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.
Author(s): Didier Sornette
Publisher: Princeton University Press
Year: 2017
Language: English
Pages: 448
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
