Robust Asset Allocation for Robo-Advisors

67 Pages Posted: 25 Oct 2018 Last revised: 6 Nov 2018

See all articles by Thibault Bourgeron

Thibault Bourgeron

Amundi Asset Management

Edmond Lezmi

Amundi Asset Management

Thierry Roncalli

Amundi Asset Management; University of Evry

Date Written: September 1, 2018

Abstract

In the last few years, the financial advisory industry has been impacted by the emergence of digitalization and robo-advisors. This phenomenon affects major financial services, including wealth management, employee savings plans, asset managers, private banks, pension funds, banking services, etc. Since the robo-advisory model is in its early stages, we estimate that robo-advisors will help to manage around $1 trillion of assets in 2020 (OECD, 2017). And this trend is not going to stop with future generations, who will live in a technology-driven and social media-based world.

In the investment industry, robo-advisors face different challenges: client profiling, customization, asset pooling, liability constraints, etc. In its primary sense, robo-advisory is a term for defining automated portfolio management. This includes automated trading and rebalancing, but also automated portfolio allocation. And this last issue is certainly the most important challenge for robo-advisory over the next five years. Today, in many robo-advisors, asset allocation is rather human-based and very far from being computer-based. The reason is that portfolio optimization is a very difficult task, and can lead to optimized mathematical solutions that are not optimal from a financial point of view (Michaud, 1989). The big challenge for robo-advisors is therefore to be able to optimize and rebalance hundreds of optimal portfolios without human intervention.

In this paper, we show that the mean-variance optimization approach is mainly driven by arbitrage factors that are related to the concept of hedging portfolios. This is why regularization and sparsity are necessary to define robust asset allocation. However, this mathematical framework is more complex and requires understanding how norm penalties impacts portfolio optimization. From a numerical point of view, it also requires the implementation of non-traditional algorithms based on ADMM methods and proximal operators.

Keywords: Robo-Advisor, Asset Allocation, Active Management, Portfolio Optimization, Black-Litterman Model, Spectral Filtering, Machine Learning, Tikhonov Regularization, Mixed Penalty, Ridge Regression, Lasso Method, Sparsity, ADMM Algorithm, Proximal Operator

JEL Classification: C61, C63, G11

Suggested Citation

Bourgeron, Thibault and Lezmi, Edmond and Roncalli, Thierry, Robust Asset Allocation for Robo-Advisors (September 1, 2018). Available at SSRN: https://ssrn.com/abstract=3261635 or http://dx.doi.org/10.2139/ssrn.3261635

Thibault Bourgeron

Amundi Asset Management ( email )

90 Boulevard Pasteur
Paris, 75015
France

Edmond Lezmi

Amundi Asset Management ( email )

90 Boulevard Pasteur
Paris, 75015
France

Thierry Roncalli (Contact Author)

Amundi Asset Management ( email )

90 Boulevard Pasteur
Paris, 75015
France

University of Evry ( email )

Boulevard Francois Mitterrand
F-91025 Evry Cedex
France

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