Which Measures Predict Risk Taking in a Multi-Stage Controlled Decision Process?

31 Pages Posted: 9 Dec 2014 Last revised: 22 Feb 2020

See all articles by Kremena Bachmann

Kremena Bachmann

University of Zurich - Department of Banking and Finance; Zurich University of Applied Sciences

Thorsten Hens

University of Zurich - Department of Banking and Finance; Norwegian School of Economics and Business Administration (NHH); Swiss Finance Institute

Remo Stössel

University of Zurich - Department of Banking and Finance

Date Written: April 9, 2016

Abstract

We assess the ability of different risk profiling measures to predict risk taking along a multi-stage decision process. The latter involves decisions under ambiguity, decisions under risk, decisions after gaining experience and decisions after receiving outcome information on previous decisions. We find that in all decisions risk taking can be predicted by some questions on individuals’ risk tolerance but it is not related to self-reported investment experience. Although simulated experience as part of our study design improves the risk awareness and leads to higher risk taking, it cannot substitute the assessment of risk tolerance and in particular the assessment of individual’s loss aversion. In contrast, self-assessed risk tolerance measures are not suitable for predicting risk taking in any stage of the decision process. Among the socioeconomic characteristics only the gender has some predictive power.

Keywords: investment advice, risk profiling, experience sampling, risk attitude, risk perception, risk preferences

JEL Classification: D81, G11

Suggested Citation

Bachmann, Kremena and Hens, Thorsten and Stössel, Remo, Which Measures Predict Risk Taking in a Multi-Stage Controlled Decision Process? (April 9, 2016). Bachmann, K., Hens, T., and Stössel, R. (2018). Which Measures Predict Risk Taking in a Multi-Stage Controlled Decision Process? Financial Services Review, 26, 339–365. Doi.org/10.2139/ssrn.2535859, Available at SSRN: https://ssrn.com/abstract=2535859 or http://dx.doi.org/10.2139/ssrn.2535859

Kremena Bachmann

University of Zurich - Department of Banking and Finance ( email )

Plattenstrasse 32
CH-8032 Zurich, Zurich 8032
Switzerland

Zurich University of Applied Sciences ( email )

Institut fuer Angewandte Medienwissenschaft
Zur Kesselschmiede 35
Winterthur, CH 8401
Switzerland

Thorsten Hens

University of Zurich - Department of Banking and Finance ( email )

Plattenstrasse 32
Zurich, 8032
Switzerland
+41-44 634 37 06 (Phone)

Norwegian School of Economics and Business Administration (NHH)

Helleveien 30
Bergen, 5045
Norway

Swiss Finance Institute

c/o University of Geneva
40, Bd du Pont-d'Arve
CH-1211 Geneva 4
Switzerland

Remo Stössel (Contact Author)

University of Zurich - Department of Banking and Finance ( email )

Plattenstrasse 32
Zürich, 8032
Switzerland

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