Enhanced corporate bonds: Where algorithms meet capital market expertise

How can an enhanced approach be implemented to reliably offer added value? This is precisely where Quoniam’s combination of models, infrastructure and experts comes in to play.

Corporate bond enhanced strategies with systematic approaches

Investors are looking for strategies that deliver added value compared to the benchmark while keeping risk under control. This is particularly important for strategies with low tracking error, as even small deviations can significantly affect the risk profiles and regulatory requirements of institutional investors over the long term.

Quoniam limits the deviation from the benchmark by systematically monitoring clearly defined target tracking errors and applying with science-based forecast models and active risk management. This enables us to target controlled outperformance.

In the first part of our analysis, we showed why passive approaches in the corporate bond sector are never entirely passive. To overcome these challenges, it is worth taking a look at the advantages of a systematic approach – and how the Quoniam Enhanced strategy implements it in practice.

Our process aims to achieve:

• Performance advantages: Our goal is gross outperformance of 0.3%–0.4% to exceed the benchmark on a net basis and deliver excess returns despite comparable risk.
• High risk-adjusted performance: We aim for one unit of gross alpha per unit of tracking error, resulting in an information ratio of 1. This offers investors the highest possible risk-adjusted performance.
• Efficient implementation: Low transaction costs are fundamental to the success of enhanced strategies. Our transaction costs are demonstrably below the market average, enabling efficient implementation of the strategy.
• Successful risk management: Whether it’s accurately estimating individual bond risks, forecasting downgrades to the high-yield segment or dealing with company-specific special situations, risk management is part of the DNA of a systematic approach. By avoiding unnecessary risks, we increase the likelihood of outperforming.
• Individual solutions: Exclusions from the benchmark, sustainability criteria, maturity or sector restrictions – Client portfolio remains tailor-made.

Building on this process for day-to-day portfolio management, our approach offers further strategic strengths in risk management.

Controlled risk dimensions

A successful enhanced portfolio takes many risk dimensions into account simultaneously – from interest rate and sector risks to sustainability risks. Specific to an enhanced corporate bond portfolio, this means:

The large number of risks that need to be considered simultaneously makes it clear that a systematic approach is advantageous, one in which an algorithm guarantees that all criteria are met simultaneously. Meanwhile, the portfolio manager monitors the optimisation results and supplements the computer’s precision with human expertise.

Combining technology and capital market expertise

A systematic approach combines the strengths of technology-based, automated processes with “human intelligence” based on capital market expertise. When algorithms outperform humans, computers are used. This is especially true when it comes to the simultaneous and systematic evaluation of large data sets, and it is particularly useful in the following areas:

Automated processes

• Risk monitoring: A large number of risk signals at bond level and for the overall portfolio have to be evaluated every day. Are there any significant deviations from the benchmark or from the previous day’s values? Algorithms can be set up to compile all anomalies and make them available to the portfolio manager.
• Yield forecasts: To achieve outperformance within the risk limits, our models find attractively valued bonds and generates a daily yield forecast for all bonds.
• Risk forecasts: To construct risk-adjusted optimal portfolios, a risk forecast for all bonds is also required. This applies to both systematic and issuer-specific risks. Downgrade probabilities can also be easily integrated.

While models and software handle standard processes, experienced portfolio managers intervene to reduce risk wherever there are special situations that require experience and expertise. These include in particular:

Discretionary elements

• Issuer-specific special situations: A company takeover, a natural disaster or a short seller report: there are many situations in which a model may not have all the information relevant for assessing earnings opportunities and risks. Portfolio managers can assess the respective situation and decide how to proceed with regard to a security’s deviation risk from the benchmark.
• Stress tests and scenario analyses: War in the Middle East with an impact on oil prices, trade wars with varying effects on countries and industries, or elections and referendums – today, there are many risks lurking beneath the surface of the market that are not always apparent. Through extensive stress tests and scenario analyses, portfolio managers can gain an understanding of the potential impact of certain risks and reduce risk if necessary.

Conclusion

Combining human judgement with the precision of powerful models creates portfolios that have the potential to outperform the benchmark even after costs – with controlled deviation risks.