the simplest and deepest challenge is to understand the limits of your knowledge. If you develop a thesis with regard to the value of a company, you implicitly have a thesis on the peers of that company. All valuation judgements are relative. The question is, relative to what? The goal is to understand the drivers of pervasive returns, i.e., not of returns that we can forecast through deep investigation of a specific company, but rather that have a common explanatory factor; and then measure performance relative to those factors. There are at least two payoffs from following this process:
The first is an improvement in the alpha research process. If you know what the environment is, then you know if a bet on a particular company carries with it unintended bets. Separating the stock from the environment gives you clarity of thought.
The second is an improvement in the risk management process. If you know your environment, you can control your risk much more effectively; specifically, you can effectively reduce the environmental risk and keep only your intended bets; you can hedge out what you don't know.2
This subject is covered throughout the book, and is the main subject of Chapters 3, 4, and 5.
2.2 How to Size Your Positions
Once you have effectively estimated the true stock-specific return, your next problem is converting a thesis into an investment. It stands to reason that, the stronger the conviction, the larger the position should be. This leaves many questions unanswered. Is conviction the only variable? How does stock risk enter the sizing decision? What is the role played by the other stocks in the portfolio?
This is the subject of Chapter 6.
2.3 How to Learn from Your History
According to Plato, Socrates famously told the jury that sentenced him to death that “the unexamined life is not worth living”. He was probably referring to portfolio managers. Billions of people happily live their unexamined yet worthy lives, but not many portfolio managers survive for long without examining their strategies. If you want to remain among the (professionally) living, you must make a habit of periodically revisiting your decisions and learning from them. The life of the good portfolio manager is one marked by continuous self-doubt and adaptation. The distinctive features of a strategy's performance are stock selection, position sizing, and timing skills. The challenge is how to quantify them and improve upon them.
This is the subject of Chapter 8.
2.4 How to Trade Efficiently
Transaction costs play a crucial role in the viability of a trading strategy. Often, portfolio managers are not fully aware of the fact that these costs can eat up a substantial fraction of their revenues. As a result, they may over-trade, either by opening and closing positions more aggressively than needed, or by adjusting too often the size of a position over the lifetime of the trade. Earning events and other catalysts like product launches, drug approvals, sell-side upgrades/downgrades are an important source of revenue for fundamental PMs; how should one trade these events in order to maximize revenues inclusive of costs? Finally, what role should risk management play in event (and, in particular, earnings) trades? Positioning too early exposes the PM to unwanted risk in the days preceding the event.
This is the subject of Sections 8.2.1 and 8.3.
2.5 How to Limit Factor Risk
The output of your fundamental research changes continuously. The rules of your risk management process should not. They should be predictable, implementable, effective. These usually come in the form of limits: on your deployed capital, on your deployed portfolio risk, but also on less obvious dimensions of your strategy; for example, single-position maximum size is an important aspect of risk management. The challenge is to determine the rules that allow a manager to fully express her ideas while controlling risk.
This is the subject of Section 7.2.
2.6 How to Control Maximum Losses
An essential mandate of a manager is to protect capital. The Prime Directive, in almost everything, is to survive. A necessary condition for survival is not to exceed a loss threshold beyond which the future of the firm or of your strategy would be compromised. This is often implemented via explicit or implicit stop-loss policies. But how to set these policies? And how does the choice of a limit affect your performance?
This is the subject of Chapter 9.
2.7 How to Determine Your Leverage
This challenge is not faced by all portfolio managers. When they are working for a multi-PM platform, leverage decisions are the responsibility of the firm. However, a few independent investors do start their own hedge funds, and choosing a leverage that makes the firm viable, attractive to investors, and prudent is perhaps the most important decision they face.
This is the subject of Chapter 10.
2.8 How to Analyze New Sources of Data
New sources of data that go far beyond standard financial information become available every day. The portfolio manager faces the challenge of evaluating them, processing them and incorporating them into their investment process. The ability to transform data and extract value from them will become an important competitive advantage in the years to come. The range of methods available to an investor is as wide as the methods of Statistics, Machine Learning and Artificial Intelligence, and experimenting with them all is a daunting task. Are there ways to screen and learn from data so that the output is consistent with and complementary to your investment process?
This is the subject of Section 8.4.
Notes
1 1 Among them, The Journal of Portfolio Management, The Journal of Financial Data Science, and the Financial Analysts Journal.
2 2 Joe Armstrong, a leading computer scientist and the inventor of the computer language Erlang, uses an effective metaphor for the lack of separation between the object of interest and its environment: You wanted a banana but what you got was a gorilla holding the banana and the entire jungle [Seibel, 2009].
Chapter 3 A Tour of Risk and Performance
What will you learn here: A very gentle introduction to factor models, starting with the simplest example of a model, which you probably already know. And how risk estimation, performance attribution and hedging can be performed using this simple approach.
Why do you need it: Because the themes I introduce here will return over and over again throughout
