Chaos theory in the financial markets : applying fractals, fuzzy logic, genetic algorithms, swarm simulation & the Monte Carlo method to manage market chaos & volatility / Dimitris N. Chorafas.

Format:

Book

Author/Creator:

Chorafas, Dimitris N.

Language:

English

Subjects (All):

Investments--Mathematics.

Investments.

Chaotic behavior in systems.

Fractals.

Fuzzy logic.

Genetic algorithms.

Monte Carlo method.

Physical Description:

xli, 382 pages : illustrations ; 24 cm

Place of Publication:

Chicago, Ill. : Probus Pub. Co., [1994]

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.

Contents:

Part 1 Introduction to Complexity Theory 1

1 Implementing Chaos Theory in Financial Markets 3

2 Change, Order, and Non-Traditional Research 5

3 Time and the Concept of a Chaotic Market Behavior 9

4 Looking at the Origins of Chaos Theory 11

5 Concepts of a Chaotic Market Behavior 13

6 The Able Treatment of Time 17

7 Time Series, Nonlinearities, and Bifurcations 21

8 Why Are We Interested in Chaos Theory? 25

9 Efficient Market Hypothesis and Strange Attractors 28

2 Organization, Evolution, and the Edge of Chaos 33

2 Understanding the Evolution of Systems 34

3 Learning Effects and the Edge of Chaos 37

4 Adaptive Agents and Critical Conditions 40

5 Edge of Chaos and Solution Space 43

6 A Grammar for Problems of Complexity 45

7 Is Equilibrium a Prerequisite to Organization? 49

8 Clues to the Origin of Dynamic Systems 52

9 Principles of Evolution and Risk Management 55

3 Fundamental Notions Underlying the Theory of Complexity and Its Mathematics 59

2 Macroscopic and Microscopic Concepts 60

3 Exploring the Macroscopic Viewpoint 64

4 Is Complexity Theory a Matter of Fashion? 68

5 The Need to Restructure Our Know-how 71

6 Learning from the Behavior of Other Systems 73

7 Entropy and Organization 77

8 Randomness, Probable States, and Prediction 81

9 Weeding Noise Out of Financial Data 86

4 NonLinear Equations and Fractals Underpinning Chaos Theory 89

2 Linear and Nonlinear Models in Forex Operations 91

3 Escaping the Linear Approaches 95

4 Developing Equations for Nonlinear Systems 98

5 Implementing Concepts from Physics in Financial Analysis 100

6 The Need to Rethink Time Series and Solution Spaces 104

7 Dynamic Equations, Forex Trading, and Fractal Concepts 108

8 An Introduction to the Theory of Fractals 112

9 Concepts and Processes in Fractal Geometry 118

Part 2 From Genetic Algorithms to Fuzzy Engineering 125

5 The Essence of Genetic Algorithms and their Implementation 127

2 What Is the Sense of Using a Genetic Algorithm? 128

3 The Mechanics of Genetic Algorithms 133

4 Selection, Mutation, and Performance in the Stock Market 136

5 The Process of Generation in Foreign Exchange Operations 140

6 Applying the Genetic Algorithm in Off-Balance Sheet Operations 143

7 Adaptive Agents and Research in the Capital Markets 146

8 Increased Returns and Positive Feedback 149

9 Biological Research and Genetic Algorithms 152

6 Predictors, Simulators, and Artificial Life at Santa Fe Institute 157

2 The Concept of Reasoning by Analogy 158

3 Modeling Community Intelligence 161

4 Can We Reflect a Pattern of Group Thinking? 165

5 Using Supercomputer Power to Face Processing Requirements 167

6 Swarms and Systems with Feedback 171

7 Competitive Advantages of an Ecological Approach 174

8 Problems and Opportunities in Developing Predictors 178

9 The Financial Industry's Achilles Heel 180

7 Non-Traditional Financial Analysis at MIT 187

2 MIT Researchers Turn Away from Modern Portfolio Theory 188

3 Volatility and the Asynchronous Nature of Financial Data 191

4 Trying to Visualize Multiple Variable Data 194

5 Autocorrelation, Chaos, and Volatility 197

6 The Concept of Risk and Cumulative Exposure 200

7 Why Logistics Equations Need Memory Systems 204

8 Capitalizing on Computer Storage and Agile Algorithms 208

9 Bankers Trust Positions Itself for Greater Competitiveness in the Market 213

8 Using Fuzzy Engineering in Financial Environments 217

2 Implementing Concepts in Fuzzy Logic 220

3 Financial Analysis through Fuzzy Sets 223

4 Advantages from the Implementation of Fuzzy Engineering 227

5 The Cyclical Nature of Financial Business 229

6 Benefits from a Fuzzy Cognitive Model for Financial Operations 233

7 Paying Attention to the Inference Method 236

8 Integrating Fuzzy Engineering and Neural Networks 240

9 Fuzzy Functions, Genetic Algorithms, and Fractals 243

Part 3 Implementing Advanced Financial Analysis 247

9 Dealing with Uncertainty in the Financial Markets 249

2 Initial Conditions and Possibility Theory 251

3 The Meaning of Uncertainty in Financial Data 254

4 Can We Learn from Other Implementation Domains? 258

5 Improving the Scope of Analysis through Fuzzy Sets 261

6 Establishing the Customer's Profile for Relationship Management 264

7 Using a Fuzzy Sets Graph to Judge Customer Behavior 268

8 Automating Sensitive Aspects of Banking Work 272

9 Developing the Client Mirror and Doing Sensitivity Analysis 276

10 Case Studies on How to Apply Fuzzy Engineering 281

2 A Grading Procedure Involving Uncertainty and the Defuzzification Concept 283

3 Capitalizing on the Power of Defuzzification 288

4 Quantification, Qualification, and Fuzzification in Trading 290

5 The Evaluation of Collateral for Equities 293

6 More Accurate Ways for Pricing Collaterals 296

7 Visual Programming and Practical Results 299

8 A Fuzzy System for Bond Evaluation 303

9 Ways and Means of Estimating Cash Flow 308

11 Using the Monte Carlo Method in Financial Analysis 315

2 Problems Connected to the Construction of Stochastic Models 317

3 Concepts and Challenges in Implementing Monte Carlo 320

4 Using Monte Carlo in a Financial Environment 324

5 Understanding the Business of Securitization 328

6 Making Home Mortgages a Marketable Product 333

7 Exploring the Business Opportunities That Are Present 336

8 Developing Valid Models for Securities Pricing 339

12 Can We Reach the Goal of Managing Complexity? 343

2 Complexity, Adaptability, and Behavioral Patterns 345

3 The Process of Learning at the Edge of Chaos 350

4 Is Chaotic Behavior a Prerequisite to Renewal? 353

5 Pattern Formation in an Environment of Complex Behavior 356

6 New Strategies in Financial Trading and in Personalized Investment Services 360

7 Streamlined or Overlapping Research Interests? 363

8 Studies in Finance that Enhance Competitive Advantages 366

9 Organizational Prerequisites in Managing Complexity 369.

Notes:

Includes index.

ISBN:

1557385556

OCLC:

30522469