Cumulative Prospect Theory and Non-linear Probability Weighting in Individual Asset Allocation

Davies, Greg B.; Satchell, Stephen E.

doi:10.1037/e683332011-009

Cited by 8 publications

(14 citation statements)

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“…Davies and Satchell (2005) investigate closedform solutions with strict assumptions on the asset process. In our simulation world we can easily solve the standard portfolio choice problem numerically.…”

Section: The Merton Problemmentioning

confidence: 99%

Optimal pension insurance design

Døskeland

Nordahl

2008

Journal of Banking & Finance

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In this paper we provide a framework for how the traditional life and pension contracts with a guaranteed rate of return can be optimized to increase customers' welfare. Given that the contracts have to be priced correctly, we use individuals' preferences to find the preferred design. Assuming CRRA utility, we cannot explain the existence of any form of guarantees. Through numerical solutions we quantify the difference (measured in security equivalents) to the preferred Merton solution of direct investments in a fixed proportion of risky and risk free assets. The largest welfare loss seems to come from the fact that guarantees are effective by the end of each year, not only by the expiry of the contract. However, the demand for products with guarantees may be explained through behavioral models accounting for loss aversion, e.g. cumulative prospect theory. In this case, the optimal design seems to be a simple contract with a life-time guarantee.

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Section: The Merton Problemmentioning

confidence: 99%

Optimal pension insurance design

Døskeland

Nordahl

2008

Journal of Banking & Finance

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“…Starmer and Sugden 1989;Luce and Fishburn 1991;Tversky and Kahneman 1992;Wakker and Tversky 1993). Variants of cumulative prospect theory are increasingly widely applied in both theoretical and empirical work (recent examples are Davies and Satchell 2004;Trepel, Fox and Poldrack 2005;Wu, Zhang and Abdellaoui 2005;Baucells and Heukamp 2006;Schmidt and Zank 2008) and some have argued that such theories may be serious contenders for replacing expected utility theory at least for specific purposes (see Camerer 1989). No doubt this is partly because there is considerable empirical support for both reference-dependence and decision weights (see Starmer 2000).…”

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confidence: 99%

Third-generation prospect theory

2008

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We present a new theory of decision under uncertainty: third-generation prospect theory (PT 3 ). This retains the predictive power of previous versions of prospect theory, but extends that theory by allowing reference points to be uncertain while decision weights are specified in a rank-dependent way. We show that PT 3 preferences respect a state-conditional form of stochastic dominance. The theory predicts the observed tendency for willingness-to-accept valuations of lotteries to be greater than willingness-to-pay valuations. When PT 3 is made operational by using simple functional forms with parameter values derived from existing experimental evidence, it predicts observed patterns of the preference reversal phenomenon. Keywords Prospect theory . Preference reversal . Reference dependence JEL Classification D81In this paper we present a new theory of decision under uncertainty: third-generation prospect theory (PT 3 for short). The motivation for the theory is empirical: our model is presented as a descriptive theory intended to outperform the current 'best buys' in the literature. PT 3 has three key features: reference dependence, decision weights and uncertain reference points (i.e. reference points that can be lotteries). The first two features are the common characteristics of different versions of prospect theory, including the original (or first-generation) version (Kahneman and J Risk Uncertainty

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“…Berkelaar, Kouwenberg, and Post (2004) use US stock market data to calculate excess returns, and estimate loss aversion to be 2.71. Davies and Satchell (2005) use US and UK aggregate data on equity excess returns and find that strategies consistent with observed asset allocation can only be obtained when loss aversion is within the interval [1.8, 2.6].…”

Section: Literature Reviewmentioning

confidence: 94%

“…In a different approach, Davies and Satchell (2005) integrate the value and weighting functions to study the allocation of investments. Using stock market data on excess returns from the US and UK, they investigate what parameter values of their functions are needed to make their analytical portfolio and observed allocations correspond.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The work investigating the combined effects of the value and probability functions in financial decision making is only now emerging. Strikingly the research findings show that substantive changes can arise when multiple dimensions of the behavioral literature are considered (Blavatskyy and Pogrebna, 2005;Davies and Satchell, 2005;Langer and Weber, 2005), but the complexity of the phenomena suggests that additional work is needed. Further, most recent research has focused primarily on asset price models for equity and bond markets, with little analysis on the role of behavioral theory in futures markets and the hedging decision.…”

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confidence: 91%

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