Publication:

Life-Cycle Asset Allocation Strategies for Individual Investors

datacite.rightsrestricted
dc.contributor.advisorMulvey, John
dc.contributor.authorChen, Eric
dc.date.accessioned2019-08-16T13:21:54Z
dc.date.accessioned2026-09-30T00:03:57Z
dc.date.available2019-08-16T13:21:54Z
dc.date.available2026-09-30T00:03:57Z
dc.date.created2019-04-15
dc.date.issued2019-08-16
dc.description.abstractThe rise of defined contribution pensions and the corresponding decline of defined benefit pensions in the U.S. has shifted much of the risk of retirement planning onto individuals. A popular asset allocation strategy for individuals in defined contribution pensions is the Standard Glide Path (SGP), which uses an individual’s target retirement date to glide equity allocation lower as retirement nears. In this study, we propose a goal-aware Dynamic Glide Path (DGP) that mimics SGP’s asset allocations, except when trailing a benchmark wealth path. When trailing the benchmark, DGP overlays an equity tilt relative to SGP in order to catch up to the benchmark. Using a regime-based Monte Carlo simulation framework, we evaluate the merits of SGP and DGP along with three other asset allocation strategies. Overall, we find that DGP’s goal-aware dynamic tilting helps individuals stay on track for a retirement goal better than SGP. Based on simulation results over a hypothetical individual’s entire life-cycle with sensitivity analysis, DGP improves on SGP’s probability of reaching the goal at retirement by 4 - 8 percentage points, making DGP the best of all five tested asset allocation strategies at reaching the goal. However, DGP exposes one to slightly worse downside scenarios than SGP as measured by drawdown and goal-relative value and conditional value at risk.en_US
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/dsp016t053j834
dc.identifier.urihttps://theses-dissertations.princeton.edu/handle/88435/dsp016t053j834
dc.language.isoenen_US
dc.titleLife-Cycle Asset Allocation Strategies for Individual Investorsen_US
dc.typePrinceton University Senior Theses
dspace.entity.typePublication
pu.contributor.authorid961168635
pu.date.classyear2019en_US
pu.departmentOperations Research and Financial Engineering*
pu.pdf.coverpageSeniorThesisCoverPage

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