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Chaos Theory in the Financial Markets

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ISBN-10: 1557385556

ISBN-13: 9781557385550

Edition: 1994

Authors: Robert L. Trippi, Robert L. Trippi

List price: $95.00
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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.
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Book details

List price: $95.00
Copyright year: 1994
Publisher: McGraw-Hill Education
Publication date: 3/22/1994
Binding: Hardcover
Pages: 400
Size: 6.30" wide x 9.30" long x 1.21" tall
Weight: 1.694
Language: English

List of Figures and Tables
Introduction to Complexity Theory
Implementing Chaos Theory in Financial Markets
Change, Order, and Non-Traditional Research
Time and the Concept of a Chaotic Market Behavior
Looking at the Origins of Chaos Theory
Concepts of a Chaotic Market Behavior
The Able Treatment of Time
Time Series, Nonlinearities, and Bifurcations
Why Are We Interested in Chaos Theory?
Efficient Market Hypothesis and Strange Attractors
Organization, Evolution, and the Edge of Chaos
Understanding the Evolution of Systems
Learning Effects and the Edge of Chaos
Adaptive Agents and Critical Conditions
Edge of Chaos and Solution Space
A Grammar for Problems of Complexity
Is Equilibrium a Prerequisite to Organization?
Clues to the Origin of Dynamic Systems
Principles of Evolution and Risk Management
Fundamental Notions Underlying the Theory of Complexity and Its Mathematics
Macroscopic and Microscopic Concepts
Exploring the Macroscopic Viewpoint
Is Complexity Theory a Matter of Fashion?
The Need to Restructure Our Know-how
Learning from the Behavior of Other Systems
Entropy and Organization
Randomness, Probable States, and Prediction
Weeding Noise Out of Financial Data
NonLinear Equations and Fractals Underpinning Chaos Theory
Linear and Nonlinear Models in Forex Operations
Escaping the Linear Approaches
Developing Equations for Nonlinear Systems
Implementing Concepts from Physics in Financial Analysis
The Need to Rethink Time Series and Solution Spaces
Dynamic Equations, Forex Trading, and Fractal Concepts
An Introduction to the Theory of Fractals
Concepts and Processes in Fractal Geometry
From Genetic Algorithms to Fuzzy Engineering
The Essence of Genetic Algorithms and their Implementation
What Is the Sense of Using a Genetic Algorithm?
The Mechanics of Genetic Algorithms
Selection, Mutation, and Performance in the Stock Market
The Process of Generation in Foreign Exchange Operations
Applying the Genetic Algorithm in Off-Balance Sheet Operations
Adaptive Agents and Research in the Capital Markets
Increased Returns and Positive Feedback
Biological Research and Genetic Algorithms
Predictors, Simulators, and Artificial Life at Santa Fe Institute
The Concept of Reasoning by Analogy
Modeling Community Intelligence
Can We Reflect a Pattern of Group Thinking?
Using Supercomputer Power to Face Processing Requirements
Swarms and Systems with Feedback
Competitive Advantages of an Ecological Approach
Problems and Opportunities in Developing Predictors
The Financial Industry's Achilles Heel
Non-Traditional Financial Analysis at MIT
MIT Researchers Turn Away from Modern Portfolio Theory
Volatility and the Asynchronous Nature of Financial Data
Trying to Visualize Multiple Variable Data
Autocorrelation, Chaos, and Volatility
The Concept of Risk and Cumulative Exposure
Why Logistics Equations Need Memory Systems
Capitalizing on Computer Storage and Agile Algorithms
Bankers Trust Positions Itself for Greater Competitiveness in the Market
Using Fuzzy Engineering in Financial Environments
Implementing Concepts in Fuzzy Logic
Financial Analysis through Fuzzy Sets
Advantages from the Implementation of Fuzzy Engineering
The Cyclical Nature of Financial Business
Benefits from a Fuzzy Cognitive Model for Financial Operations
Paying Attention to the Inference Method
Integrating Fuzzy Engineering and Neural Networks
Fuzzy Functions, Genetic Algorithms, and Fractals
Implementing Advanced Financial Analysis
Dealing with Uncertainty in the Financial Markets
Initial Conditions and Possibility Theory
The Meaning of Uncertainty in Financial Data
Can We Learn from Other Implementation Domains?
Improving the Scope of Analysis through Fuzzy Sets
Establishing the Customer's Profile for Relationship Management
Using a Fuzzy Sets Graph to Judge Customer Behavior
Automating Sensitive Aspects of Banking Work
Developing the Client Mirror and Doing Sensitivity Analysis
Case Studies on How to Apply Fuzzy Engineering
A Grading Procedure Involving Uncertainty and the Defuzzification Concept
Capitalizing on the Power of Defuzzification
Quantification, Qualification, and Fuzzification in Trading
The Evaluation of Collateral for Equities
More Accurate Ways for Pricing Collaterals
Visual Programming and Practical Results
A Fuzzy System for Bond Evaluation
Ways and Means of Estimating Cash Flow
Using the Monte Carlo Method in Financial Analysis
Problems Connected to the Construction of Stochastic Models
Concepts and Challenges in Implementing Monte Carlo
Using Monte Carlo in a Financial Environment
Understanding the Business of Securitization
Making Home Mortgages a Marketable Product
Exploring the Business Opportunities That Are Present
Developing Valid Models for Securities Pricing
Can We Reach the Goal of Managing Complexity?
Complexity, Adaptability, and Behavioral Patterns
The Process of Learning at the Edge of Chaos
Is Chaotic Behavior a Prerequisite to Renewal?
Pattern Formation in an Environment of Complex Behavior
New Strategies in Financial Trading and in Personalized Investment Services
Streamlined or Overlapping Research Interests?
Studies in Finance that Enhance Competitive Advantages
Organizational Prerequisites in Managing Complexity