In this new white paper, senior members of our multi-asset team and quantitative research group propose a robust framework for an industrialised implementation of asset allocation strategy tailored to meet the requirements of individual portfolios.
A three-step framework
The robust framework proposed consists of three steps:
- A unique unconstrained tactical portfolio is created by relating the conviction in each view to the allocation of risk budget to the assets underlying the view.
- Tailored portfolios with investor-specific constraints and targets are constructed using robust portfolio optimisation based on implied active returns derived from the unique unconstrained tactical portfolio. The implied returns are derived from reverse optimisation using the same robust approach. Robust optimisation is the core engine of the industrialisation process. It produces portfolios consistent with the views while complying with constraints without requiring human intervention.
- Finally, a factor-based risk model endows the framework with transparency by allowing for comparison of risk-factor exposures in portfolios with those in the original views’ exposures.
Empirically tested
Through empirical examples, our team of authors show that robust portfolio optimisation produces allocations that are consistent with views while fulfilling constraints, avoiding the weaknesses of mean-variance optimisation. This robustness simplifies the industrialisation of the construction of customised portfolios.
The adoption of a statistical factor-based risk model is key to ensuring transparency. Comparison of risk-factor exposures in portfolios with those in the original views allows investors to gauge whether the framework manages to retain the risk-factor exposures contained in views.
The steps of the proposed framework are designed to efficiently implement the views into customised investor portfolios in a robust manner while complying with risk aversion constraints.
Improving operational efficiency
The improvement of operational efficiency and automation brought about by this framework significantly reduces the delay between decisions and their implementation across a large range of portfolios (‘time to market’) and allows portfolio managers to focus on the core task of generating performance for investors.
This framework can be also deployed for robo-advisors that aim at adding value with tactical asset allocation or adapted for types of active investing.
- Read ‘MULTI-FACTOR ALLOCATION: BNP PARIBAS ASSET MANAGEMENT’S PRINCIPLES FOR REDESIGNING TACTICAL ASSET ALLOCATION’ by Tarek Issaoui, Olivier Retiere, Romain Perchet, Francois Soupe and Chenyang Yin
Any views expressed here are those of the author as of the date of publication, are based on available information, and are subject to change without notice. Individual portfolio management teams may hold different views and may take different investment decisions for different clients. This document does not constitute investment advice.
The value of investments and the income they generate may go down as well as up and it is possible that investors will not recover their initial outlay. Past performance is no guarantee for future returns.
Investing in emerging markets, or specialised or restricted sectors is likely to be subject to a higher-than-average volatility due to a high degree of concentration, greater uncertainty because less information is available, there is less liquidity or due to greater sensitivity to changes in market conditions (social, political and economic conditions).
Some emerging markets offer less security than the majority of international developed markets. For this reason, services for portfolio transactions, liquidation and conservation on behalf of funds invested in emerging markets may carry greater risk.
