EconPapers    
Economics at your fingertips  
 

Simple Allocation Rules and Optimal Portfolio Choice Over the Lifecycle

Victor Duarte, Julia Fonseca, Aaron S. Goodman and Jonathan Parker

No 29559, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: In many areas of economics, relatively simple models developed for insight are used as quantitative guides to behavior. In this paper, we develop a machine-learning algorithm to solve for optimal portfolio choice in a complex, calibrated lifecycle model that includes many features of reality modeled only separately in previous work. The average optimal portfolio share invested in stocks declines with age, consistent both with age-dependent rules derived from simpler models and with implicit advice embedded in current lifecycle investment products. But optimal portfolios decline more evenly, slowly, and by less than in current lifecycle products, and have substantial heterogeneity, particularly by wealth level and expected equity premium, implying significant gains to further customization in these dimensions. Together these two factors imply gains from moving to optimal portfolios of up to 3.5 to 3.7 percent of average consumption.

JEL-codes: C61 D15 E21 G11 G51 (search for similar items in EconPapers)
Date: 2021-12
New Economics Papers: this item is included in nep-big, nep-cmp and nep-mac
Note: AP EFG TWP
References: Add references at CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
http://www.nber.org/papers/w29559.pdf (application/pdf)

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:nbr:nberwo:29559

Ordering information: This working paper can be ordered from
http://www.nber.org/papers/w29559

Access Statistics for this paper

More papers in NBER Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by ().

 
Page updated 2026-09-26
Handle: RePEc:nbr:nberwo:29559