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.

Preface to the Princeton Science Library Edition xiii
Preface to the 2002 Edition xix
1 Financial crashes: what, how, why, and when? 3
What Are Crashes, and Why Do We Care? 3
The Crash of October 1987 5
Historical Crashes 7
The Tulip Mania 7
The South Sea Bubble 9
The Great Crash of October 1929 12
Extreme Events in Complex Systems 15
Is Prediction Possible? A Working Hypothesis 20
2 Fundamentals of financial markets 26
The Basics 27
Price Trajectories 27
Return Trajectories 30
Return Distributions and Return Correlation 33
The Efficient Market Hypothesis and the Random Walk 38
The Random Walk 38
A Parable: How Information Is Incorporated in Prices, Thus Destroying Potential "Free Lunches" 42
Prices Are Unpredictable, or Are They? 45
Risk-Return Trade-Off 47
3 Financial crashes are "outliers" 49
What Are "Abnormal" Returns? 49
Drawdowns (Runs) 51
Definition of Drawdowns 51
Drawdowns and the Detection of "Outliers" 54
Expected Distribution of "Normal" Drawdowns 56
Drawdown Distributions of Stock Market Indices 60
The Dow Jones Industrial Average 60
The Nasdaq Composite Index 62
Further Tests 65
The Presence of Outliers Is a General Phenomenon 69
Main Stock Market Indices, Currencies, and Gold 70
Largest U.S. Companies 73
Synthesis 75
Symmetry-Breaking on Crash and Rally Days 76
Implications for Safety Regulations of Stock Markets 77
4 Positive feedbacks 81
Feedbacks and Self-Organization in Economics 82
Hedging Derivatives, Insurance Portfolios, and Rational Panics 89
"Herd" Behavior and "Crowd" Effect 91
Behavioral Economics 91
Herding 94
Empirical Evidence of Financial Analysts' Herding 96
Forces of Imitation 99
It Is Optimal to Imitate When Lacking Information 99
Mimetic Contagion and the Urn Models 104
Imitation from Evolutionary Psychology 106
Rumors 108
The Survival of the Fittest Idea 111
Gambling Spirits 112
"Anti-Imitation" and Self-Organization 114
Why It May Pay to Be in the Minority 114
El-Farol's Bar Problem 115
Minority Games 117
Imitation versus Contrarian Behavior 118
Cooperative Behaviors Resulting from Imitation 121
The Ising Model of Cooperative Behavior 122
Complex Evolutionary Adaptive Systems of Boundedly Rational Agents 130
5 Modeling financial bubbles and market crashes 134
What Is a Model? 134
Strategy for Model Construction in Finance 135
Basic Principles 135
The Principle of Absence of Arbitrage Opportunity 136
Existence of Rational Agents 137
"Rational Bubbles" and Goldstone Modes of the Price "Parity Symmetry" Breaking 139
Price Parity Symmetry 140
Speculation as Spontaneous Symmetry Breaking 144
Basic Ingredients of the Two Models 148
The Risk-Driven Model 150
Summary of the Main Properties of the Model 150
The Crash Hazard Rate Drives the Market Price 152
Imitation and Herding Drive the Crash Hazard Rate 155
The Price-Driven Model 162
Imitation and Herding Drive the Market Price 162
The Price Return Drives the Crash Hazard Rate 164
Risk-Driven versus Price-Driven Models 168
6 Hierarchies, complex fractal dimensions, and log-periodicity 171
Critical Phenomena by Imitation on Hierarchical Networks 173
The Underlying Hierarchical Structure of Social Networks 173
Critical Behavior in Hierarchical Networks 177
A Hierarchical Model of Financial Bubbles 181
Origin of Log-Periodicity in Hierarchical Systems 186
Discrete Scale Invariance 186
Fractal Dimensions 188
Organization Scale by Scale: The Renormalization Group 192
Principle and Illustration of the Renormalization Group 192
The Fractal Weierstrass Function: A Singular Time-Dependent Solution of the Renormalization Group 195
Complex Fractal Dimensions and Log-Periodicity 198
Importance and Usefulness of Discrete Scale Invariance 208
Existence of Relevant LengthScales 208
Prediction 209
Scenarios Leading to Discrete Scale Invariance and Log-Periodicity 210
Newcomb-Benford Law of First Digits and the Arithmetic System 211
The Log-Periodic Law of the Evolution of Life? 213
Nonlinear Trend-Following versus Nonlinear Fundamental Analysis Dynamics 217
Trend Following: Positive Nonlinear Feedback and Finite-Time Singularity 218
Reversal to the Fundamental Value: Negative Nonlinear Feedback 220
Some Characteristics of the Price Dynamics of the Nonlinear Dynamical Model 223
7 Autopsy of major crashes: universal exponents and logperiodicity 228
The Crash of October 1987 228
Precursory Pattern 231
Aftershock Patterns 236
The Crash of October 1929 239
The Three Hong Kong Crashes of 1987, 1994, and 1997 242
The Hong Kong Crashes 242
The Crash of October 1997 and Its Resonance on the U.S. Market 246
Currency Crashes 254
The Crash of August 1998 259
Nonparametric Test of Log-Periodicity 263
The Slow Crash of 1962 Ending the "Tronics" Boom 266
The Nasdaq Crash of April 2000 269
"Antibubbles" 275
The "Bearish" Regime on the Nikkei Starting from January 1, 1990 276
The Gold Deflation Price Starting in Mid-1980 278
Synthesis: "Emergent" Behavior of the Stock Market 279
8 Bubbles, crises, and crashes in emergent markets 281
Speculative Bubbles in Emerging Markets 281
Methodology 285
Latin-American Markets 286
Asian Markets 295
The Russian Stock Market 304
Correlations across Markets: Economic Contagion and Synchronization of Bubble Collapse 309
Implications for Mitigations of Crises 314
9 Prediction of bubbles, crashes, and antibubbles 320
The Nature of Predictions 320
How to Develop and Interpret Statistical Tests of Log-Periodicity 325
First Guidelines for Prediction 329
What Is the Predictive Power of Equation (15)? 329
How Long Prior to a Crash Can One Identify the Log-Periodic Signatures? 330
A Hierarchy of Prediction Schemes 334
The Simple Power Law 334
The "Linear" Log-Periodic Formula 335
The "Nonlinear" Log-Periodic Formula 336
The Shank's Transformation on a Hierarchy of Characteristic Times 336
Application to the October 1929 Crash 337
Application to the October 1987 Crash 338
Forward Predictions 338
Successful Prediction of the Nikkei 1999 Antibubble 339
Successful Prediction of the Nasdaq Crash of April 2000 342
The U.S. Market, December 1997 False Alarm 342
The U.S. Market, October 1999 False Alarm 346
Present Status of Forward Predictions 346
The Finite Probability That No Crash Will Occur during a Bubble 346
Estimation of the Statistical Significance of the Forward Predictions 347
Statistical Confidence of the Crash"Roulette" 347
Statistical Significance of a Single Successful Prediction via Bayes's Theorem 349
The Error Diagram and the Decision Process 351
Practical Implications on Different Trading Strategies 352
10 2050: The end of the growth era? 355
Stock Markets, Economics, and Population 355
The Pessimistic Viewpoint of "Natural" Scientists 357
The Optimistic Viewpoint of "Social" Scientists 359
Analysis of the Faster-Than-Exponential Growth of Population, GDP, and Financial Indices 361
Refinements of the Analysis 369
Complex Power Law Singularities 369
Prediction for the Coming Decade 371
The Aging "Baby Boomers" 377
Related Works and Evidence 378
Scenarios for the "Singularity" 383
Collapse 384
Transition to Sustainability 389
Resuming Accelerating Growth by Overpassing Fundamental Barriers 393
The Increasing Propensity to Emulate the Stock Market Approach 395
References 397
Index 419