Why Stock Markets Crash: Critical Events in Complex Financial Systems

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