Stalking the Black Swan

Stalking the Black Swan: Research and Decision Making in a World of Extreme Volatility

Kenneth A. Posner
Copyright Date: 2010
Pages: 288
https://www.jstor.org/stable/10.7312/posn15048
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  • Book Info
    Stalking the Black Swan
    Book Description:

    Kenneth A. Posner spent close to two decades as a Wall Street analyst, tracking the so-called "specialty finance" sector, which included controversial companies such as Countrywide, Fannie Mae, Freddie Mac, CIT, and MasterCard-many of which were caught in the subprime mortgage and capital markets crisis of 2007. While extreme volatility is nothing new in finance, the recent downturn caught many off guard, indicating that the traditional approach to decision making had let them down. Introducing a new framework for handling and evaluating extreme risk, Posner draws on years of experience to show how decision makers can best cope with the "Black Swans" of our time.

    Posner's shrewd assessment combines the classic fundamental research approach of Benjamin Graham and David Dodd with more recent developments in cognitive science, computational theory, and quantitative finance. He outlines a probabilistic approach to decision making that involves forecasting across a range of scenarios, and he explains how to balance confidence, react accurately to fast-breaking information, overcome information overload, zero in on the critical issues, penetrate the information asymmetry shielding corporate executives, and integrate the power of human intuition with sophisticated analytics. Emphasizing the computational resources we already have at our disposal-our computers and our minds-Posner offers a new track to decision making for analysts, investors, traders, corporate executives, risk managers, regulators, policymakers, journalists, and anyone who faces a world of extreme volatility.

    eISBN: 978-0-231-52167-3
    Subjects: Business, Finance

Table of Contents

  1. Front Matter
    (pp. i-iv)
  2. Table of Contents
    (pp. v-vi)
  3. ACKNOWLEDGMENTS
    (pp. vii-viii)
  4. INTRODUCTION
    (pp. ix-xvi)

    The premise of this book is that the practice of fundamental research can help decision makers adapt to a world of “Black Swans,” the seemingly improbable but highly consequential surprises that turn our familiar ways of thinking upside down. Most commonly associated with the work of Benjamin Graham and David Dodd, fundamental research is the study of causal variables underlying the performance of companies, industries, or economies, with the goal of predicting future developments. The research strategies in this book build upon that heritage, but they have been updated for the growing importance of computer technology, and they have a...

  5. PART I: Uncertainty
    • Chapter 1 Forecasting in Extreme Environments
      (pp. 3-31)

      Black swans are but one manifestation of the uncertainty with which we peer into the future, mindful that other people are trying to do the same thing and that the interaction of opinions and decisions can affect the very future outcomes we are trying to foresee. Investors have strong incentives to make accurate forecasts in the face of this kind of uncertainty. So do decision makers in most any field characterized by competition, cooperation, or other forms of collective action.

      This chapter describes some of the main sources of extreme volatility and how to make better forecasts in challenging times....

    • Chapter 2 Thinking in Probabilities
      (pp. 32-54)

      People naturally think in probabilities. For high-stakes decisions, getting the odds right is a critical skill—especially when Black Swans are involved. The challenge for decision makers is that Black Swans are future possibilities, not present certainties. They lurk in some future scenarios but not others. Some swans cannot be anticipated, whereas others are widely discussed and debated for years in advance.¹ For example, the media started warning about a housing bubble almost immediately after the stock market crash in 2001. Back then, the odds were remote that housing would collapse, too. By 2006, the probabilities were rising. By 2007,...

    • Chapter 3 The Balance Between Overconfidence and Underconfidence, and the Special Risk of Complex Modeling
      (pp. 55-84)

      Sometimes we reach accurate decisions in an intuitive flash; sometimes our instincts are fatally flawed. Some answers emerge from intensive research and sophisticated analysis; sometimes this approach fails, too. In recent decades social scientists have focused on overconfidence as a cause of many mistakes, blaming the phenomenon for excessive stock trading and market volatility, bad investment decisions by chief executive officers, costly delays in labor negotiations, litigation, even wars.¹ In the investment markets, it is presumed that investors who lost money when Black Swans struck must have been overconfident. To avoid such risks, investors might try to be less confident,...

  6. PART II: Information
    • Chapter 4 Fighting Information Overload with Strategy
      (pp. 87-109)

      One reason we are surprised by episodes of extreme volatility is that the world contains more information than any person, team, or organization can process. Information overload is a fundamental reality of the modern world. This is evident in the markets, where processing huge volumes of data is a full-time job for legions of analysts, traders, and portfolio managers. Managing information is also a crucial skill for intelligence analysts, market researchers, corporate strategists, policymakers, and decision makers in many other fields. True, ever more powerful computers assist us in organizing and analyzing information. Unfortunately, any computational device, human or silicon,...

    • Chapter 5 Making Decisions in Real Time: How to React to New Information Without Falling Victim to Cognitive Dissonance
      (pp. 110-136)

      The textbook approach presumes decision makers have all the time in the world to assemble and weigh information, but in the real world, things happen on the fly. From 2000 through late 2005, Fannie Mae’s stock fell by about 30%, a result of political pressure, slowing growth, and accounting problems. At least there was time to analyze the issues. In October 2007, Fannie’s stock fell 30% in a single day. As discussed in the previous chapter, the speed of computation is limited by physical and mathematical constraints. Extreme volatility—when great change is compressed into short time periods—does not...

    • Chapter 6 Mitigating Information Asymmetry
      (pp. 137-154)

      Who ends up on the wrong side of a Black Swan? It may be those analysts who do not react properly to new information but instead dig in their heels and stick with their prior views, as discussed in the last chapter. Or it may be an investor who focuses on the wrong variables, failing to allocate resources to the critical issues (Chapter 4). But even decision makers who sidestep these traps may fail because they do not get their hands on the relevant information. The Black Swan’s victims include those who were simply uninformed.

      Investors who tire of relying...

  7. PART III: Analysis and Judgment
    • Chapter 7 Mapping from Simple Ideas to Complex Analysis
      (pp. 157-177)

      In an increasingly complex world, some kind of modeling is a necessary part of decision making in almost any field. Sophisticated or simple, models can dramatically extend our forecasting capability. But modeling also introduces a new source of complexity into the decision-making process—namely, questions about the model’s accuracy and reliability. In the worst case, modeling mistakes may leave decision makers vulnerable to Black Swan surprises by blinding them to the true range of possible outcomes, as we saw with subprime loss forecasting (Chapter 3).

      After all, it is only with hindsight that Black Swans seem simple. And it is...

    • Chapter 8 The Power and Pitfalls of Monte Carlo Modeling
      (pp. 178-205)

      In a world of information and complexity, high-powered computer models have become essential tools for many decision makers. Among the most versatile of these, the Monte Carlo model generates probability trees with thousands or millions of branches—however many it takes to resolve problems too complex or uncertain to be worked out by hand. The model’s accuracy sometimes seems eerie. A mainstay of science, engineering, and quantitative finance, the model finds uses in other areas, even locating sunken treasure.

      However, if not used properly, Monte Carlo models can be hazardous to your health. Their results are unreliable if you do...

    • Chapter 9 Judgment
      (pp. 206-236)

      This book has argued that fundamental research and analysis can improve decision making in volatile environments. But the analytic tools we have discussed—intuition, mental simulation, computer modeling—all share the limitation of any algorithmic routine: they cannot be counted on to solve every problem. For this reason, successful decision making requires something extra, which we will call judgment.

      The textbook approach to judgment advises weighing the pros and cons. Though weighing is a necessary step, the advice nonetheless misses the bigger picture: judgment is a kind of recursive process, in which we turn our analytic tools on the very...

  8. NOTES
    (pp. 237-256)
  9. INDEX
    (pp. 257-268)