Artikel

Combining investment and tax strategies for optimizing lifetime solvency under uncertain returns and mortality

This paper considers investors who are 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. This optimization works with stochastic investment returns and stochastic mortality, extending and combining different investment and tax-efficiency paradigms. We find that optimizing the investment strategy has a much larger impact on the investor remaining solvent than optimizing the tax strategy. This result is key to effectively optimizing both strategies simultaneously. This optimized investment strategy soundly beats a standard target date fund strategy, and the novel optimized tax strategy displays optimal desired properties suggested by non-stochastic tax optimization research.

Sprache
Englisch

Erschienen in
Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 14 ; Year: 2021 ; Issue: 7 ; Pages: 1-25 ; Basel: MDPI

Klassifikation
Wirtschaft
Thema
dynamic programming
goals-based wealth management
Monte Carlo methods
mortality
optimal strategy
retirement planning
Roth accounts
tax deferred accounts
taxable accounts
taxes

Ereignis
Geistige Schöpfung
(wer)
Das, Sanjiv R.
Ostrov, Daniel
Casanova, Aviva
Radhakrishnan, Anand
Srivastav, Deep
Ereignis
Veröffentlichung
(wer)
MDPI
(wo)
Basel
(wann)
2021

DOI
doi:10.3390/jrfm14070285
Handle
Letzte Aktualisierung
10.03.2025, 11:45 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Das, Sanjiv R.
  • Ostrov, Daniel
  • Casanova, Aviva
  • Radhakrishnan, Anand
  • Srivastav, Deep
  • MDPI

Entstanden

  • 2021

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