An alternative to traditional Euro Credit Management: a Smart Beta Credit approach incorporating ESG criteria

How should we approach bond management as the end of a bull market cycle unprecedented in terms of its size and duration begins? Over the past two decades, bond portfolios have been boosted by the steady fall in interest rates. This has reduced the importance of bond selection – all investors have needed to do to prosper has been to increase their portfolio’s duration and its exposure to credit risk and less liquid assets.

But now, against a backdrop of persistently low interest rates and increasingly stringent regulations, risk management has become a major concern for bond investors. This has been reinforced by an increase in market uncertainties: in Europe, political risk has come to the fore and growth prospects are falling, forcing the European Central Bank to postpone rate hikes, although it is withdrawing its program of massive liquidity injections. In the US, meanwhile, the future direction of long-term rates, which currently seem to be peaking, is unclear.

In this kind of environment, investors need to rethink their approach to fixed income management if they want to maximise their returns while controlling risk. And the stakes are high for European institutional investors, whose portfolios contain an average of 30% in credit!

Smart beta: an attractive option for credit investors

They may fairly represent the broad fixed income markets, but replicating traditional bond indices is not the best way of allocating risk in a bond portfolio. Structurally biased in favour of the most indebted issuers, they often suffer from poor diversification. What’s more, they can be difficult to replicate because transaction fees hit the returns that investors ultimately receive, and some issues are difficult to invest in practice.

A good bond strategy needs to nullify the biases that traditional bond indices involve, while at the same time focusing on allocating to liquid, investable issues. Smart beta bond indices are a good example of such an approach. They provide a different kind of exposure to the markets, but without adding undue duration or credit risk. And as they are managed according to a systematic approach that is free from any human emotional biases, they can exploit opportunities throughout what are very wide investment universes – a particularly valuable characteristic in view of the growing size of the bond markets. Their management approach is also transparent, which makes it possible to better control the strategies’ behaviour in different market regimes.

What’s more, quantitative approaches such as smart beta or factor investing can easily be adapted into a dedicated format to suit individual clients’ needs. This might mean, for example, meeting a particular solvency capital requirement or matching a certain set of liabilities, complying with specific ESG requirements, or just abiding by regulations that constrain the investment universe. Quantitative approaches can also be combined with hedging strategies for investors who would like to, for example, achieve an absolute return but completely remove their exposure to interest rate risk.

In just a few years, smart beta indices have emerged as complementary tools to traditional indices within the equity universe. Low-risk factor strategies have proven particularly popular for their ability to diversify and reduce the risk level of an equity portfolio without affecting its overall performance.

The use of smart beta indices in credit management has so far remained limited due to a series of obstacles inherent to the asset class – but that can be overcome. The development of accurate databases covering a broad credit universe is paving the way for the launch of factor-based credit strategies. Meanwhile, recent academic research has provided some answers to the difficulties involved in launching smart beta strategies in the heterogeneous credit universe, setting out the most relevant factors for investors to take into account.

What are the best factors to use?

Not all traditional risk factors, such as value, momentum, low risk, size, and quality, are suitable for building smart beta bond strategies or portfolios. For example, the momentum factor is not as attractive as it is for equities, particularly for investment-grade bonds compared to high yield bonds, because it involves high turnover, which means significant transaction costs for a credit portfolio. Neither does the size factor work well in the credit universe as low size tends to mean low liquidity. The value and low-risk factors, on the other hand, work well in credit.

CPR AM combines low-risk and ESG in its Smart Beta Credit strategy

CPR AM has focused on the low-risk factor in launching the first of its new range of smart beta credit funds. Our Smart Beta Credit Low Risk strategy meets investors’ needs today and should also be able to do so in the future. It integrates not just traditional measures used within credit risk management, but also ESG factors within its definition of low risk. The strategy attaches great importance to liquidity risk, and it looks for complementary sources of return so that risk reduction is not achieved to the detriment of performance.

How can a low-risk credit strategy work? Choosing the right risk indicator is vital. A low-risk strategy aims to provide a return comparable to that of the broad asset class over the long term, but with a lower level of risk. In equities, low-volatility strategies generally buy the stocks with the lowest volatilities. However, this approach cannot be applied to credit because, other than in periods of stress, the volatility of investment-grade credit can be very close to that of high-yield credit (even though high yield is much riskier than investment grade).

Duration-based measures are much more effective than volatility when it comes to measuring the risk of credit investments. Academic literature has embraced the DTS (duration times spread) measure, which measures the impact of a bond’s credit spread and its duration on its risk profile. For example, a corporate bond with a low spread and long duration may have similar risk to a bond with a high spread and short duration. Unlike volatility, this measure is based on up-to-date data – not what happened in the past. The indicator is therefore not affected by the aging of securities or new issues in the universe. This makes it easy to compare issues and perform backtests.

To achieve long-term returns comparable to those of investment-grade credit but with a consistently lower level of risk, CPR AM has chosen to combine the DTS risk measure with a rigorous specific risk control and structural sources of return to enhance performance. Controlling the liquidity risk of every issue the strategy invests in is also an important part of the process.

Our analysis focuses on filtering out issuers that involve a significant specific risk, either for fundamental reasons identified by our credit analysis team, or for extra-financial reasons identified by our in-house ESG specialists!

Analysis of the credit investment universe reveals that duration risk is poorly compensated over the long term, and that securities with the lowest ratings within the investment-grade category offer the best risk-reward profile. This is an incentive to invest in securities with a maturity of between 1 and 5 years, with a focus on BBB rated issuers.

We also believe that it’s a mistake to immediately sell bonds in portfolio that have been downgraded to BB – known as fallen angels. This help us avoid the crystallisation of losses incurred at the time of the downgrade. Historically, the rise in these bonds’ spreads has tended to take place before the downgrade occurs, and they generally recover thereafter. In addition, our investment strategy is not 100% systematic as we invest in the primary market to capture issuance premiums.

The strategy has exciting potential. Historical simulations show that our approach would have achieved returns in line with that of euro investment-grade credit over the medium term (3-5 years), but with 30% lower volatility and a 40% lower maximum drawdown.