Advanced Portfolio Management. Giuseppe A. Paleologo

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Advanced Portfolio Management - Giuseppe A. Paleologo


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9781119789819 (epdf)

      ISBN 9781119789802 (epub)

      Cover Design: Wiley

      Cover Image: © Giovani Battista Piranesi, Public Domain

      I wrote this book for equity fundamental analysts and portfolio managers, present and future. I am addressing the reader directly: I am talking to you, the investor who is deeply in the weeds of the industry and the companies you cover, investigating possible mispricings or unjustified divergences in valuation between two companies. You, the reader, are obsessed with your work and want to be better at it. If you are reading this, and think, that's me!, rest assured: yes, it's probably you. You were the undergraduate in Chemical Engineering from Toronto who went from a summer job at a liquor store to founding an $8B hedge fund. The deeply thoughtful Norwegian pension fund manager who kept extending our meeting asking questions. The successful energy portfolio manager who interviewed me for my first hedge fund job, and the new college graduate from a large state university in Pennsylvania taking a job as an associate in a financials team.

      There is a second group of readers who will benefit from this book: the quantitative researchers who are, more and more, essential members of fundamental teams. There is not a strict separation between PMs and quantitative researchers. The quantitive researchers will find the appendix useful, if they want to implement programmatically the advanced tools the book describes.

      The book introduces a few themes, and then revisits them by adding details. You will learn how to:

      1 Separate stock-specific return drivers from the investment environment's return drivers;

      2 Size your positions;

      3 Understand your performance;

      4 Measure and decompose risk;

      5 Hedge the risk you don't want;

      6 Use diversification to your advantage;

      7 Manage losses;

      8 Set your leverage.

      The approach I follow is to offer recommendations and best practices that are motivated by theory and confirmed by empirical evidence and successful practice. While I rely heavily on the framework of factor modeling, I believe that even a reader who does not currently have access to a risk model can still get a lot out of it. Day-to-day, several portfolio managers run very successful books without checking their factor risk decomposition every minute. The reason is that they have converted insights into effective heuristics. Wherever I can, I will flesh out these rules of thumb, and explain how and when they work.

      The mathematical requirements are minimal. Having taken an introductory course in Statistics should give the tools necessary to follow the text. Different readers have different objectives. Some want to get the gist of a book. Time is precious, only the thesis matters, its defense doesn't. Gettysburg Address: This new nation was conceived in Liberty, and dedicated to the proposition that all men are created equal. Hamlet: revenge is a futile pursuit. Moby Dick: please, don't hunt whales. To the CliffsNotes-oriented reader, to the secret agent perusing a book between Martinis: there is hope. Just read the sections that are not marked by a “

”. Then there is the detail-oriented reader.

      If you always collect all the trophies when playing a video game, or if you felt compelled to finish War and Peace in high school and didn't regret it: please read all the chapters and sections marked by “

”, but skip the double-starred chapter “
”. You will learn the “Why” of things, not only the “How”. These sections contain empirical tests and more advanced material and their results are not used in the remainder of the book. Finally, for the quantitative researcher and the risk manager, there is the double-starred appendix. Think of this as eleven on the volume knob of a guitar amplifier, as the “Chuck Norris Guide to Portfolio Construction.” If you can read it, you should.

      For those of you who are starting now, you are entering an industry in transition. If you could travel in time to 1995 and visit a portfolio manager's desk, you would have seem him or her using the same tools, processes and data they are using in 2020: Microsoft Excel, to model company earnings; a Bloomberg terminal; company-level models of earnings (also written in Excel), quarterly conferences where one meets with company executives. All of this is changing. Aside from the ever-present game of competition and imitation, two forces are moving the industry. The first is the availability of new data sources. “New”, because storage and computational advances make it possible to collect and process unstructured, transactional data sets that were not collected before. And “available”, because networking and cloud computing reduce dramatically the cost of consuming and managing these data. The second driving force is the transition of new analytical tools from mathematics to technology. Optimization, Factor Models, Machine Learning methods for supervised and unsupervised prediction: these were once advanced techniques that required expertise and relied on immature software prototypes. Now we have tools – technologies, really – that are robust, easy-to-use, powerful and free. Bloomberg and Excel are no longer sufficient, and with that, the toolkit that served the industry for so many years is suddenly incomplete. To meet the new challenges, fundamental teams are hiring “data scientists”. Don't be fooled by the generic title. These are people who need to combine quantitative rigor and technical expertise with the investment process. Very often, they test new data sources; they run optimizations; they test hypotheses that the portfolio manager formulated. Ultimately, however, it is the portfolio manager who constructs the portfolio and supervises the action of the data scientist. The portfolio manager knows alphas, portfolio construction, risk management and data, and these are deeply connected. The success of a strategy is up to her competency and knowledge


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