Artificial Intelligence in Finance and Investing
Theory and Application in Portfolio Management
By: Robert R. Trippi, Jae K. Lee
Hardcover | 19 November 1995 | Edition Number 2
At a Glance
272 Pages
Revised
24.13 x 16.51 x 2.54
Hardcover
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To illustrate the concepts presented in the book, the authors conclude with a valuable practice session and analysis of a typical knowledge-based system for investment management, K-FOLIO. For those who want to stay on the cutting edge of the "application" revolution, Artificial Intelligence in Finance and Investing offers a pragmatic introduction to the use of knowledge-based systems in securities selection and portfolio management.
List of Figures | p. xv |
List of Tables | p. xix |
Preface | p. xxi |
Introduction | p. 1 |
Artificial Intelligence and Investing | p. 1 |
The Organization of This Book | p. 3 |
Nature of the Security Investment Domain | p. 7 |
Characteristics of Investment Assets | p. 8 |
Theories of Stock Price Determination | p. 9 |
Random, Ordered, and Complex Systems | p. 10 |
Value-Based Investing | p. 10 |
The Efficient Market Hypothesis | p. 11 |
Beyond the EMH | p. 12 |
Risk Issues | p. 14 |
What Is Risk? | p. 14 |
Cognitive Error and Stochastic Risk Modeling | p. 14 |
Market Psychology and Noise | p. 15 |
Institutional Trading and Market Behavior | p. 15 |
Agency and Database Commonality Effects | p. 15 |
Trading Dynamics and Instability | p. 16 |
The Exploitation of Anomalies | p. 17 |
The Cost and Value of Information | p. 17 |
Implied Probability Distributions | p. 17 |
Decision Rules and Black Box Investing | p. 19 |
Conclusions | p. 20 |
Endnotes | p. 20 |
References | p. 21 |
Modern Approaches to Portfolio Selection | p. 23 |
Introduction | p. 23 |
Goal Programming | p. 25 |
Mean-Variance Optimization | p. 27 |
The Markowitz Model | p. 27 |
The Efficient Frontier | p. 29 |
Model Enhancement | p. 30 |
Beta and Index Models | p. 31 |
Security Risk and Portfolio Risk | p. 35 |
The Role of Riskless Assets | p. 36 |
Mean Absolute Deviation Optimization | p. 37 |
Markowitz and Capital-Asset Pricing Model Limitations | p. 38 |
CAPM Extensions and Program Trading | p. 40 |
Endnotes | p. 41 |
References | p. 43 |
Artificial Intelligence in Investment Management: An Overview | p. 45 |
Knowledge-Based Systems, Auto-Learning Systems, and Intelligent Systems | p. 45 |
Introduction to Knowledge Representation | p. 46 |
Expert Systems and Financial Services | p. 51 |
An Early ES for Portfolio Selection | p. 52 |
Contemporary Systems | p. 53 |
Emerging Artificial Intelligence Technologies | p. 57 |
Conclusions | p. 61 |
Endnotes | p. 62 |
References | p. 62 |
Portfolio-Selection System Issues | p. 67 |
Expert System Components | p. 67 |
Rule-Based Systems | p. 70 |
Representation in Rule-Based Systems | p. 70 |
Inference Strategies | p. 71 |
Frame-Based Systems | p. 73 |
Investment Support Features | p. 75 |
Knowledge Representation | p. 75 |
Inference and Explanation | p. 77 |
Knowledge Acquisition and Maintenance | p. 78 |
System Architecture | p. 79 |
References | p. 79 |
Knowledge Representation and Inference | p. 81 |
Introduction | p. 82 |
The Rule Base | p. 82 |
Syntax of Rules | p. 82 |
Example Rules | p. 82 |
The Database | p. 84 |
Relational Database Examples | p. 84 |
Inheritance, Average-up, and Sum-up | p. 85 |
Working Memory | p. 87 |
Security Inference | p. 89 |
Conflict-Set Generation | p. 90 |
Composite-Grade Generation | p. 90 |
Explanation Synthesis | p. 93 |
Dialogues | p. 93 |
Company-Based Dialogue | p. 95 |
Industry-Based Dialogue | p. 96 |
Criteria-Based Dialogue | p. 97 |
Grade-Based Dialogue | p. 98 |
Conclusions | p. 98 |
References | p. 99 |
Handling Investment Uncertainties | p. 101 |
Introduction | p. 102 |
The Bayesian Approach | p. 102 |
Definitions and Formulas | p. 102 |
An Illustrative Example | p. 104 |
Handling Uncertain Evidence | p. 106 |
Handling More Than Two Levels of Hypotheses | p. 108 |
Inference Strategy in the Bayesian Approach | p. 108 |
The Sequence of Applying Evidence | p. 109 |
Stopping Rules | p. 110 |
Discussion | p. 111 |
The Certainty Factor Approach | p. 111 |
The Fuzzy Logic Approach | p. 112 |
Possibility Theory | p. 112 |
Fuzzy Logic | p. 112 |
A Fuzzy Logic-Based Expert System | p. 113 |
A Compensatory Fuzzy-Logic Approach | p. 114 |
Attenuation by the Credibility of Rules | p. 115 |
Discussion | p. 115 |
Nonmonotonic Reasoning | p. 116 |
Conclusions | p. 116 |
References | p. 116 |
Knowledge Acquisition, Integration, and Maintenance | p. 119 |
Introduction | p. 119 |
The Representation and Integration of Investor Preferences | p. 120 |
The Organization of Investor Preference Bases | p. 120 |
The Representation of Investor Preferences | p. 120 |
The Integration and Interpretation of Preferences | p. 123 |
Sources for Knowledge Acquisition | p. 124 |
Knowledge Structure and Maintenance | p. 125 |
Structuring Knowledge | p. 125 |
Maintenance Aids | p. 127 |
The Selective Integration of Relevant Knowledge | p. 128 |
Conclusions | p. 130 |
References | p. 130 |
Machine Learning | p. 131 |
Introduction | p. 131 |
Why Machine Learning? | p. 131 |
Machine-Learning Systems | p. 132 |
Learning Strategies | p. 133 |
Implied Distribution Surrogates | p. 133 |
Inductive Learning | p. 134 |
ID3 | p. 135 |
The Concept-Learning Algorithm | p. 135 |
Application of Inductive Learning to Investment Decisions | p. 139 |
The Potential of Inductive Learning in Investment | p. 139 |
Syntactic Pattern-Based Learning | p. 143 |
The SYNPLE Framework | p. 144 |
Performance | p. 149 |
Genetic Adaptive Algorithms | p. 152 |
The Genetic Algorithm Approach to Learning | p. 152 |
Problem Representation Issues | p. 153 |
A Genetic Algorithm for Trading Rule Generation | p. 154 |
Conclusions | p. 156 |
References | p. 156 |
Neural Networks | p. 159 |
Introduction | p. 159 |
Architecture of Neural Networks | p. 160 |
Learning in Neural Networks | p. 162 |
Strengths and Weaknesses | p. 164 |
Neural Network Applications | p. 166 |
Neural Networks for Stock Price Prediction | p. 167 |
Other Neural Network Applications | p. 170 |
Example of Integrating Neural Networks and Rules | p. 173 |
Conclusions | p. 177 |
References | p. 178 |
Integrating Knowledge with Portfolio Optimization | p. 183 |
Introduction | p. 183 |
An Unenhanced Markowitz Model Example | p. 184 |
The Interpretation of Knowledge | p. 185 |
Quadratic Programming with Prioritized Decision Variables | p. 188 |
Performance Evaluation | p. 192 |
Conclusions | p. 194 |
References | p. 195 |
Integrating Knowledge with Databases | p. 197 |
Introduction | p. 198 |
Database Evolution | p. 198 |
Relational Databases | p. 198 |
The Advent of Knowledge Bases | p. 199 |
Object-Oriented Databases | p. 200 |
The Management of Financial Data | p. 201 |
The Organization of Financial Data | p. 201 |
The Use of Financial Data | p. 204 |
The Management of Price and Trading Volume Data | p. 204 |
The Organization of Price and Volume Data | p. 204 |
The Uses of Price and Volume Data | p. 205 |
Management of the Function Base | p. 205 |
Functions | p. 205 |
Reserved Words | p. 206 |
Conclusions | p. 207 |
References | p. 207 |
An Illustrative Session with K-FOLIO | p. 209 |
Introduction | p. 209 |
Selecting Investment Characteristics, Environmental Assumptions, and Knowledge Sources | p. 210 |
Individual Stock Evaluation | p. 212 |
Industry Evaluation | p. 212 |
Criteria-Based Dialogue | p. 213 |
Grade-Based Listing | p. 214 |
Portfolio Selection | p. 215 |
Conclusions | p. 217 |
References | p. 227 |
Concluding Remarks | p. 229 |
System Design Criteria: A Summary | p. 229 |
Directions for Future Research | p. 231 |
Name Index | p. 233 |
Subject Index | p. 237 |
Table of Contents provided by Syndetics. All Rights Reserved. |
ISBN: 9781557388681
ISBN-10: 1557388687
Published: 19th November 1995
Format: Hardcover
Language: English
Number of Pages: 272
Audience: Professional and Scholarly
Publisher: IRWIN/MCGRAW HILL
Country of Publication: US
Edition Number: 2
Edition Type: Revised
Dimensions (cm): 24.13 x 16.51 x 2.54
Weight (kg): 0.6
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