AN INTEGRATED STOCK MARKET FORECASTING MODEL USING NEURAL NETWORKS A thesis presented to the faculty of the Fritz J. and Dolores H. Russ College of Engineering and Technology of Ohio University In partial fulfillment of the requirements for the degree Master of Science Sriram Lakshminarayanan August 2005 © 2005 Sriram Lakshminarayanan All Rights Reserved This thesis entitled AN INTEGRATED STOCK MARKET FORECASTING MODEL USING NEURAL NETWORKS By Sriram Lakshminarayanan has been approved for the Department of Industrial and Manufacturing Systems Engineering and the Russ College of Engineering and Technology by Gary R. Weckman Associate Professor of Industrial & Manufacturing Systems Engineering Dennis Irwin Dean, Fritz J. and Dolores H. Russ College of Engineering and Technology LAKSHMINARAYANAN SRIRAM. M.S. August 2005. Industrial and Manufacturing Systems Engineering An Integrated Stock Market Forecasting Model Using Neural Networks (126pp.) Director of Thesis: Gary Weckman This thesis focuses on the development of a stock market forecasting model based on an Artificial Neural Network architecture. This study constructs a hybrid model utilizing various technical indicators, Elliott’s wave theory, sensitivity analysis and fuzzy logic. Initially, a baseline network is constructed based on available literature. The baseline model is then improved by applying several useful information domains to the different models. Optimizations of the Neural Network models are performed by augmenting the network with useful information at every stage. Approved: Gary Weckman Associate Professor of Industrial and Manufacturing Systems Engineering 5 TABLE OF CONTENTS ABSTRACT........................................................................................................................ 4 LIST OF FIGURES ............................................................................................................ 8 LIST OF TABLES............................................................................................................ 10 CHAPTER 1. INTRODUCTION ................................................................................ 11 1.1 Description of Forecasting ...........................................................................................11 1.2 Forecasting Problems ...................................................................................................12 1.3 Previous Research ........................................................................................................13 1.4 Current Research ..........................................................................................................14 1.5 Thesis Structure............................................................................................................15 CHAPTER 2. BACKGROUND .................................................................................. 17 2.1 Artificial Neural Networks ...........................................................................................17 2.1.1 Introduction ...........................................................................................................................17 2.1.2 Applications of Neural Networks ..........................................................................................18 2.1.3 Common Classification of Neural Networks.........................................................................19 2.1.4 Neural Network Architectures and Training .........................................................................20 2.1.5 Neural-Network Training ......................................................................................................27 2.2 Elliott’s Wave Theory ..................................................................................................30 2.2.1 History...................................................................................................................................30 6 2.2.2 Principles of Elliott Wave Theory .........................................................................................30 2.2.3 Fibonacci Series and Elliott’s Wave Theory .........................................................................34 2.3 Stock Market Indicators ...............................................................................................36 2.3.1 Introduction ...........................................................................................................................36 2.3.2 Classification of Indicators....................................................................................................37 2.3.3 Basic Indicators .....................................................................................................................38 2.4 Fuzzy Logic..................................................................................................................42 2.4.1 Introduction to Fuzzy Logic ..................................................................................................42 2.4.2 Fuzzy Set Theory...................................................................................................................43 CHAPTER 3. METHODOLOGY ............................................................................... 46 3.1 Stock Market Data........................................................................................................46 3.2 Analysis of Preliminary Indicators...............................................................................49 3.3 Indicator Selection Using Self Organizing Map...........................................................50 3.4 Elliott Wave Implementation........................................................................................55 3.5 Summary.......................................................................................................................57 CHAPTER 4. RESULTS AND DISCUSSION........................................................... 58 4.1 Performance of the Benchmark Network .....................................................................58 4.2 Indicator Analysis using Self Organizing Maps...........................................................63 7 4.3 Elliott Wave Indicators Implementation.......................................................................69 CHAPTER 5. CONCLUSIONS AND FUTURE RESEARCH .................................. 75 5.1 Conclusions ..................................................................................................................75 5.2 Future Research............................................................................................................77 REFERENCES ................................................................................................................. 79 APPENDIX A................................................................................................................... 82 APPENDIX B ................................................................................................................... 97 APPENDIX C ................................................................................................................. 112 8 LIST OF FIGURES Figure 2-1 Schematic Diagram of a Neural Network ....................................................... 18 Figure 2-2 Functional Diagram of Perceptron.................................................................. 21 Figure 2-3 Generalized Feed Forward Nework (Adapted from NeuroSolutions) ............ 23 Figure 2-4 Sigmoid Function............................................................................................ 24 Figure 2-5 Gaussian Function........................................................................................... 25 Figure 2-6 2-D Kohonen Self Organizing Map (Adapted from [18]) .............................. 26 Figure 2-7 Cross Validation during Training.................................................................... 29 Figure 2-8 Elliott's Five Wave Pattern.............................................................................. 31 Figure 2-9 A Typical Elliott Wave Cycle. (Adapted from [13]) ...................................... 33 Figure 2-10 Fibonacci Ratios in Elliott Wave .................................................................. 35 Figure 2-11 Fibonacci Ratio Possibilities in Elliot Wave................................................. 35 Figure 2-12 Fuzzy Sets Example...................................................................................... 44 Figure 2-13 Fuzzy System Schematic Diagram (Adapted from [20] )............................. 45 Figure 3-1 Generalized Feed Forward Network as represented by NeuroSolutions ........ 49 Figure 3-2 NeuroSolutions Representation of a Self Organizing Map............................. 52 Figure 3-3 Final Generalized Feed Forward Network implementation............................ 54 Figure 3-4 Self Organizing Map Methodology ................................................................ 54 Figure 3-5 Probability of Data being Turning Point......................................................... 56 Figure 3-6 Forecasting Decisions ..................................................................................... 56 9 Figure 3-7 Methodology ................................................................................................... 57 Figure 4-1 MLP Network Performance of AGP............................................................... 61 Figure 4-2 GFF Network Performance of AGP...............................................................