One of the most important elements of our systematic strategies is our collaboration with Barclays Quantitative Portfolio Strategy (QPS), an organization that has long been recognized as one of the leaders in credit quantitative research. Each day, Barclays QPS provides individual signal scores for three alpha factorsfor each bond in a specified index. Each signal is scored on a scale of 1-10 for each factor and we used a combined score of the three signals to rank index constituents. Our process seeks to maintain exposure to high scoring bonds.
In this post, we discuss our goals for the construction of portfolios and the choices behind the signals used.
We designed our portfolio construction process to aim for the following goals:
At bottom, we want to be consistent with the “client-first” mindset that drives our team’s decision making. We need to make sure that the systematic portfolios are generating alpha, are in line with client objectives and are measured against an appropriate benchmark.
The first systematic strategies coming out of our collaboration with QPS are focused on investment grade corporate credit where strategies are based on signals related to three factors: Value, Momentum and Sentiment.
For details on our backtesting data, please contact the team.
To implement our systematic portfolios, we rank each bond in an Index by combined signal score and use this in an optimized portfolio construction process. An important step is that we incorporate risk controls in an effort to remove the impacts of certain outside influences on bond performance. On a daily basis, we review the duration-times-spread (DTS), the duration, the sector weightings, the ratings, and the issuer concentration of our systematic portfolio versus the Index, and make sure that the portfolio remains within specified ranges on those parameters. For example, we aim to keep duration +/- 0.25 years of the Index, and sector exposure within +/- 3% of the benchmark sector weights.
Furthermore, we want to minimize costs, and transaction costs associated with turnover can be a significant contributor to implementation costs. Our research indicates that over time the benefits associated with portfolio turnover may be offset by excessive transaction costs as turnover rises above 12-15% per month. So all else equal, we want securities with less turnover.
We are cognizant of transaction costs and the negative impacts they can have on portfolio outcomes. Our analysis shows that over time, portfolios that average approximately 10% monthly turnover strike the right balance between actively seeking opportunities while avoiding excess transaction costs. Therefore, our optimizations cap turnover at 10%.
By incorporating risk and liquidity constraints, we aim to replicate the optimized portfolio with the same risk parameters, while minimizing costs and increasing incremental alpha (Figure 1).
Figure 1: Implementation Considerations Drive Optimization
Optimized Portfolio Structure Maximizes Risk-constrained Factor Exposure
Through our collaboration with Barclays QPS, we can use robust data to designate which securities are optimal for the systematic portfolios. However, transparency and pricing needs to be considered as well. Fixed income markets are broad and fragmented, and even though the asset class has made great strides in trading efficiencies, illiquidity remains a concern for certain issues.
Our capabilities as an index manager allow us to efficiently build portfolios that tightly control signal exposure while optimizing liquidity and minimizing trading costs. We combine the Barclays QPS data excellence with our practical portfolio sampling and implementation experience.
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