Decision Support for Retirement Portfolio Management: Overcoming Myopic Loss Aversion via Technology Design
As firms continue to abandon pensions in favor of employee-managed retirement plans, tremendous demands are being placed on the decision-making proficiency of future retirees. As reflected in the equity premium puzzle, individual investors tend to hold overly conservative portfolios that provide meager payoffs over time. Consequently, there is growing concern that the vast majority of retirement accounts might be insufficiently funded when employees reach retirement. Given that most retirement plans can now be managed online, a potential solution lies in designing a Web-based decision support system (DSS) that helps future retirees make more-profitable portfolio management decisions. This paper reports the results of a study in which 159 retirement plan participants were asked to use an experimental website to manage a portfolio of retirement investments over a simulated 30-year period. Using a psychological approach toward designing the DSS, myopic loss aversion is put forth as a theoretical explanation for the psychological mechanisms that encourage investors to hold overly conservative portfolios. Armed with this knowledge, three design features--information horizon, system restrictiveness, and decisional guidance--are implemented as part of an overarching design strategy targeted at increasing investors' willingness to take calculated risks. The results indicate that investor conservatism diminishes when the DSS presents prospective probabilities and payoffs over long time horizons. In contrast, short-term information horizons constitute a major stumbling block for investors. However, when confronted with short-term information horizons, risk aversion can be successfully counteracted by configuring a DSS to either restrict the frequency of decisions or to suggest a relatively aggressive portfolio allocation. These findings carry important implications for theory and practice.
Volume (Year): 55 (2009)
Issue (Month): 10 (October)
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