Restaurant Industry Stock Price Forecasting Model Utilizing Artificial Neural Networks to Combine Fundamental and Technical Analysis A thesis presented to the faculty of the Russ College of Engineering and Technology of Ohio University In partial fulfillment of the requirements for the degree Master of Science Ronald W. Dravenstott June 2012 © 2012 Ronald W. Dravenstott. All Rights Reserved. 2 This thesis titled Restaurant Industry Stock Price Forecasting Model Utilizing Artificial Neural Networks to Combine Fundamental and Technical Analysis by RONALD W. DRAVENSTOTT has been approved for the Department of Industrial and Systems Engineering and the Russ College of Engineering and Technology by Gary R. Weckman Associate Professor of Industrial and Systems Engineering Dennis Irwin Dean, Russ College of Engineering and Technology 3 ABSTRACT DRAVENSTOTT, RONALD W., M.S., June 2012, Industrial and Systems Engineering Restaurant Industry Stock Price Forecasting Model Utilizing Artificial Neural Networks to Combine Fundamental and Technical Analysis Director of Thesis: Gary R. Weckman Stock price forecasting is a classic problem facing analysts. Forcasting models have been developed for predicting individual stocks and stock indices around the world and in numerous industries. According to a literature review, these models have yet to be applied to the restaurant industry. Strategies for forecasting typically include fundamental and technical variables. In this thesis, fundamental and technical inputs were combined into an Artificial Neural Network stock prediction model for the restaurant industry. Models were designed to forecast 1 week, 4 weeks, and 13 weeks into the future. The model performed better than the benchmarks. The prediction accuracy of the model reached as high as 60%. The model with the most success was a Multilayer Perceptron Artificial Neural Network with 2 hidden layers having 40 and 20 processing elements in those layers using the hyperbolic tangent transfer function and Delta Bar Delta learning algorithm. Approved: _____________________________________________________________ Gary R. Weckman Associate Professor of Industrial and Systems Engineering 4 ACKNOWLEDGMENTS Thank you to those who helped and supported me through my educational journey. I would especially like to thank Kelley, my parents, John, and Dr. Weckman. I could not have done this without you. 5 TABLE OF CONTENTS Page Abstract ............................................................................................................................... 3 Acknowledgments............................................................................................................... 4 List of Tables ...................................................................................................................... 7 List of Figures ..................................................................................................................... 8 1 Introduction ...................................................................................................................... 9 2 Approaches to Stock Market Prediction ........................................................................ 12 3 Literature Review........................................................................................................... 15 4 Data Set Description ...................................................................................................... 22 5 Methodology .................................................................................................................. 24 5.1 Classification Models ............................................................................................. 25 5.2 Function Approximation Models ............................................................................ 27 5.3 Plan of Action ......................................................................................................... 28 5.4 Building and Training the ANN Models ................................................................ 32 5.5 Testing Different Network Architectures ............................................................... 33 5.5.1 Single-Company Models ................................................................................. 33 5.5.2 All-Company Models ....................................................................................... 33 5.5.3 Testing Different Network Sizes ..................................................................... 34 5.5.4 Testing Different Learning Algorithms ........................................................... 34 5.6 Best Performing Model Parameters ........................................................................ 35 6 Results ............................................................................................................................ 36 6.1 Classification Networks .......................................................................................... 36 6.2 Function Approximation Models ............................................................................ 36 6.2.1 Performing Sensitivity Analysis ...................................................................... 39 6.2.2 Constructing Models Based on Sensitivity Analysis ....................................... 39 6.2.3 Evaluating Sensitivity Analysis ....................................................................... 39 6.2.4 Further Evaluating Initial Models .................................................................... 40 6.3 Development of the Maintenance Approach .......................................................... 41 6 6.4 Maintenance Approach Results .............................................................................. 42 6.5 Classification Networks .......................................................................................... 43 6.5.1 2-Class Prediction Models ............................................................................... 43 6.5.2 3-Class Prediction Models ............................................................................... 44 6.5.3 5-Class Prediction Models ............................................................................... 45 6.6 Benchmark Results ................................................................................................. 48 6.6.1 Stepwise Multilinear Regression Results ......................................................... 48 6.6.2 Analyst Results ................................................................................................ 50 6.6.3 Buy and Hold Results ...................................................................................... 53 7 Summary and Discussion ............................................................................................... 55 8 Conclusion ..................................................................................................................... 59 8.1 Future Research ...................................................................................................... 62 References ......................................................................................................................... 63 7 LIST OF TABLES Page Table 1: Data Inputs and Lag Lengths .............................................................................. 20 Table 2: Source Legend .................................................................................................... 21 Table 3: Company List...................................................................................................... 23 Table 4: Model Evaluation Examples ............................................................................... 26 Table 5: Model Characteristics Table ............................................................................... 27 Table 6: Model Evaluation Example ................................................................................ 28 Table 7: Initial Function Approximation Model Results .................................................. 37 Table 8: 13-week Prediction Model Confusion Matrix .................................................... 40 Table 9: 4-week Prediction Model Confusion Matrix ...................................................... 41 Table 10: 4-week Prediction Model Results with Differing Test Period Lengths ............ 42 Table 11: Maintenance ANN Prediction Model Results .................................................. 42 Table 12: Maintenance ANN Prediction Model Confusion Matrix .................................. 43 Table 13: 2-Class Prediction Model Confusion Matrix .................................................... 44 Table 14: 3-class Prediction Model Confusion Matrix ..................................................... 45 Table 15: 5-class Prediction Model Confusion Matrix ..................................................... 46 Table 16: Results of 5-class Prediction Models ................................................................ 47 Table 17: Stepwise Multilinear Regression Results ......................................................... 49 Table 18: Stepwise Multilinear Regression Confusion Matrix......................................... 49 Table 19: Stepwise Multilinear Regression Price Coefficients ........................................ 50 Table 20: Analyst Confusion Matrix ................................................................................ 53 Table 21: Buy and Hold Confusion Matrix .....................................................................