Portfolio Optimization Without Optimization

35 Pages Posted: 21 Jan 2021

See all articles by Mike Aguilar

Mike Aguilar

Duke University

Anessa Custovic

Cardinal Retirement Planning Inc.

Date Written: January 20, 2021

Abstract

In this paper we introduce a simple, yet flexible approach to construct (enhanced) index tracking portfolios. PADME (Portfolio Allocation via Density MatchEs) uses the density function of returns as a robust means of capturing investor preferences, and identifies suitable portfolios through a measure of closeness. Recognizing model uncertainty, PADME avoids optimization and instead relies upon the behavioral concept of revealed preferences by offering the investor a palette of options from which to choose. In a case study we consider an EIT manager with an SP500 benchmark. We illustrate PADME's flexibility by imposing several types of Bayesian beliefs simultaneously. Using far fewer assets than the SP500, we are able to generate numerous portfolios whose out-of-sample risk and return were superior to the benchmark during the turbulent COVID Crisis of 2020.

Keywords: index tracking, portfolio resilience, evolutionary heuristic, revealed preferences, full scale optimization

JEL Classification: G11, G02

Suggested Citation

Aguilar, Mike and Custovic, Anessa, Portfolio Optimization Without Optimization (January 20, 2021). Available at SSRN: https://ssrn.com/abstract=3770361 or http://dx.doi.org/10.2139/ssrn.3770361

Mike Aguilar (Contact Author)

Duke University ( email )

Durham, NC 27708
United States

Anessa Custovic

Cardinal Retirement Planning Inc. ( email )

2530 Meridian Pkway #100
Durham, NC 27713
United States

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