scholarly journals Applied prospect theory: assessing the βs of M&A-intensive firms

2019 ◽  
Vol 16 (2) ◽  
pp. 236-248
Author(s):  
Garth Ryan Homan ◽  
Gary van Vuuren
Keyword(s):  
Kalman Filter ◽  
Prospect Theory ◽  
Risk Premium ◽  
Utility Theory ◽  
Human Error ◽  
Cognitive Biases ◽  
Predictive Accuracy ◽  
Market Price ◽  
Expected Utility Theory ◽  
Specific Risk

Behavioral components of Kahneman and Tversky’s (1979) prospect theory (PT) were applied to derive an adjusted Capital Asset Pricing Model (CAPM) in the estimation of merger and acquisition-intensive firms’ expected returns. The premise was that the CAPM – rooted in expected utility theory – is violated by the behavioral biases identified in prospect theory. Kahneman and Tversky’s prospect theory (1979) has demonstrated that weaknesses abound in the viability of classical utility theory predictions. For mergers and acquisitions, firms appear to be isolated from and immune to human error, yet decisions which involve the undertaking of capital-intensive projects are delegated to senior management. These individuals are prone to cognitive biases and personalized risk appetites that may (and often do) compromize attitudes and behavior when it comes to pricing risky ventures. Having established that beta estimates using linear regression are inferior, the CAPM was implemented utilizing beta estimates obtained from the Kalman filter. The results obtained were assessed for their long-term market price predictive accuracy. The authors test the reliability of the CAPM as a predictor of price, observe the rationality of human behavior in capital markets, and attempt to model premiums to adjust CAPM returns to a level that more appropriately accounts for firm specific risk. The researchers show that market participants behave irrationally when assessing M&A firms’ specific risk. Logistic regression coupled with the development of a risk premium was implemented to correct the original Kalman filter returns and was tested for improvements in predictive power.

scholarly journals Cumulative Prospect Theory Version with Fuzzy Values of Outcome Estimates

Risks ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 72
Author(s):  
Oleg Uzhga-Rebrov ◽  
Peter Grabusts
Keyword(s):  
Prospect Theory ◽  
Expected Utility ◽  
Utility Theory ◽  
Expected Utility Theory ◽  
Cumulative Prospect Theory ◽  
Development History ◽  
Decisions Under Risk ◽  
History Of ◽  
Main Factors ◽  
Theoretical Foundations

Choosing solutions under risk and uncertainty requires the consideration of several factors. One of the main factors in choosing a solution is modeling the decision maker’s attitude to risk. The expected utility theory was the first approach that allowed to correctly model various nuances of the attitude to risk. Further research in this area has led to the emergence of even more effective approaches to solving this problem. Currently, the most developed theory of choice with respect to decisions under risk conditions is the cumulative prospect theory. This paper presents the development history of various extensions of the original expected utility theory, and the analysis of the main properties of the cumulative prospect theory. The main result of this work is a fuzzy version of the prospect theory, which allows handling fuzzy values of the decisions (prospects). The paper presents the theoretical foundations of the proposed version, an illustrative practical example, and conclusions based on the results obtained.

scholarly journals An Experimental Comparison of Risky and Riskless Choice—Limitations of Prospect Theory and Expected Utility Theory

2019 ◽  
Vol 11 (3) ◽  
pp. 34-67 ◽  
Author(s):  
Hui-Kuan Chung ◽  
Paul Glimcher ◽  
Agnieszka Tymula
Keyword(s):  
Prospect Theory ◽  
Empirical Evidence ◽  
Expected Utility ◽  
Utility Theory ◽  
Expected Utility Theory ◽  
Experimental Comparison ◽  
Reference Dependence ◽  
Empirical Results ◽  
Incentive Compatible ◽  
Gain Loss

Prospect theory, used descriptively for decisions under both risk and certainty, presumes concave utility over gains and convex utility over losses; a pattern widely seen in lottery tasks. Although such discontinuous gain-loss reference-dependence is also used to model riskless choices, only limited empirical evidence supports this use. In incentive-compatible experiments, we find that gain-loss reflection effects are not observed under riskless choice as predicted by prospect theory, even while in the same subjects gain-loss reflection effects are observed under risk. Our empirical results challenge the application of choice models across both risky and riskless domains. (JEL C91, D12, D81)

Contrasting Rational and Psychological Analyses of Political Choice

1988 ◽  
Vol 82 (3) ◽  
pp. 719-736 ◽  
Author(s):  
George A. Quattrone ◽  
Amos Tversky
Keyword(s):  
Risk Aversion ◽  
Prospect Theory ◽  
Expected Utility ◽  
Reference Point ◽  
Utility Theory ◽  
Expected Utility Theory ◽  
Risky Choice ◽  
Political Candidates ◽  
Risk Seeking ◽  
Psychological Analysis

We contrast the rational theory of choice in the form of expected utility theory with descriptive psychological analysis in the form of prospect theory, using problems involving the choice between political candidates and public referendum issues. The results showed that the assumptions underlying the classical theory of risky choice are systematically violated in the manner predicted by prospect theory. In particular, our respondents exhibited risk aversion in the domain of gains, risk seeking in the domain of losses, and a greater sensitivity to losses than to gains. This is consistent with the advantage of the incumbent under normal conditions and the potential advantage of the challenger in bad times. The results further show how a shift in the reference point could lead to reversals of preferences in the evaluation of political and economic options, contrary to the assumption of invariance. Finally, we contrast the normative and descriptive analyses of uncertainty in choice and address the rationality of voting.

scholarly journals Psychological parameters have units: A bug fix for stochastic prospect theory and other decision models

2018 ◽  
Author(s):  
Neil Stewart ◽  
Benjamin Scheibehenne ◽  
Thorsten Pachur
Keyword(s):  
Risk Aversion ◽  
Prospect Theory ◽  
Utility Theory ◽  
Expected Utility Theory ◽  
Decision Models ◽  
Strongly Correlated ◽  
Choice Data ◽  
Physical Sciences ◽  
Parameter Measuring ◽  
Choice Probabilities

To fit models like prospect theory or expected utility theory to choice data, a stochastic model is needed to turn differences in values into choice probabilities. In these models, the parameter measuring risk aversion is strongly correlated with the parameter measuring the sensitivity to differences in value. We use dimensional analysis from the physical sciences to show that this is because the sensitivity parameter has units which depend on the risk aversion parameter. This means that comparing sensitivities across individuals with different level of risk aversion is meaningless and forbidden. We suggest a simple bug fix for prospect theory and other decision models which corrects this problem. The bug fix completely removes the correlation between sensitivity and risk aversion parameters in model estimations and allows the parameters to be interpreted as they were originally intended.

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