Optimal Asset Allocation for Retirement Savings: Deterministic vs. Time Consistent Adaptive Strategies

38 Pages Posted: 26 Jun 2017 Last revised: 26 Dec 2018

See all articles by Peter Forsyth

Peter Forsyth

University of Waterloo - David R. Cheriton School of Computer Science

Kenneth R. Vetzal

University of Waterloo

Date Written: October 18, 2018

Abstract

We consider optimal asset allocation for a long-term investor saving for retirement. The investment portfolio consists of a bond index and a stock index. Using multi-period mean variance criteria, we explore two types of strategies: deterministic strategies are based only on the time remaining until the anticipated retirement date, while adaptive strategies also consider the investor's accumulated wealth. The vast majority of financial products designed for retirement saving currently offered in the U.S. market use deterministic strategies, a prominent example being target date funds. The factors used to determine the specific asset allocations for these products are unclear. We develop methods which give the best possible allocations for deterministic strategies, according to mean-variance criteria. We also consider optimal adaptive strategies. For both a synthetic market where the stock index is modeled by a jump diffusion process and bootstrap resampling of long-term historical data, we find that the optimal adaptive strategy significantly outperforms the optimal deterministic strategy. This suggests that investors are not being well-served by the strategies currently dominating the marketplace.

Keywords: mean-variance, dynamic asset allocation, jump diffusion, resampled backtests, deterministic strategy, adaptive strategy

JEL Classification: G11, G22

Suggested Citation

Forsyth, Peter and Vetzal, Kenneth R., Optimal Asset Allocation for Retirement Savings: Deterministic vs. Time Consistent Adaptive Strategies (October 18, 2018). Available at SSRN: https://ssrn.com/abstract=2991828 or http://dx.doi.org/10.2139/ssrn.2991828

Peter Forsyth (Contact Author)

University of Waterloo - David R. Cheriton School of Computer Science ( email )

200 University Avenue West
Waterloo, ON
Canada

Kenneth R. Vetzal

University of Waterloo ( email )

200 University Avenue West
Waterloo, Ontario N2L 3G1 N2L 3G1
Canada
519-885-1211 (Phone)
519-888-7562 (Fax)

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