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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 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].