Why Stock Markets Crash: Critical Events in Complex Financial Systems

Description

The scientific study of complex systems has transformed a wide range of disciplines in recent years, enabling researchers in both the natural and social sciences to model and predict phenomena as diverse as earthquakes, global warming, demographic patterns, financial crises, and the failure of materials. In this book, Didier Sornette boldly applies his varied experience in these areas to propose a simple, powerful, and general theory of how, why, and when stock markets crash. Most attempts to explain market failures seek to pinpoint triggering mechanisms that occur hours, days, or weeks before the collapse. Sornette proposes a radically different view: the underlying cause can be sought months and even years before the abrupt, catastrophic event in the build-up of cooperative speculation, which often translates into an accelerating rise of the market price, otherwise known as a "bubble." Anchoring his sophisticated, step-by-step analysis in leading-edge physical and statistical modeling techniques, he unearths remarkable insights and some predictions--among them, that the "end of the growth era" will occur around 2050. Sornette probes major historical precedents, from the decades-long "tulip mania" in the Netherlands that wilted suddenly in 1637 to the South Sea Bubble that ended with the first huge market crash in England in 1720, to the Great Crash of October 1929 and Black Monday in 1987, to cite just a few. He concludes that most explanations other than cooperative self-organization fail to account for the subtle bubbles by which the markets lay the groundwork for catastrophe. Any investor or investment professional who seeks a genuine understanding of looming financial disasters should read this book. Physicists, geologists, biologists, economists, and others will welcome Why Stock Markets Crash as a highly original "scientific tale," as Sornette aptly puts it, of the exciting and sometimes fearsome--but no longer quite so unfathomable--world of stock markets. Contents Preface to the Princeton Science Library Edition Preface to the 2002 Edition Chapter 1 FINANCIAL CRASHES: WHAT, HOW, WHY, AND WHEN? What Are Crashes, and Why Do We Care? The Crash of October 1987 Historical Crashes The Tulip Mania The South Sea Bubble The Great Crash of October 1929 Extreme Events in Complex Systems Is Prediction Possible? A Working Hypothesis Chapter 2 FUNDAMENTALS OF FINANCIAL MARKETS The Basics Price Trajectories Return Trajectories Return Distributions and Return Correlation The Efficient Market Hypothesis and the Random Walk The Random Walk A Parable: How Information Is Incorporated in Prices, Thus Destroying Potential “Free Lunches” Prices Are Unpredictable, or Are They? Risk–Return Trade-Off Chapter 3 FINANCIAL CRASHES ARE “OUTLIERS” What Are “Abnormal” Returns? Drawdowns (Runs) Definition of Drawdowns Drawdowns and the Detection of “Outliers” Expected Distribution of “Normal” Drawdowns Drawdown Distributions of Stock Market Indices The Dow Jones Industrial Average The Nasdaq Composite Index Further Tests The Presence of Outliers Is a General Phenomenon Main Stock Market Indices, Currencies, and Gold Largest U.S. Companies Synthesis Symmetry-Breaking on Crash and Rally Days Implications for Safety Regulations of Stock Markets Chapter 4 POSITIVE FEEDBACKS Feedbacks and Self-Organization in Economics Hedging Derivatives, Insurance Portfolios, and Rational Panics “Herd” Behavior and “Crowd” Effect Behavioral Economics Herding Empirical Evidence of Financial Analysts’ Herding Forces of Imitation It Is Optimal to Imitate When Lacking Information Mimetic Contagion and the Urn Models Imitation from Evolutionary Psychology Rumors The Survival of the Fittest Idea Gambling Spirits “Anti-Imitation” and Self-Organization Why It May Pay to Be in the Minority El-Farol’s Bar Problem Minority Games Imitation versus Contrarian Behavior Cooperative Behaviors Resulting from Imitation The Ising Model of Cooperative Behavior Complex Evolutionary Adaptive Systems of Boundedly Rational Agents Chapter 5 MODELING FINANCIAL BUBBLES AND MARKET CRASHES What Is a Model? Strategy for Model Construction in Finance Basic Principles The Principle of Absence of Arbitrage Opportunity Existence of Rational Agents “Rational Bubbles” and Goldstone Modes of the Price “Parity Symmetry” Breaking Price Parity Symmetry Speculation as Spontaneous Symmetry Breaking Basic Ingredients of the Two Models The Risk-Driven Model Summary of the Main Properties of the Model The Crash Hazard Rate Drives the Market Price Imitation and Herding Drive the Crash Hazard Rate The Price-Driven Model Imitation and Herding Drive the Market Price The Price Return Drives the Crash Hazard Rate Risk-Driven versus Price-Driven Models Chapter 6 HIERARCHIES, COMPLEX FRACTAL DIMENSIONS, AND LOG-PERIODICITY Critical Phenomena by Imitation on Hierarchical Networks The Underlying Hierarchical Structure of Social Networks Critical Behavior in Hierarchical Networks A Hierarchical Model of Financial Bubbles Origin of Log-Periodicity in Hierarchical Systems Discrete Scale Invariance Fractal Dimensions Organization Scale by Scale: The Renormalization Group Principle and Illustration of the Renormalization Group The Fractal Weierstrass Function: A Singular Time-Dependent Solution of the Renormalization Group Complex Fractal Dimensions and Log-Periodicity Importance and Usefulness of Discrete Scale Invariance Existence of Relevant LengthScales Prediction Scenarios Leading to Discrete Scale Invariance and Log-Periodicity Newcomb–Benford Law of First Digits and the Arithmetic System The Log-Periodic Law of the Evolution of Life? Nonlinear Trend-Following versus Nonlinear Fundamental Analysis Dynamics Trend Following: Positive Nonlinear Feedback and Finite-Time Singularity Reversal to the Fundamental Value: Negative Nonlinear Feedback Some Characteristics of the Price Dynamics of the Nonlinear Dynamical Model Chapter 7 AUTOPSY OF MAJOR CRASHES: UNIVERSAL EXPONENTS AND LOG-PERIODICITY The Crash of October 1987 Precursory Pattern Aftershock Patterns The Crash of October 1929 The Three Hong Kong Crashes of 1987, 1994, and 1997 The Hong Kong Crashes The Crash of October 1997 and Its Resonance on the U.S. Market Currency Crashes The Crash of August 1998 Nonparametric Test of Log-Periodicity The Slow Crash of 1962 Ending the “Tronics” Boom The Nasdaq Crash of April 2000 “Antibubbles” The “Bearish” Regime on the Nikkei Starting from January 1, 1990 The Gold Deflation Price Starting in Mid-1980 Synthesis: “Emergent” Behavior of the Stock Market Chapter 8 BUBBLES, CRISES, AND CRASHES IN EMERGENT MARKETS Speculative Bubbles in Emerging Markets Methodology Latin-American Markets Asian Markets The Russian Stock Market Correlations across Markets: Economic Contagion and Synchronization of Bubble Collapse Implications for Mitigations of Crises Chapter 9 PREDICTION OF BUBBLES, CRASHES, AND ANTIBUBBLES The Nature of Predictions How to Develop and Interpret Statistical Tests of Log-Periodicity First Guidelines for Prediction What Is the Predictive Power of Equation (15)? How Long Prior to a Crash Can One Identify the Log-Periodic Signatures? A Hierarchy of Prediction Schemes The Simple Power Law The “Linear” Log-Periodic Formula The “Nonlinear” Log-Periodic Formula The Shank’s Transformation on a Hierarchy of Characteristic Times Application to the October 1929 Crash Application to the October 1987 Crash Forward Predictions Successful Prediction of the Nikkei 1999 Antibubble Successful Prediction of the Nasdaq Crash of April 2000 The U.S. Market, December 1997 False Alarm The U.S. Market, October 1999 False Alarm Present Status of Forward Predictions The Finite Probability That No Crash Will Occur during a Bubble Estimation of the Statistical Significance of the Forward Predictions Statistical Confidence of the Crash“Roulette” Statistical Significance of a Single Successful Prediction via Bayes’s Theorem The Error Diagram and the Decision Process Practical Implications on Different Trading Strategies Chapter 10 2050: THE END OF THE GROWTH ERA? Stock Markets, Economics, and Population The Pessimistic Viewpoint of “Natural” Scientists The Optimistic Viewpoint of “Social” Scientists Analysis of the Faster-Than-Exponential Growthof Population, GDP, and Financial Indices Refinements of the Analysis Complex Power Law Singularities Prediction for the Coming Decade The Aging “Baby Boomers” Related Works and Evidence Scenarios for the “Singularity” Collapse Transition to Sustainability Resuming Accelerating Growth by Overpassing Fundamental Barriers The Increasing Propensity to Emulate the Stock Market Approach References Index