- Why Use Analytics?
- What Is Data Analytics?
- How Data Analytics Is Used and How It Differs from Credit Analysis
- An Example
- How This Book Is Structured
How This Book Is Structured
Section I outlines why the corporate debt markets are so different from other asset classes. This section highlights some of the problems and difficulties with trying to undertake analytics in the corporate debt markets. It also discusses some of the common data sources.
Section II goes over some of the key terms used in the marketplace and common to analytics and also reviews typical tools that are used in the markets. If you are familiar with the markets, you might choose to skim this section. If you are relatively new to the markets or are coming from the programming and system side, you might not be familiar with the topics covered in this section and should find it helpful.
Section III summarizes the definitions of the various markets within the overall spectrum of corporate debt. It also outlines who the major players are in the corporate debt market and how they utilize data analytics.
Section IV covers indexes. Jonathan Blau discusses the details and design of corporate debt market indexes. Indexes are the most widely used source for performance comparisons of various markets and for portfolio performance attribution and comparison. Understanding how these indexes work, the different methodologies, and their shortcomings is key in understanding much of the everyday analytics that goes on regarding market data.
Section V examines how data analytics is used starting from a top-down approach. This macro approach starts with examining performance and relative value at the market level and then works its way down to analyzing key subsectors of the markets to develop investment themes and capture trends that might be occurring within a given market. We then explore some of the tools used to derive lists of possible credits to select that can meet the investment themes that are developed.
Section VI focuses on analyzing supply-and-demand trends in the marketplace, known commonly as technicals. Understanding the trends in market technicals can be critical in helping to make timing decisions and weighting decisions in the marketplace.
Section VII explores special vehicles that have evolved in the market. Miranda Chen, an expert on these products, authored this section, which outlines liquid bond indexes, credit default swap indexes, and exchange-traded funds and shows how they depend on analytics for their construction. This section also outlines why monitoring these vehicles can help give insight into market trends and technical more quickly than some other sources of data.
Section VIII explores collateralized loan obligations. These structured products also utilize analytics systems to be structured and to maintain their portfolios within the rules that they have to operate. Similar to the other structures’ vehicles, understanding and monitoring data on these products can add insights into analyzing trends in the corporate bank loan markets.
Section IX outlines the key features of portfolio analysis and performance attribution. This is one of the most developed uses of analytics in the market and we would expect to see the use of such products expand and evolve. This section is written by Alexander Chan, who has worked on developing and running several of the early attribution systems and some of the most current and up-to-date systems.
Section X takes a look at some of the possibilities of where data analytics for corporate debt might be heading in the coming years.
We then include some closing remarks.