Preface

A successful fund manager and former academic once told me that business school research is focused too much on topics that get professors published and promoted and too little on explaining how markets actually work. This was not an entirely facetious remark. He was referring to what he perceived to be a fundamental disconnect between the objectives of mainstream business school curricula and those of investment professionals. As both an academic and an investment professional, I not only echo that sentiment but I also see the gap widening. This book attempts to bridge that gap.

My experience over the past thirty years confirms a continuing trend in core business school curriculums away from rigorous analytics. It should come as no surprise, therefore, to see practitioners discount the value of strong analytic skills in their decision-making processes. Indeed, my conversations with practitioners reveal a heavy bias to their instincts as investment managers and a tone that challenges me to prove to them that there is somehow a cost associated with not understanding the details of various concepts such as mean variance optimization, the decomposition of risk, derivatives, and so forth. They simply point to their portfolio outperformance relative to their benchmarks as evidence suggesting that these skills are at best superfluous. I might note here as well that most of the professionals around today learned to manage assets during the long-running bull market that began in the early 1980s—a period of time when it was arguably difficult not to have made money. I don't think it was a coincidence that business schools began to deemphasize scientific rigor at about the same time.

The past 30 years have also coincided with a second “industrial revolution” made possible in part by unprecedented gains in computer processing power. Improvements in information technology followed which revolutionized both the quantity and quality of information available which, in turn, gave rise to an array of new securities and trading innovations, strategies, tactics, and portfolio design and ushered in a new era of market globalization. It is interesting that the skills that complemented the new technology (mathematics, statistics, and programming for instance) were being deemphasized at about the time they should have been leveraged upon. Anyone with a Bloomberg terminal and a spreadsheet was thought to have the requisite skill set to handle any empirical questions that may have arisen while those individuals who actually understood the math were thought to possess purely gratuitous skill sets. This mindset should have changed as the credit crisis unfolded but, ironically, a large part of the burden of blame for the crisis was heaped on the technology itself and not the fact that the users of that technology were grossly underprepared to wield it responsibly.

The simple facts are that technology and the credit crisis together have accelerated the pace of globalization. In turn, the global economic environment will continue to challenge the status quo while markets will become even more competitive, more volatile, and inherently more risky. These developments would seem to suggest that both business school curricula and investment managers embrace opportunities to attain skills that will make them more effective competitors in this global environment.

Investment managers are sometimes fond of saying that they rely on their “gut” instincts, that they understand implicitly the tenets of modern portfolio theory and that investment management is more an art than a science, begging the question of why we bother with the science—a logic that would apply equally well, I suppose, to children petitioning to skip their music lessons. The truth is, however, other things constant; we'd all prefer to be better at the science. The problem is that the level of rigor is often far beyond our abilities either because we had little formal training initially or that the outcome of that training has long since lapsed into disuse. The objective of this book is to help the reader fill the gaps in that knowledge in a step-wise fashion that emphasizes the underlying theoretical principles in an environment rich with direct applications using market data.

My approach is to write in a conversational tone, introducing a problem in financial economics and then following by developing the intuition behind the theory. I try to get the reader to think of the problem in a way that compels the construction of the model used to solve it in a way that makes the solution method seem natural. I try not to skip any steps in the mathematical derivations and provide chapter appendices when I think further discussion may distract the reader from the point at hand. All through the book, emphasis is on practical applications. To that end, most chapters have a companion set of spreadsheets that contain the data and applications. These too are completely solved, step by step. All data used in the text appear in the chapter spreadsheets. All tables and figures included in the text also appear in the chapter spreadsheets. The reader can therefore replicate all the results that appear in the text. Since the spreadsheets are all linked to the data I provide, then swapping new data will update the existing links including all tables and graphs.

I have always felt that most students understand complex subjects more clearly through applications. Using real data enhances the relevance of an application. Providing the ability to update data on open source spreadsheets enables the student to continue to learn in a hands-on fashion long after closing this book. My experience with this format has been very well received in the classroom and typically students find ways to integrate what they learn in these applications into their everyday work on the job.

The coverage in this book is somewhat broader than what is found in standard texts. I include, for instance, an early chapter devoted to equity pricing models and how pricing is approached by practitioners along with some caveats associated with these models. I also develop optimization and statistical concepts more rigorously than what is usually found in investments textbooks and I spend a lot more time on portfolio optimization and construction and risk management. There are chapters on topics that I think practitioners are increasingly interested in such as anomalies, active management, Monte Carlo techniques, factor models, systemic risk, hedging, private equity, and structured finance (collateralized debt obligations). Derivatives have also become more important to portfolio managers. I acknowledge that there are some excellent texts that cover derivative securities and therefore devote only three chapters to futures, forwards, swaps, and options and refer the reader to sources cited in the end of chapter references for deeper study. These three chapters are followed by a chapter devoted to hedging portfolio risk using derivatives and finally by the chapter on structured credit. The options treatment benefits greatly from the spreadsheets and these provide another valuable tool that students can take away for future use.

This book is targeted to graduate students in finance and economics, CFAs, and experienced portfolio managers. Although I try to derive my results from first principles, the material in this book is not a substitute for courses in calculus and statistics. As such, students with some formal training in these two areas will benefit greatly. Upper level undergraduates will also find many chapters and applications easily accessible, especially Chapters 1–8, 11, 15, and 19. The usual disclaimer applies as I take full responsibility for all errors and omissions.

Steve Peterson
Richmond, Virginia