Giuseppe A. Paleologo - Advanced Portfolio Management

This subject is covered throughout the book, and is the main subject of Chapters 3, 4, and 5.

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.

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.

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.

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.

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.

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.

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.

1 1Among them, The Journal of Portfolio Management, The Journal of Financial Data Science, and the Financial Analysts Journal.

2 2Joe 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].

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 the book, from simple heuristics to advanced optimizations.

When will you need this: Always. This will become your second nature. You will break the ice at cocktail parties mentioning how much risk decomposition helped you in your life.

On July 3, 1884, the Customer's Afternoon Letter (owned by Dow Jones & Co.) began publishing the first stock index: a simple price average of nine transportation companies and two industrial ones. In 1886 it published the first Dow Jones Industrial Average. In 1889, the newspaper became The Wall Street Journal , and over time more indices were created. Indices provided a benchmark against which to compare one's investment; and they are a summary of the overall behavior of the market or of a specific sector. A typical benchmarking exercise: if we hold a stock, on any given day we first look at the overall market return, as provided by the index, and then we compute the out- or underperformance of the stock compared to the market. When we look at indices as market summaries, we implicitly know that they describe most, or at least some, of the stock returns for that market segment. In a very real way, having an index gives us a way to describe performance and variation of stock returns. Factor models capture these two intuitive facts, make it rigorous, and extend them in many directions.

A first extension aims to offer more flexibility in the relationship between stock and benchmark. For example, a cyclical stock in the financial sector like Synchrony Financial (ticker: SYF) moves more in sync (lame pun) with the market than, say, Walmart (ticker: WMT), a large, stable, defensive stock. Figure 3.1bears this out. We take the daily returns of Synchrony and Wal-Mart and regress them against the daily returns of SPY. 1 The regression coefficient is denoted “beta”. If the market returns an incremental 1%, the stock returns an incremental (beta) * (1%), everything else being equal. It is a measure of market sensitivity that differs from stock to stock. Figure 3.1shows the regression of SYF daily returns against SP500 futures returns, for the period January 2, 2018, to December 31, 2019. Let us go with the assumption that we can estimate the true beta of a stock to the market; i.e., the estimation error of the beta doesn't really matter. Then a simple decomposition of market return + stock-specific return gives us a great deal of information. On the benchmarking side, we now know what fraction of the return is attributable to the market. It may seem that SYF is outperforming the market in a bull market and Wal-Mart is underperforming. However, after decomposing returns, it may be the other way around: SYF is just a leveraged bet on the market, and after we remove the market contribution, SYF has underperformed, and WMT has outperformed. Another benefit from the linear relationship between market and stock returns is that it establishes a relationship among all stock returns. The market is the common link. For the overwhelming majority of stocks, the beta to the market is positive, but it can take values in a wide interval; several stocks exhibit betas higher than two. This relationship has implications for expected returns and risk as well. We will delve deeper into both later in this chapter. But before we proceed, we introduce a new term, “alpha”. In your daily job, it's alpha that will pay your salary. Beta, on the other hand, can get you fired. This explains why so many portfolio managers have the symbol tattooed on their bodies, while no one ever thought of getting a tattoo with the symbol . Not even risk managers.