Chaos Theory in the Financial Markets

by Trippi, Robert

• ISBN13: 9781557385550
• ISBN10: 1557385556
• Edition: 1st
• Format: Hardcover
• Copyright: 1994-03-22
• Publisher: McGraw-Hill Education

Summary

Chaos theory is a revolutionary approach to understanding and forecasting the behavior of complex systems. The theory, which utilizes nonlinear mathematics to identify the underlying rules of evolving systems, provides extraordinary insights into the dynamics of the financial markets. In so doing, Dr. Chorafas explores a variety of new approaches that provide an entirely new perspective on financial market analysis and forecasting. Topics include: the concepts and mathematics of chaos theory; using nonlinear equations and fractals to forecast the currency market; genetic algorithms and neural networks.

Table of Contents

List of Figures and Tables	p. xiii
Foreword	p. xvii
Foreword	p. xxi
Preface	p. xxiii
Acknowledgments	p. xxvii
Introduction to Complexity Theory	p. 1
Implementing Chaos Theory in Financial Markets	p. 3
Introduction	p. 3
Change, Order, and Non-Traditional Research	p. 5
Time and the Concept of a Chaotic Market Behavior	p. 9
Looking at the Origins of Chaos Theory	p. 11
Concepts of a Chaotic Market Behavior	p. 13
The Able Treatment of Time	p. 17
Time Series, Nonlinearities, and Bifurcations	p. 21
Why Are We Interested in Chaos Theory?	p. 25
Efficient Market Hypothesis and Strange Attractors	p. 28
Organization, Evolution, and the Edge of Chaos	p. 33
Introduction	p. 33
Understanding the Evolution of Systems	p. 34
Learning Effects and the Edge of Chaos	p. 37
Adaptive Agents and Critical Conditions	p. 40
Edge of Chaos and Solution Space	p. 43
A Grammar for Problems of Complexity	p. 45
Is Equilibrium a Prerequisite to Organization?	p. 49
Clues to the Origin of Dynamic Systems	p. 52
Principles of Evolution and Risk Management	p. 55
Fundamental Notions Underlying the Theory of Complexity and Its Mathematics	p. 59
Introduction	p. 59
Macroscopic and Microscopic Concepts	p. 60
Exploring the Macroscopic Viewpoint	p. 64
Is Complexity Theory a Matter of Fashion?	p. 68
The Need to Restructure Our Know-how	p. 71
Learning from the Behavior of Other Systems	p. 73
Entropy and Organization	p. 77
Randomness, Probable States, and Prediction	p. 81
Weeding Noise Out of Financial Data	p. 86
NonLinear Equations and Fractals Underpinning Chaos Theory	p. 89
Introduction	p. 89
Linear and Nonlinear Models in Forex Operations	p. 91
Escaping the Linear Approaches	p. 95
Developing Equations for Nonlinear Systems	p. 98
Implementing Concepts from Physics in Financial Analysis	p. 100
The Need to Rethink Time Series and Solution Spaces	p. 104
Dynamic Equations, Forex Trading, and Fractal Concepts	p. 108
An Introduction to the Theory of Fractals	p. 112
Concepts and Processes in Fractal Geometry	p. 118
From Genetic Algorithms to Fuzzy Engineering	p. 125
The Essence of Genetic Algorithms and their Implementation	p. 127
Introduction	p. 127
What Is the Sense of Using a Genetic Algorithm?	p. 128
The Mechanics of Genetic Algorithms	p. 133
Selection, Mutation, and Performance in the Stock Market	p. 136
The Process of Generation in Foreign Exchange Operations	p. 140
Applying the Genetic Algorithm in Off-Balance Sheet Operations	p. 143
Adaptive Agents and Research in the Capital Markets	p. 146
Increased Returns and Positive Feedback	p. 149
Biological Research and Genetic Algorithms	p. 152
Predictors, Simulators, and Artificial Life at Santa Fe Institute	p. 157
Introduction	p. 157
The Concept of Reasoning by Analogy	p. 158
Modeling Community Intelligence	p. 161
Can We Reflect a Pattern of Group Thinking?	p. 165
Using Supercomputer Power to Face Processing Requirements	p. 167
Swarms and Systems with Feedback	p. 171
Competitive Advantages of an Ecological Approach	p. 174
Problems and Opportunities in Developing Predictors	p. 178
The Financial Industry's Achilles Heel	p. 180
Non-Traditional Financial Analysis at MIT	p. 187
Introduction	p. 187
MIT Researchers Turn Away from Modern Portfolio Theory	p. 188
Volatility and the Asynchronous Nature of Financial Data	p. 191
Trying to Visualize Multiple Variable Data	p. 194
Autocorrelation, Chaos, and Volatility	p. 197
The Concept of Risk and Cumulative Exposure	p. 200
Why Logistics Equations Need Memory Systems	p. 204
Capitalizing on Computer Storage and Agile Algorithms	p. 208
Bankers Trust Positions Itself for Greater Competitiveness in the Market	p. 213
Using Fuzzy Engineering in Financial Environments	p. 217
Introduction	p. 217
Implementing Concepts in Fuzzy Logic	p. 220
Financial Analysis through Fuzzy Sets	p. 223
Advantages from the Implementation of Fuzzy Engineering	p. 227
The Cyclical Nature of Financial Business	p. 229
Benefits from a Fuzzy Cognitive Model for Financial Operations	p. 233
Paying Attention to the Inference Method	p. 236
Integrating Fuzzy Engineering and Neural Networks	p. 240
Fuzzy Functions, Genetic Algorithms, and Fractals	p. 243
Implementing Advanced Financial Analysis	p. 247
Dealing with Uncertainty in the Financial Markets	p. 249
Introduction	p. 249
Initial Conditions and Possibility Theory	p. 251
The Meaning of Uncertainty in Financial Data	p. 254
Can We Learn from Other Implementation Domains?	p. 258
Improving the Scope of Analysis through Fuzzy Sets	p. 261
Establishing the Customer's Profile for Relationship Management	p. 264
Using a Fuzzy Sets Graph to Judge Customer Behavior	p. 268
Automating Sensitive Aspects of Banking Work	p. 272
Developing the Client Mirror and Doing Sensitivity Analysis	p. 276
Case Studies on How to Apply Fuzzy Engineering	p. 281
Introduction	p. 281
A Grading Procedure Involving Uncertainty and the Defuzzification Concept	p. 283
Capitalizing on the Power of Defuzzification	p. 288
Quantification, Qualification, and Fuzzification in Trading	p. 290
The Evaluation of Collateral for Equities	p. 293
More Accurate Ways for Pricing Collaterals	p. 296
Visual Programming and Practical Results	p. 299
A Fuzzy System for Bond Evaluation	p. 303
Ways and Means of Estimating Cash Flow	p. 308
Using the Monte Carlo Method in Financial Analysis	p. 315
Introduction	p. 315
Problems Connected to the Construction of Stochastic Models	p. 317
Concepts and Challenges in Implementing Monte Carlo	p. 320
Using Monte Carlo in a Financial Environment	p. 324
Understanding the Business of Securitization	p. 328
Making Home Mortgages a Marketable Product	p. 333
Exploring the Business Opportunities That Are Present	p. 336
Developing Valid Models for Securities Pricing	p. 339
Can We Reach the Goal of Managing Complexity?	p. 343
Introduction	p. 343
Complexity, Adaptability, and Behavioral Patterns	p. 345
The Process of Learning at the Edge of Chaos	p. 350
Is Chaotic Behavior a Prerequisite to Renewal?	p. 353
Pattern Formation in an Environment of Complex Behavior	p. 356
New Strategies in Financial Trading and in Personalized Investment Services	p. 360
Streamlined or Overlapping Research Interests?	p. 363
Studies in Finance that Enhance Competitive Advantages	p. 366
Organizational Prerequisites in Managing Complexity	p. 369
Index	p. 373
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