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📄 Contents

  1. Why Use Analytics?
  2. What Is Data Analytics?
  3. How Data Analytics Is Used and How It Differs from Credit Analysis
  4. An Example
  5. How This Book Is Structured
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How Data Analytics Is Used and How It Differs from Credit Analysis

If the fundamental credit analysis, which is so important in making proper investment decisions, starts from the bottom up, data analytics can best be viewed as working from the top down. The work can start from examining some of the macro data on the various fixed-income markets and comparing data across these markets. The next step down the ladder would be to start examining the various subsets of a given market. All of this analysis should encompass return analysis as well as comparisons of relative value and volatility. You will likely run this analysis through historical cycles and be trying to extract how past trends might give you a road map into the future or what relationships seem out of line from historic trends and present opportunities or risks. You may spend a particular amount of time examining specific periods of heightened volatility and how various market segments reacted during these times.

This macro work will help you to develop themes and strategies that you and your team will need to follow in your investing or trading strategies. It will also help you understand where your current strategies have you positioned. This may all lead to your developing investment themes you want to pursue.

To find the specific investment ideas that you want to delve into, you will likely use analytics on a database using queries and data sorts to develop narrower and narrower lists of securities that meet the criteria for your investment themes. One of the keys to the quality of the analytics is how robust the database is.

After you develop these lists, the detailed fundamental and relative value analysis can kick in for credit selection. A portion of the analysis that you need to do to best understand when and where to invest includes technical analysis of supply and demand in the marketplace. Many of the items that you will use for your analysis have their own idiosyncrasies that you need to understand to best do your analysis.

One of the most widely used tools is market indexes, which each have benefits and drawbacks that need to be understood. It is also critical to understand credit default swap (CDS) indexes, exchange-traded funds (ETFs), and collateralized loan obligations (CLOs), all of which rely heavily on data analytics to be run and managed. In doing all this analysis, you need to understand who the end user is as well as the other players who can influence the marketplace. You also need to understand the basics of bond math as well as statistics—both of which are necessary for undertaking this work.

If much of the analytic work just described is to reach a conclusion about where you should be best positioned for optimal performance, performance attribution shows you how you have been positioned and how that has impacted you. Performance attribution analysis is a culmination of much of the data analytics tools that are used across the marketplace.

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