You have great investment ideas. If you turn them into highly profitable portfolios, this book is for you.
Advanced Portfolio Management: A Quant's Guide for Fundamental Investors is for fundamental equity analysts and portfolio managers, present, and future. Whatever stage you are at in your career, you have valuable investment ideas but always need knowledge to turn them into money. This book will introduce you to a framework for portfolio construction and risk management that is grounded in sound theory and tested by successful fundamental portfolio managers. The emphasis is on theory relevant to fundamental portfolio managers that works in practice, enabling you to convert ideas into a strategy portfolio that is both profitable and resilient. Intuition always comes first, and this book helps to lay out simple but effective "rules of thumb" that require little effort to implement and understand. At the same time, the book shows how to implement sophisticated techniques in order to meet the challenges a successful investor faces as his or her strategy grows in size and complexity. Advanced Portfolio Management also contains more advanced material and a quantitative appendix, which benefit quantitative researchers who are members of fundamental teams.
You will learn how to:
Separate stock-specific return drivers from the investment environment's return drivers
Understand current investment themes
Size your cash positions based on
Your investment ideas
Understand your performance
Measure and decompose risk
Hedge the risk you don't want
Use diversification to your advantage
Manage losses and control tail risk
Set your leverage
Author Giuseppe A. Paleologo has consulted, collaborated, taught, and drank strong wine with some of the best stock-pickers in the world; he has traded tens of billions of dollars hedging and optimizing their books and has helped them navigate through big drawdowns and even bigger recoveries. Whether or not you have access to risk models or advanced mathematical background, you will benefit from the techniques and the insights contained in the bookâand won't find them covered anywhere else.
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.
I imagine that these readers are at different stages in their careers. Since the companies they cover are fundamentally different, they do think in different ways. But they all share a feature: they all have valuable trading ideas but realize that having good ideas is useless without the knowledge of how to turn them into money. This is the objective of portfolio construction and risk management: how to put together a portfolio of holdings that will be profitable over time and will survive adversities. This book is a short, incomplete guide toward investment enlightenment.
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.
1.1 What You Will Find Here
The book introduces a few themes, and then revisits them by adding details. You will learn how to:
Separate stock-specific return drivers from the investment environment's return drivers;
Size your positions;
Understand your performance;
Measure and decompose risk;
Hedge the risk you don't want;
Use diversification to your advantage;
Manage losses;
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.
1.2 Asterisks; Or, How to Read This Book
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 â
â. 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 â
â, but skip the double-starred chapter â
â. You will learn the âWhyâ of things, not only the âHowâ. 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 âChuck Norris Guide to Portfolio Construction.â If you can read it, you should.
1.3 Acknowledgments
I thank Qontigo (formerly Axioma) for making available its US risk model; special thanks to Chris Canova and Sebastian Ceria. Samantha Enders, Purvi Patel, and Bill Falloon at Wiley guided the book composition from the first phone call to its publication. The following people read the book and provided corrections and feedback: Ashish Bajpai, Victor Bomers, Omer Cedar, Phil Durand, François Drouin, Ross Fabricant, Tom Fleming, Izabella Goldenberg, Ernesto Guridi, Dimitrios Margaritis, Chris Martin, Michael Medeiros, Gurraj Singh Sangha, Ashutosh Singh, David Stemerman, Thomas Twiggs, Davide Vetrale, and Bernd Wuebben. I also owe much to people with whom I discussed â and from whom I learned about â several of these topics. Although they are too many to mention them all, Ravi Aggarwal, Brandon Haley, Gustav Rydbeck, Fabian Blohm, Costis Maglaras, Sai Muthialu, Vishal Soni and Samer Takriti, and, again, Sebastian Ceria have taught me most of what I know. All remaining errors are mine.
Chapter 2 The Problem: From Ideas to Profit
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. âNewâ, because storage and computational advances make it possible to collect and process unstructured, transactional data sets that were not collected before. And âavailableâ, 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, Machi...
