Integrating multiple sources of ordinal information in portfolio optimization

TL;DR

This paper explores two methods for integrating qualitative ordinal views into portfolio optimization, comparing robust optimization and social choice-based order aggregation, with empirical results favoring social choice methods.

Contribution

It introduces a novel combination of Black-Litterman with social choice theory for portfolio optimization and compares it to robust optimization approaches.

Findings

Social choice methods outperform robust optimization in empirical tests.

Aggregating ordinal information improves portfolio performance.

The approach is validated on EUROSTOXX 50 and S&P 100 data.

Abstract

Active portfolio management tries to incorporate any source of meaningful information into the asset selection process. In this contribution we consider qualitative views specified as total orders of the expected asset returns and discuss two different approaches for incorporating this input in a mean-variance portfolio optimization model. In the robust optimization approach we first compute a posterior expectation of asset returns for every given total order by an extension of the Black-Litterman (BL) framework. Then these expected asset returns are considered as possible input scenarios for robust optimization variants of the mean-variance portfolio model (max-min robustness, min regret robustness and soft robustness). In the order aggregation approach rules from social choice theory (Borda, Footrule, Copeland, Best-of-k and MC4) are used to aggregate the total order in a single ``consensus total order''. Then expected asset returns are computed for this ``consensus total order'' by the extended BL framework mentioned above. Finally, these expectations are used as an input of the classical mean-variance optimization. Using data from EUROSTOXX 50 and S&P 100 we empirically compare the success of the two approaches in the context of portfolio performance analysis and observe that in general aggregating orders by social choice methods outperforms robust optimization based methods for both data sets.