The perils of a blanket model: Financial anomalies and loss aversion

Eldad Yechiam

Chapter 8 in Handbook of Experimental Finance, 2022, pp 89-97 from Edward Elgar Publishing

Abstract: Loss aversion, the notion that the subjective weight on losses is larger than that of gains, is often used to explain important patterns in financial markets and economic behavior. The current review outlines challenges in the indiscriminate use of loss aversion as a unifying (or "blanket") model. First, the demonstration of loss aversion is not replicated for small to moderate stakes and in repeated decisions from feedback. Second, laboratory studies that purportedly documented loss aversion were exposed to several experimental artifacts. Third, the application of loss aversion in financial markets often requires auxiliary assumptions which are often questionable. Fourth, the predictions of loss aversion are often confounded with other effects of losses, such as effects on attention. These four challenges suggest that the applicability of loss aversion is lower than what is commonly assumed. The field of experimental finance should go beyond assuming or demonstrating loss aversion, and carefully evaluate its relevance.

Keywords: Economics and Finance (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.elgaronline.com/view/edcoll/9781800372337/9781800372337.00014.xml (application/pdf)
Our link check indicates that this URL is bad, the error code is: 503 Service Temporarily Unavailable

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:elg:eechap:20035_8

Ordering information: This item can be ordered from
http://www.e-elgar.com

Access Statistics for this chapter

More chapters in Chapters from Edward Elgar Publishing
Bibliographic data for series maintained by Darrel McCalla ().