Ambiguity aversion and experiential learning : implications for long-term savings decisions
Purpose: This paper evaluates potential methods for reducing ambiguity surrounding returns on equity to improve long-term savings decisions. Design/methodology/approach: We evaluate 221 undergraduate students in the US and first assess the degree of ambiguity aversion exhibited by individuals in the sample population as they decide between a risky (known probability) option and ambiguous (unknown probability) option pertaining to their chances of winning $0 or $1 in a hypothetical lottery. Similarly, we test whether sampling historical return data through learning modules influences long-term decision making regarding asset allocation within a retirement portfolio. Findings: Allowing participants to experience the underlying probability through sampling significantly influences behavior, as participants were more likely to select the ambiguous option after sampling. Here, we also find that participants who receive interactive learning modules – which require users to manually alter the asset allocation to produce a sample of historical return data based on the specific allocation entered in the model – increase their post-learning equity allocations by 10.1% more than individuals receiving static modules. Interestingly, we find no significant evidence of ambiguity aversion playing a role in the asset allocation decision. Originality/value: We find that decision-making related to ambiguous and risky options can be substantially influenced by experiential learning. Our study supplements previous literature, providing a link between research on the effect of ambiguity on stock market participation and implementation of educational programs to improve the asset allocation decision for young adults.
Year of publication: |
2020
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Authors: | Foltice, Bryan ; Rogers, Rachel |
Published in: |
Review of Behavioral Finance. - Emerald, ISSN 1940-5979, ZDB-ID 2517439-3. - Vol. 13.2020, 4 (23.07.), p. 410-437
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Publisher: |
Emerald |
Saved in:
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