Modern Portfolio Management

Moving Beyond Modern Portfolio Theory

Todd E. Petzel

• Publisher: Wiley
• Year: 2021
• ISBN:
  9781119818199
• Edition: 1
• Topic: Business
• Subtopic: Investments & Securities
• Index: Business

Get a practical and thoroughly updated look at investment and portfolio management from an accomplished veteran of the discipline

In Modern Portfolio Management: Moving Beyond Modern Portfolio Theory, investment executive and advisor Dr. Todd E. Petzel delivers a grounded and insightful exploration of developments in finance since the advent of Modern Portfolio Theory. You'll find the tools and concepts you need to evaluate new products and portfolios and identify practical issues in areas like operations, decision-making, and regulation.

In this book, you'll also:

• Discover why Modern Portfolio Theory is at odds with developments in the field of Behavioral Finance
• Examine the never-ending argument between passive and active management and learn to set long-term goals and objectives
• Find investor perspectives on perennial issues like corporate governance, manager turnover, fraud risks, and ESG investing

Perfect for institutional and individual investors, investment committee members, and fiduciaries responsible for portfolio construction and oversight, Modern Portfolio Management is also a must-read for fund and portfolio managers who seek to better understand their investors.

CHAPTER 1
Introduction

There are countless books that try to explain how to manage portfolios of stocks, bonds, real assets, and virtually every other investment opportunity. An obvious question is, “Why does anyone need another one now?” In the past 15 years there has been a gut-wrenching liquidity crisis and stock market crash followed by one of the least loved bull markets in history. Then a booming economy and market was laid low by the COVID-19 pandemic. As people stumble through this minefield, it seems at a basic level either that previous books do not have the right prescriptions or that few investors have learned enough from them.

There are two distinct reasons a new discussion is timely. First, the investment world has shifted dramatically toward more analytically based approaches over the past 40 years, but these models are either defective or were applied incorrectly, leading to the great financial crisis. The second reason regards significant institutional change in the investment world. The old approach had a broker assemble securities into a portfolio. Profits from the old way of managing portfolios have shrunk dramatically because of negotiated commission and other basic competitive forces. To replace those profits, firms have turned to financial engineers to construct customized structured products and more exotic, “scientifically grounded” investment funds for their sales forces. These products appear straightforward on the surface but can have hidden risks and costs that can meaningfully erode investment performance. Today's investor simply must acquire a basic knowledge of how securities, derivatives, and other investment vehicles work before they can make informed portfolio decisions.

Some believe that before the 1970s investing was something of a folk art, full of quaint habits that produced inferior performance. As modern portfolio theory (MPT) developed, the “science” of investing was refined. Investments could be systematically evaluated in terms of risk and combined to produce “optimized” portfolios. By the time the twenty-first century arrived, portfolio management had become highly dependent upon, and some would say dominated by, quantitative methods and financial engineers. The fact that many markets around the world almost melted down in 2008 and early 2009, destroying years of wealth creation and catching legions of quants off guard, suggests that currently accepted portfolio management practice has some serious flaws.

More than a dozen years after the financial crisis, many of the old, bad habits of selling financial products based on their promised analytical superiority have crept back into everyday use. Sadly, few of the deficiencies have been repaired. The structure and precision of our portfolio modeling have been grossly oversold to investors.

Models rely on expected returns, risks, and correlations that are estimated from thousands of data points. Before the widespread use of computers, these calculations took days and weeks to complete. Early finance pioneers like Bill Sharpe and Harry Markowitz would spend considerable time gathering data and carefully thinking about the construction of a problem before rolling up their sleeves and actually cranking out their complicated estimates. Today, finance professionals routinely download data sets in seconds and complete countless calculations in the blink of an eye. Unfortunately, one of the downsides of this is that the cost of poorly specified models to the researcher is almost nil. To the investor, however, the costs have been massive.

Too many people believe that investing can be reduced to a science. Today, portfolio models are based on data sets too large to have been collected or analyzed just a few years ago. Statistics teaches that as the number of observations in the sample grows, greater confidence can be placed on the model, but many of the basic foundations of statistics are not present when dealing with market information. New data are piled upon old, and the models are reestimated. Few critically ask whether the unstated assumptions behind the statistical models truly apply to the markets being studied.

Economics in general and finance in particular are not physical sciences. They are behavioral disciplines, and the difference is profound. A chemist can try to duplicate a complicated physical reaction hundreds of times, each time carefully observing the outcomes. The more tightly the experimental environment can be controlled, the closer the outcomes should cluster. When the data do not fit the model being tested, it is likely due to some error in the model specification. That is how science advances. When the data fail to match the predictions of the model, the scientist rethinks the interaction of the variables, sometimes rearranging them or sometimes discovering that new variables need to be introduced.

In contrast, consider the economist. They may have a collection of thousands of market prices or returns to examine, but unlike the chemist's data set, these observations came from real life and not from any experiments. Each day may bring a new set of market data; however, it was not produced by a controlled experiment, nor can the researcher ever control the environment. Today, economists are being asked to model the impact of exploding federal deficits on interest rates, inflation, and real economic growth. How large and lasting will the impact of COVID-19 be on incomes and wealth? While these are important thought exercises, there is nothing in the history of the United States that provides data on which to build a precise model with these conditions. An examination of other nations that have experienced huge national debt may be suggestive, but none of these countries have had the same population, technology base, or government structures, to name just three of innumerable variables that cannot be controlled. Economic models are simply suggestions about what might happen, and the range of actual outcomes can and does vary widely.

When financial engineers travel the modeling path, the problems are similar. Despite attempts to account for all the factors that play a role, no database drawn from market data can possibly have the statistical rigor of a set of physical science experiments. Not only do the histories fail to reflect all the possible outcomes, but there is also the basic problem of markets: people who invest react to their environments, and the environments, in turn, change because of these actions. An example will show the difference.

Suppose you walk into a casino and notice that everyone around the roulette wheel has bet on 13 black. This could mean one of two things: either the crowd has figured out that the wheel is defective or rigged and 13 black has a higher-than-average chance of happening, or it is a superstitious bunch with a common bias. If the wheel is fair, there is a 1 in 38 chance of the spinning ball landing in 13 black. It does not matter how many people bet on that number in any turn of the wheel; the probability stays the same.

Markets are different.

If large numbers of market participants try to buy Chinese stocks, gold, bitcoin, mortgage securities, or any other traded good, the market price will change. And as seen in the near financial meltdowns in the second half of 2008 and March 2020, when those traders suddenly turn to sell, prices can change in ways that seemingly defy the probabilities. Whenever enough other participants join an investor in the same trade, the price of the trade changes, and the probability of any given subsequent outcome shifts, sometimes in completely unanticipated ways.

The world has too often seen the response to such seismic shifts. The quantitative fund manager, who has frequently just lost a great deal of other people's money, proclaims complete shock that these virtually impossible events occurred. Then they race back to the computer with the new data in hand to try to fix the flaws in the model. At the most basic level, however, it is not the model that is broken; It is the entire premise behind it.

This is not to suggest that quantitative methods do not have a place in portfolio management. However, the pendulum may have swung too far, resulting in inflated expectations for their benefits. Moderation and skepticism, which are very useful tools for investors, have often been pushed aside. When a manager is on a winning streak and gathering investors, it is often hard to ask what can go wrong or to question whether an approach that worked with $100 million under management is likely to work with $20 billion or more. Investors want to believe that a successful manager's historical record is a product of skill and not luck and that skill is as reliable as identifying the true odds on the roulette wheel. This is rarely the case.

The complexity of quantitative approaches to trading and portfolio construction only adds to this mystique for some. If the lay investor cannot comprehend the trading model, it must be profound. But the frequent market disasters that can be linked to these quants, almost always losing great sums of other people's money, should suggest otherwise. A theoretical chemist might be able to recite incomprehensible formulas about how egg protein reacts to heat, but that does not mean they make an edible omelet.

It is time for the pendulum to swing back toward the art of investing to find an appropriate balance. Throughout this book, models will be questioned. Are all the variables considered? Are the estimated models drawn from a broad enough experience? Does the game change if many are looking at the world in a similar way? What can go wrong outside the model? That is why this book is subtitled Moving Beyond Modern Portfolio Theory. It is foolish to completely reject the quantitative advances of the past 50 years. It is equally foolish to believe that pushing further down the same quantitative path today can totally solve the problems in portfolio management.

This book suggests a middle ground. It starts with a quantitative foundation, but there is no promise of completeness or any easy solutions. Nothing will be optimized. To suggest there is a single best approach is total hubris. The living, breathing organizations we call markets will continue to evolve and react to the steps and missteps of their participants. Investors must embrace this uncertainty and incorporate dynamism into their approach to succeed.

There are four main sections in this book. The first is designed to explain modern portfolio theory in a way that is accessible to most readers. Chapter 2 focuses on the key question of setting objectives. If you do not know where you are going, how do you expect to get there? Chapter 3 digs into the key concepts and may be a somewhat challenging discussion for those who are bit removed from their time in the classroom; the principles are important all the same. So many fund managers and brokers use and misuse these concepts in their sales pitches that the only way any investor evens the playing field is to have at least a basic knowledge of this material.

Chapter 4 returns to shallower analytical waters, where we shall stay. Here the building blocks of portfolio construction are described and the process of asset allocation developed.

The second section is devoted to the steps of building portfolios. The many practical challenges to executing any asset allocation are presented in Chapter 5. Chapter 6 sorts out suggestions regularly presented to investors to earn an enhanced return. Spoiler alert: most are not worth trying.

It has been more than a decade since the financial crisis, but the memories of those events still shape our investment thinking. Chapters 7 and 8 dig deeper into the topics of tail risk, bubbles, and crashes. The third section consists of three chapters (Chapter 9–11) that can be viewed as reference material to be drawn on as needed. The goal is to give you informative descriptions of the vast array of investment vehicles regularly included in modern portfolios. Traditional securities, derivatives, and a wide array of structures and packages are explained and compared.

The final section covers four chapters (Chapter 12–15) that are both practical and philosophical in nature. What is the nature of decision-making around investments? How are investments regulated, and how does that affect your decisions? How is the investment world likely to evolve, and what does that mean for us? And finally, what are the major lessons we can take from throughout this book? It was not easy to keep the list to 10, but if the reader wants a handy reminder of things to keep top of mind, Chapter 15 is the one to bookmark.

As we create this foundation, we will regularly challenge many parts of accepted wisdom. An aggressive marketer might assert that their book is all any investor needs. This is never true. Most people will not build their portfolios without help. This book will prepare you to ask the right questions of investment managers, brokers, or advisors and then to appreciate their answers. When finished, you should have a framework to grow capital through time, avoid major pitfalls, and be able to adapt to worlds not yet seen or even imagined.