End-to-end risk budgeting portfolio optimization with neural networks

A. Sinem Uysal (), Xiaoyue Li () and John M. Mulvey ()
Additional contact information
A. Sinem Uysal: Princeton University
Xiaoyue Li: Princeton University
John M. Mulvey: Princeton University

Annals of Operations Research, 2024, vol. 339, issue 1, No 16, 397-426

Abstract: Abstract Traditional stochastic optimization in financial operations research applications consist of a two-step process: (1) calibrate parameters of the assumed stochastic processes by minimizing a loss function, and (2) optimize a decision vector by reference to the investor’s reward/risk measures. Yet this approach can encounter the error maximization problem. We combine the steps in a single unified feedforward network, called end-to-end. Two variants are examined: a model-free neural network, and a model-based network in which a risk budgeting model is embedded as an implicit layer in a deep neural network. We performed computational experiments with major ETF indices and found that the model-based approach leads to robust performance out-of-sample (2017–2021) when maximizing the Sharpe ratio as the training objective, achieving Sharpe ratio of 1.16 versus 0.83 for a pure risk budgeting model. Simulation studies show statistically significant difference between model-based and model-free approaches as well. We extend the end-to-end algorithm by filtering assets with low volatility and low returns, boosting the out-of-sample Sharpe ratio to 1.24. The end-to-end approach can be readily applied to a wide variety of financial and other optimization problems.

Keywords: End-to-end learning; Risk parity; Risk budgeting portfolio optimization; Asset selection (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10479-023-05539-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:339:y:2024:i:1:d:10.1007_s10479-023-05539-4

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-023-05539-4

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().