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.
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 “
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 “
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 – and from whom I learned about – 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. “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 of these topics. A good portfolio manager can be – and should be! – a good risk manager, too. I believe it is possible to explain the basics of a systematic approach to portfolio construction without resorting to advanced mathematics and requiring much preexisting knowledge. This book is an elementary book in the sense that it assumes very little. I hope most readers will find in it something they already know, but that all readers will find something they did not know.
It seems inevitable that many books on this important subject must exist. In my years spent working as a quantitative researcher and consultant for the sell- and the buy-side, I have never been able to wholeheartedly recommend a book to my clients and colleagues that would help them in their endeavors. Like Italian art during the Renaissance, real-world finance works through a system of apprenticeship. Finance practitioners acquire most of their knowledge by doing and experiencing things. They talk and listen to risk-takers like themselves. They believe portfolio managers more than “managers” who have never managed a portfolio. They have a strong incentive not to share knowledge with outsiders, in order to protect their edge. All of this conspires against the existence of a good book on portfolio construction. Although the distance between professionals and academics is smaller in Finance than in other disciplines, it is still wide; the specific subject of portfolio management is covered by only a handful of journals.1
Summing up, there is no master theory yet of portfolio management. There are problems and technologies to solve in part these problems. Theories come and go; but a solution to a real problem is forever. As you explore portfolio management, you will find papers on optimization, position sizing, exploratory analysis of alternative data, timing of factors. Keep in mind the following maxim, which I paraphrase from a seminal paper on reproducible research:
An article about the theory of portfolio management is not the scholarship itself, it is merely advertising of the scholarship.
[Buckheit and Donoho, 1995]
Always look for simulation-based validations of a theory, and question the soundness of the assumptions in the simulation; and always look for empirical tests based on historical data, while being aware that these historical tests are most interesting when they show the limits of applicability of the theory, not when they confirm it [López de Prado, 2020].
Now, what are the problems?
2.1 How to Invest in Your Edge, and Hedge the Rest
Perhaps
