‘An Artificial Neural Networks Primer with Financial Applications Examples in Financial Distress Predictions and Foreign Exchange Hybrid Trading System ’ by Dr Clarence N W Tan, PhD Bachelor of Science in Electrical Engineering Computers (1986), University of Southern California, Los Angeles, California, USA Master of Science in Industrial and Systems Engineering (1989), University of Southern California Los Angeles, California, USA Masters of Business Administration (1989) University of Southern California Los Angeles, California, USA Graduate Diploma in Applied Finance and Investment (1996) Securities Institute of Australia Diploma in Technical Analysis (1996) Australian Technical Analysts Association Doctor of Philosophy Bond University (1997) URL: http://w3.to/ctan/ E-mail: [email protected] School of Information Technology, Bond University, Gold Coast, QLD 4229, Australia Table of Contents Table of Contents 1. INTRODUCTION TO ARTIFICIAL INTELLIGENCE AND ARTIFICIAL NEURAL NETWORKS ..........................................................................................................................................2 1.1 INTRODUCTION ...........................................................................................................................2 1.2 ARTIFICIAL INTELLIGENCE ..........................................................................................................2 1.3 ARTIFICIAL INTELLIGENCE IN FINANCE .......................................................................................4 1.3.1 Expert System ...................................................................................................................4 1.3.2 Artificial Neural Networks in Finance..............................................................................4 1.4 ARTIFICIAL NEURAL NETWORKS.................................................................................................5 1.5 APPLICATIONS OF ANNS.............................................................................................................7 1.6 REFERENCES .............................................................................................................................10 2. AN ARTIFICIAL NEURAL NETWORKS’ PRIMER ...........................................................14 2.1 CHRONICLE OF ARTIFICIAL NEURAL NETWORKS DEVELOPMENT..............................................14 2.2 BIOLOGICAL BACKGROUND ......................................................................................................16 2.3 COMPARISON TO CONVENTIONAL COMPUTATIONAL TECHNIQUES............................................17 2.4 ANN STRENGTHS AND WEAKNESSES .......................................................................................19 2.5 BASIC STRUCTURE OF AN ANN ................................................................................................20 2.6 CONSTRUCTING THE ANN ........................................................................................................21 2.7 A BRIEF DESCRIPTION OF THE ANN PARAMETERS ...................................................................22 2.7.1 Learning Rate .................................................................................................................22 2.7.2 Momentum ......................................................................................................................22 2.7.3 Input Noise......................................................................................................................23 2.7.4 Training and Testing Tolerances....................................................................................23 2.8 DETERMINING AN EVALUATION CRITERIA ................................................................................23 2.9 REFERENCES .............................................................................................................................24 3. THE TECHNICAL AND STATISTICAL ASPECTS OF ARTIFICIAL NEURAL NETWORKS ........................................................................................................................................27 3.1 ARTIFICIAL NEURAL NETWORK MODELS ..................................................................................27 3.2 NEURODYNAMICS .....................................................................................................................27 3.2.1 Inputs..............................................................................................................................27 3.2.2 Outputs ...........................................................................................................................28 3.2.3 Transfer (Activation) Functions......................................................................................28 3.2.4 Weighing Schemes and Learning Algorithms.................................................................30 3.3 NEURAL NETWORKS ARCHITECTURE........................................................................................30 3.3.1 Types of interconnections between neurons ...................................................................30 3.3.2 The Number of Hidden Neurons.....................................................................................31 3.3.3 The Number of Hidden Layers........................................................................................32 3.3.4 The Perceptron ...............................................................................................................32 3.3.5 Linear Separability and the XOR Problem.....................................................................34 3.3.6 The Multilayer Perceptron .............................................................................................37 3.4 LEARNING.................................................................................................................................39 3.4.1 Learning Algorithms.......................................................................................................39 3.5 STATISTICAL ASPECTS OF ARTIFICIAL NEURAL NETWORKS ......................................................43 3.5.1 Comparison of ANNs to Statistical Analysis...................................................................43 3.5.2 ANNs and Statistical Terminology..................................................................................43 3.5.3 Similarity of ANN Models to Statistical Models .............................................................44 3.5.4 ANNs vs. Statistics ..........................................................................................................45 3.5.5 Conclusion of ANNs and Statistics .................................................................................46 3.6 REFERENCES .............................................................................................................................47 4. USING ARTIFICIAL NEURAL NETWORKS TO DEVELOP AN EARLY WARNING PREDICTOR FOR CREDIT UNION FINANCIAL DISTRESS.....................................................51 i Table of Contents 4.1 INTRODUCTION .........................................................................................................................51 4.2 EXISTING STUDIES: METHODOLOGICAL ISSUES.........................................................................51 4.3 APPLICATIONS OF ANNS IN PREDICTING FINANCIAL DISTRESS .................................................53 4.4 DATA AND TESTING METHODOLOGY ........................................................................................54 4.4.1 In-sample (Training) and Out-of Sample (Validation) Data Sets...................................54 4.4.2 Input (Independent) Variables........................................................................................55 4.5 ANN TOPOLOGY AND PARAMETER SETTINGS ..........................................................................57 4.5.1 Learning Rate .................................................................................................................58 4.5.2 Momentum ......................................................................................................................58 4.5.3 Input Noise......................................................................................................................59 4.5.4 Training and Testing Tolerances....................................................................................59 4.6 RESULTS ...................................................................................................................................59 4.6.1 Training Set (In-sample) Discussion ..............................................................................60 4.6.2 Validation Set (Out-of-sample) Result Comparison .......................................................60 4.6.3 Validation Set (Out-of-sample) Evaluation ....................................................................61 4.6.4 Result Summary of Type I and Type II Errors................................................................70 4.7 ASSUMPTIONS AND LIMITATION OF METHODOLOGY .................................................................70 4.8 CONCLUSION.............................................................................................................................70 4.9 MANAGERIAL AND IMPLEMENTATION ISSUES ...........................................................................71 4.10 FUTURE RESEARCH ..............................................................................................................72