A Synthetic Data-Plus-Features Driven Approach for Portfolio Optimization

Bernardo K. Pagnoncelli (), Domingo Ramírez (), Hamed Rahimian () and Arturo Cifuentes ()
Additional contact information
Bernardo K. Pagnoncelli: Université Côte d’Azur
Domingo Ramírez: Pontificia Universidad Católica de Chile
Hamed Rahimian: Clemson University
Arturo Cifuentes: CLAPES-UC

Computational Economics, 2023, vol. 62, issue 1, No 8, 187-204

Abstract: Abstract Features, or contextual information, are additional data than can help predicting asset returns in financial problems. We propose a mean-risk portfolio selection problem that uses contextual information to maximize expected returns at each time period, weighing past observations via kernels based on the current state of the world. We consider yearly intervals for investment opportunities, and a set of indices that cover the most relevant investment classes. For those intervals, data scarcity is a problem that is often dealt with by making distribution assumptions. We take a different path and use distribution-free simulation techniques to populate our database. In our experiments we use the Conditional Value-at-Risk as our risk measure, and we work with data from 2007 until 2021 to evaluate our methodology. Our results show that, by incorporating features, the out-of-sample performance of our strategy outperforms the equally-weighted portfolio. We also generate diversified positions, and efficient frontiers that exhibit coherent risk-return patterns.

Keywords: Portfolio choice; Investment decisions; Optimization techniques; Machine learning; Simulation methods (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10614-022-10274-2 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:kap:compec:v:62:y:2023:i:1:d:10.1007_s10614-022-10274-2

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-022-10274-2

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().