Preface
“Analytics” and “Big Data” have become buzzwords in many industries, and have dominated the news over the past few years. In finance, analytics and big data have been around for a long time, even if they were described with different terms. As J.R. Lowry, chief operating officer of State Street Global Exchange, stated in a 2014 interview published in the MIT Sloan Management Review, “In general, data and analytics have pervaded our business for many, many years, but it wasn't something that we were focused on in any kind of coherent way.”
The need to focus on investment analytics in a coherent way has never been greater. In the aftermath of the 2007–2009 financial crisis, there has been a tremendous amount of regulatory change. Like most industries, the financial industry is trying to cope with the challenges of managing big data and the risks associated with using models. Many asset management firms face increasing pressure to address important questions such as
- How to measure, visualize, and manage risks better?
- How to find new sources of return?
- How to manage trading activity effectively?
- How to keep costs down?
The solution of banking giant State Street Corporation was to launch a new business, State Street Global Exchange (SSGX), which applies “a wrapper of information, insights and analytics around the investment process,” and provides a “more purposeful approach to data and analytics across the company.”1 SSGX is a center that has pulled in software capabilities and analytics groups focused on risk, as well as electronic trading platforms focused on foreign exchange, fixed income, and derivatives trading.
Portfolio and risk analytics platforms are offered by investment product providers such as Barclays (the POINT Advanced Analytics Platform)2 and BlackRock (the Aladdin Platform)3 with a similar goal of combining sophisticated risk analytics with comprehensive portfolio management, trading and operations tools. Longtime portfolio software vendors (Axioma, IBM Algorithmics, MSCI Barra, and Northfield Information Services) and data providers (Bloomberg, FactSet, Thomson Reuters) are adding both advanced analytics tools and the ability to link to various data sources. New partnerships are being formed—for example, financial data provider Thomson Reuters joined forces with Palantir Technologies, a leading Silicon Valley big data technology company, to create QA Studio, a solution for quantitative research that combines powerful analytics and intuitive visualizations to help with the generation of investment ideas.4 The development of free open source software such as the statistical modeling environment R5 and the open source programming environment Python6 with libraries for financial applications has greatly improved accessibility to analytical tools and has reduced the costs of implementing portfolio analytics solutions.
In this book, we often refer to the traditional asset management company model, in which the focus is on the selection of star portfolio managers in charge of different portions of a firm's funds under management. However, new technologies have been disrupting the investment industry as a whole. The bundling of asset management practice and software platform offerings is a recent phenomenon, as is the democratization of access to financial data7 and trading opportunities.8 The popularity of automated investment services companies, also called robo advisors,9 has been increasing. New-generation asset management companies include Quantopian,10 which provides an analytics and trading platform and crowdsources investment ideas from contributors from all over the world, with the goal of rewarding top performers and applying tested strategies to asset management instead of hiring and managing individual portfolio managers. The core of Quantopian's strategy involves providing useful market and stock fundamentals data, as well as a tool for backtesting, zipline, which has been made open source (free) to help create and support a community of contributors.
Nobody can tell what the future of the portfolio management industry will look like but it certainly seems inevitable that data and analytics will play a major role in it.
Central Themes
Portfolio Construction and Analytics attempts to look at the analytics process at investment firms from multiple perspectives: the data management side, the modeling side, and the software resources side. It reviews many widely used approaches to portfolio analytics and discusses new trends in metrics, modeling approaches, and portfolio analytics system design. The theoretical underpinnings of some of the modeling approaches are provided for context; however, our goal is to emphasize how such models are used in practice.
The book contains 18 chapters in six parts. Part One, Statistical Models of Risk and Uncertainty, contains the fundamental statistical modeling concepts necessary to understand the modeling and measurement of portfolio risk. Part Two, Simulation and Optimization Modeling, explains two important modeling techniques for constructing portfolios with desired characteristics and evaluating their risk and performance—simulation and optimization. Part Three, Portfolio Theory, introduces the classical quantitative portfolio risk optimization approach and new tools for optimizing portfolios, both in terms of total risk and in terms of risk relative to a selected benchmark. Parts Four and Five, Equity Portfolio Management and Fixed Income Portfolio Management, focus on specific factors and strategies used in equity and fixed income portfolio management, respectively. Part Six describes the basics of financial derivative instruments and how financial derivatives can be used for portfolio construction and risk management.
The material is presented at a high level but with practical real-world examples created with R and Microsoft Excel or provided by established portfolio software vendors, and should be accessible to a broad audience. We believe that practitioners and analysts who would like to get an overview of tools for portfolio analytics will find these themes—along with the examples of applications and instructions for implementation—useful. At the same time, we address the topics in this book in a rigorous way, and provide references to the original works, so the book should be of interest to academics, students, and researchers who need an updated and integrated view of portfolio construction and analytics.
Software
We were wary of using a specific software package and turning this book into a software tutorial because the popularity of different tools changes quickly. The examples in this book were created with Microsoft Excel and R, as well as portfolio risk management software by Barclays Capital and FactSet. We assume basic familiarity with spreadsheets and Microsoft Excel. Because of the wide variability of online resources and tutorials for Microsoft Excel and the open source software package R, we do not provide tutorials with the book;11 however, we try to provide hints for the implementation of the examples with R and point to the libraries that have the analytics capabilities needed to implement the examples.12
Teaching
Portfolio Construction and Analytics covers finance and applied analytical techniques topics. It can be used as a textbook for upper-level undergraduate or lower-level graduate (such as MBA or master's) courses with emphasis on modeling, such as applied investments, financial analytics, or the decision sciences. The book can be used also as a supplement in a special topics course in quantitative methods or finance, as a reference for student projects, or as a self-study aid by students.
The book assumes that the reader has only very basic background in finance or quantitative methods, such as understanding of the time value of money, knowledge of basic calculus, and comfort with numbers and metrics. Most analytical concepts necessary for understanding the notation or applications are introduced and explained in footnotes or in specified references. This makes the book suitable for readers with a wide range of backgrounds.
Every chapter follows the same outline. The concepts are introduced in the main body of the chapter, and illustrations are provided. Instructions for implementation of the examples are provided in footnotes. There is a summary that contains the most important discussion points at the end of each chapter.
A typical course may start with the material in Chapters 1 through 6. It can then cover Chapters 8 through 14, which discuss equity and fixed income portfolio construction strategies. Chapters 7 and 15 contain special topics that would be of interest in more quantitatively oriented courses and more advanced finance courses, respectively, or can be assigned for student projects. Depending on the amount of time an instructor has, Chapters 16 through 18 would be good to include in a course on investment management, as they discuss the fundamentals of portfolio risk management with financial derivative instruments.
Disclosure
Frank J. Fabozzi is a member of two board fund complexes where BlackRock Inc. is the manager of the funds. Mention of BlackRock's analytics or products in this book should not be construed as any form of endorsement.