediction. Topics in Cogn. Sci. 5, 689–710 (2013)

    Google Scholar 

  56. Yukalov, V.I., Sornette, D.: Decision Theory with prospect inference and entanglement. Theory Dec. 70, 283–328 (2011)

    Article  Google Scholar 

  57. Wu, G., Gonzales, R.: Curvature of the probability weighting function. Manag. Sci. 42(12), 1676–1690 (1996)

    Article  Google Scholar 

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Correspondence to Polina Khrennikova .

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Khrennikova, P. (2019). Quantum-Like Model of Subjective Expected Utility: A Survey of Applications to Finance. In: Kreinovich, V., Thach, N., Trung, N., Van Thanh, D. (eds) Beyond Traditional Probabilistic Methods in Economics. ECONVN 2019. Studies in Computational Intelligence, vol 809. Springer, Cham. https://doi.org/10.1007/978-3-030-04200-4_5

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