Combining Investment and Tax Strategies for Optimizing Lifetime Solvency under Uncertain Returns and Mortality

Sanjiv Ranjan Das,Daniel N Ostrov,Aviva Casanova,Anand Radhakrishnan,Deep Srivastav

doi:10.2139/ssrn.3753996

Abstract

We consider an investor who is looking to maximize their probability of remaining solvent throughout their lifetime by using an algorithm that aims to optimize their investment allocation strategy and optimize their tax strategy for withdrawal allocations between tax deferred accounts (TDAs), Roth accounts, and taxable stock and bond accounts. Our optimization works with stochastic investment returns and stochastic mortality. We find that optimizing the investment strategy (via dynamic programming) has a much larger impact on the investor remaining solvent than optimizing the tax strategy (via Monte Carlo and numerical optimization). This result is key to effectively optimizing both strategies simultaneously. We show that our optimized investment strategy soundly beats a standard target date fund strategy, while our novel optimized tax strategy displays the optimal desired properties suggested by non-stochastic tax optimization research.

Highlights

(4) Our results show that investment optimization is far more important than tax optimization for maximizing the probability that an investor can remain solvent
At least for the case presented here, the taxation structure for the pre-tax tax deferred accounts (TDAs) used to purchase the Qualified Longevity Annuity Contracts (QLACs) and the taxation structure for the income provided by the annuity are nearly identical, so the cost in pre-tax dollars of the QLAC is identical to the post-tax cost of the annuity, where the tax structure of the payment and the payouts have to be identical since neither is subject to tax.7. Both investment optimization and tax optimization are important for retirees, but they have very different mechanisms and implications
Investment optimization is probabilistic in nature, balancing risk and return

Summary

Introduction

This paper offers four main contributions for long-term retirement planning, with a goal to maximize the likelihood of solvency during the lifetime of the investor: (i) The model in the paper combines dynamic portfolio optimization (Merton 1969, 1971; Browne 1995; Dammon et al 2004; Horan 2006a, 2006b; Das et al 2019; Jaconetti and Bruno 2008; Spitzer and Singh 2006; Wang et al 2011) and tax optimization over multiple retirement (and possibly non-retirement) accounts (Brown et al 2017; Cook et al 2015; Sumutka et al 2012; DiLellio and Ostrov 2017, 2018) into one algorithm, whereas these have been handled as separate problems in the literature. (ii) Unlike most tax optimization papers, we include the effect of annual stochastic stock returns, as opposed to using fixed returns. (iii) We consider optimization that takes into account mortality risk and its concomitant uncertain portfolio horizon, which is more realistic and useful than assuming a known terminal date or looking to optimize portfolio longevity. (iv) The analyses in the paper determine that portfolio optimization has first-order consequences on lifetime portfolio solvency, whereas the impact of tax optimization has second-order consequences. (iv) The analyses in the paper determine that portfolio optimization has first-order consequences on lifetime portfolio solvency, whereas the impact of tax optimization has second-order consequences. This insight means algorithms may ignore all but the most important features of the tax code when they are considering how to optimize lifetime solvency, making the optimization algorithm computationally feasible. The approach shown here extends the academic literature and offers a comprehensive algorithm for practical retirement planning

Objectives

Results

Conclusion