Abstract
We show that the optimal asset allocation for an investor depends crucially on the decision theory with which the investor is modeled. For the same market data and the same client data different theories lead to different portfolios. The market data we consider is standard asset allocation data. The client data is determined by a standard risk profiling question and the theories we apply are mean–variance analysis, expected utility analysis and cumulative prospect theory. For testing the robustness of our results, we carry out the comparisons for alternative data sets and also for variants of the risk profiling question.
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Notes
Comparing also with expected utility, which is consistent with second-order stochastic dominance, implies that we also touch on portfolio optimization based on stochastic dominance that was recently proposed by Kopa and Post (2015) for second-order stochastic dominance and in a subsequent paper by Post and Kopa (2016) for third-order stochastic dominance.
BhFS stands for Behavioral Finance Solutions, which is a spin-off company of the universities of St. Gallen and Zurich; for more information, see www.bhfs.ch.
The classical definition is the ratio of gains to losses which an investor with piecewise linear utility finds indifferent to an outside option of zero on a binary lottery with equal probabilities.
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This research was supported by the Swiss National Science Foundation, Grant No. 100018-149934.
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Hens, T., Mayer, J. Decision Theory Matters for Financial Advice. Comput Econ 52, 195–226 (2018). https://doi.org/10.1007/s10614-017-9668-6
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DOI: https://doi.org/10.1007/s10614-017-9668-6