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Randomized Strategies and Prospect Theory in a Dynamic Context

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Cumulative prospect theory (CPT) helps explain many features of individuals’ attitudes to risk. But, as observed by Barberis (2012), in a dynamic setting the probability weighting of CPT leads to time inconsistency. It also leads to another feature which has not been considered to date – agents may prefer randomized strategies to pure strategies. In this paper we consider the impact of allowing CPT agents to follow randomized strategies.

In the discrete-time, discrete-space model of gambling in a casino of Barberis (2012) we show that allowing randomized strategies leads to significant value gains. In a continuous-time, continuous-space model of Ebert and Strack (2014) we show that allowing randomization can significantly change the predictions of the model. Ebert and Strack show that a naive investor with CPT preferences never chooses to stop and gambles until the bitter end. We show that this extreme conclusion is no longer valid if the agent has a coin in his pocket.

This is joint work with Vicky Henderson and Alex Tse (Warwick)

Paper and Abstract

This talk is part of the Cambridge Finance Workshop Series series.

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