Quantitative finance has had a significant impact on the financial markets, starting with the shift to electronic equity trading in the 1980s and spreading into bond and credit markets in recent decades. The reasons for the rapid adoption of quant techniques in equities include the liquidity of the equity markets, prevalence of transparent market data on individual equities, and relatively low transaction costs. As quant investing techniques were developed and applied to equities, large institutional investors were able to augment or even replace their historic reliance on discretionary investment management with more systematic, scientific approaches. Quant professionals developed models, employed algorithms, and conducted rigorous back-testing exercises to support their investment theses.
For credit markets, while some quant techniques have proven useful, particularly around methods for assessing value-at-risk, probability of default, and loss-given-default, the pace of adoption has been slower than it was in equities. This is partly due to the repercussions of the global financial crisis, which was driven by the unprecedented rise of credit as an asset class, with disastrous results. While asset-backed securities have a long history in the financial markets, innovative products including collateralized debt obligations and credit default swaps were taken in directions that proved unhealthy for the markets and for investors. Nevertheless, the principles behind credit and structured products are useful and can be profitable for those who fully understand the risks and rewards. Developing and implementing quantitative strategies in credit markets should be based on expertise in the markets more generally, as well as specific domain knowledge of credit as an asset class, and the relevant technical skills to evaluate investment opportunities and the risks inherent in them.
The current environment: Investing in credit under uncertainty
The fundamental process for investing in credit-related products entails assessing bonds or derivatives and harvesting a credit risk premium, while managing the associated risks effectively. Primarily, these risks include specific credit risk and counterparty risk of the entities involved and the more general specter of interest rate risk. As with any investment, market risk, operational risk, and tail risk are also present. Through the lens of quantitative finance, a credit risk premium is tied to an estimation of the default risk in a given bond, while taking the probability of recovery into account. In the current market, credit analysts and managers must assess the usual risk factors, making decisions of the quality of the issuers and whether to 1) apply leverage, 2) engage in transactions in high yield, but riskier securities, and 3) apply hedging strategies to mitigate risks.
The credit markets are generally the domain of larger, more sophisticated investors including multi-strategy and quantitative asset managers, and hedge funds. Despite the downturn suffered in the 2007-2009 period, the credit markets have grown significantly over the past decade and quantitative approaches are well-suited for this complex area of investment. Quantitative strategies are attuned to deriving insights from vast amounts of data and are also employed in portfolio optimization, diversification, and risk management.
Essential skills for the future of credit analysis
In quantitative finance, the essential tool kit includes in-depth training in financial mathematics, probability and statistics, and computer programming. In recent decades, there has been a dramatic increase in the amount of data available across the global financial markets and machine learning has taken a prominent role in investment analysis, including the credit markets. Looking at global macroeconomic and geopolitical situations, inflation, rising interest rates, the war in the Ukraine, and climate change are all impacting the financial markets. These factors have serious implications for credit markets, which are strongly impacted by interest rates and weighed down by sovereign issues.
In the face of such turbulence those with an interest in quantitative finance can hone their skills in producing data-driven insights, focusing on advanced analytics, including machine learning techniques to generate predictions and optimize investment strategies in a rapidly shifting environment. In addition, business communication skills are highly desirable for clear and concise information sharing with internal teams and externally with clients and regulators.
Conclusion
As the financial markets weather numerous storms in the 2020s, the future holds significant challenges and opportunities for people with strong quantitative skill sets. In this environment, some investors will find it more difficult to produce alpha, while others will benefit from volatility and uncertainty around the world. In quant credit, with its direct relationship to interest rates and reliance on the nuances of risk perception, innovative strategies are emerging, and new products are in development to meet the needs of both issuers and investors. Quant credit professionals with a specialized education that boosts skills in data analysis, machine learning, quant research and more, will benefit now and in the future.
