Scholars' Mine Doctoral Dissertations Student Theses and Dissertations Fall 2011 Integrated computational intelligence and Japanese candlestick method for short-term financial forecasting Takenori Kamo Follow this and additional works at: https://scholarsmine.mst.edu/doctoral_dissertations Part of the Operations Research, Systems Engineering and Industrial Engineering Commons Department: Engineering Management and Systems Engineering Recommended Citation Kamo, Takenori, "Integrated computational intelligence and Japanese candlestick method for short-term financial forecasting" (2011). Doctoral Dissertations. 1908. https://scholarsmine.mst.edu/doctoral_dissertations/1908 This thesis is brought to you by Scholars' Mine, a service of the Missouri S&T Library and Learning Resources. This work is protected by U. S. Copyright Law. Unauthorized use including reproduction for redistribution requires the permission of the copyright holder. For more information, please contact [email protected]. INTEGRATED COMPUTATIONAL INTELLIGENCE AND JAPANESE CANDLESTICK METHOD FOR SHORT-TERM FINANCIAL FORECASTING by TAKENORI KAMO A DISSERTATION Presented to the Faculty of the Graduate School of the MISSOURI UNIVERSITY OF SCIENCE AND TECHNOLOGY In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY In ENGINEERING MANAGEMENT 2011 Approved Cihan H. Dagli, Advisor Venkata Allada Elizabeth Cudney Suzanna Long Gregory Gelles 2011 Takenori Kamo All Rights Reserved iii ABSTRACT This research presents a study of intelligent stock price forecasting systems using interval type-2 fuzzy logic for analyzing Japanese candlestick techniques. Many intelligent financial forecasting models have been developed to predict stock prices, but many of them do not perform well under unstable market conditions. One reason for poor performance is that stock price forecasting is very complex, and many factors are involved in stock price movement. In this environment, two kinds of information exist, including quantitative data, such as actual stock prices, and qualitative data, such as stock traders’ opinions and expertise. Japanese candlestick techniques have been proven to be effective methods for describing the market psychology. This study is motivated by the challenges of implementing Japanese candlestick techniques to computational intelligent systems to forecast stock prices. The quantitative information, Japanese candlestick definitions, is managed by type-2 fuzzy logic systems. The qualitative data sets for the stock market are handled by a hybrid type of dynamic committee machine architecture. Inside this committee machine, generalized regression neural network-based experts handle actual stock prices for monitoring price movements. Neural network architecture is an effective tool for function approximation problems such as forecasting. Few studies have explored integrating intelligent systems and Japanese candlestick methods for stock price forecasting. The proposed model shows promising results. This research, derived from the interval type-2 fuzzy logic system, contributes to the understanding of Japanese candlestick techniques and becomes a potential resource for future financial market forecasting studies. iv ACKNOWLEDGMENTS I would like to express my deepest appreciation to all of the individuals whose help and support made this dissertation possible. I would especially like to offer my gratitude to my advisor, Dr. Cihan H. Dagli, who has given me continuous guidance, encouragement, and advice. I am deeply grateful to my committee, Dr. Venkata Allada, Dr. Elizabeth Cudney, Dr. Suzanna Long, and Dr. Gregory Gelles, for their valuable suggestions and perceptive comments. I also thank my colleagues from the Smart Engineering Systems Laboratory. I enjoyed intellectually stimulating, yet entertaining discussions with all of you. I am sincerely thankful to all of my friends. Your warm hearts have always encouraged and supported my life. You also treated me like one of your family members. Sometimes, I have become your son, grandson, or brother. I will never forget your kindness, thoughtfulness, and support. Finally and most importantly, I thank my family in Japan for their infinite support, encouragement, and love that made this endeavor successful. v TABLE OF CONTENTS Page ABSTRACT ....................................................................................................................... iii ACKNOWLEDGMENTS ................................................................................................. iv LIST OF ILLUSTRATIONS .............................................................................................. x LIST OF TABLES ........................................................................................................... xiii SECTION 1. INTRODUCTION ...................................................................................................... 1 1.1. OVERVIEW ....................................................................................................... 1 1.2. RESEARCH MOTIVATION AND OBJECTIVES ........................................... 3 1.3. ORGANIZATION .............................................................................................. 5 2. LITERATURE REVIEW ........................................................................................... 7 2.1. DEMAND OF FORECASTING ........................................................................ 7 2.2. FORECASTING IN FINANCE ......................................................................... 9 2.2.1. Statistical Methods for Time Series Forecasting .................................... 10 2.2.2. Neural Networks ..................................................................................... 12 2.2.2.1 Backpropagation .........................................................................12 2.2.2.2 Recurrent neural networks ..........................................................15 2.2.2.3 Radial basis function neural networks ........................................17 2.2.2.4 Generalized regression neural networks .....................................18 2.2.3. Hybrid Approach ................................................................................... 20 2.3. DATA TYPES .................................................................................................. 22 2.3.1. Qualtative Data vs. Quantiative Data ..................................................... 22 2.3.2. Trading Volume vs. Non-Trading Volume ............................................ 24 2.3.3. Data Periods ........................................................................................... 24 2.4. FORECASTING METHODS: STATISTICAL VS. NEURAL NETWORK APPROACHES .......................................................... 25 2.5. MARKET CONDITIONS AND FORECASTING .......................................... 27 2.6. JAPANESE CANDLESTICK CHART TECHNIQUES.................................. 27 2.6.1. History of the Candlestick Chart Techniques ......................................... 27 vi 2.6.2. Basic Terms of Candlestick Chart Techniques ...................................... 29 2.7. FUZZY LOGIC ................................................................................................ 29 2.7.1. Fuzzy Logic for Forecasting ................................................................... 30 2.7.2. Type-1 Fuzzy Logic and Type-2 Fuzzy Logic ....................................... 32 2.7.3. Interval Type-2 Fuzzy Logic .................................................................. 32 2.8. SUMMARY ...................................................................................................... 34 3. TOOLS FOR HYBRID COMPUTATIONS ............................................................ 36 3.1. NEURAL NETWORKS ................................................................................... 36 3.1.1. Background ............................................................................................ 36 3.1.2. Basic Structure of Neural Networks ....................................................... 36 3.1.3. Learning Processof Neural Networks ..................................................... 38 3.1.4. Strength of Neural Networks .................................................................. 38 3.2. COMMITTEE MACHINE ............................................................................... 40 3.2.1. Background ............................................................................................ 40 3.2.2. Structure of a Committee Machine ........................................................ 40 3.2.3. Learning Process .................................................................................... 44 3.2.4. Strength of a Committee Machine .......................................................... 45 3.3. FUZZY LOGIC ................................................................................................ 45 3.3.1. Background ............................................................................................ 45 3.3.2. Type-1 Fuzzy Logic System ................................................................... 46 3.3.3. Type-2 Fuzzy Logic System ................................................................... 48 3.3.4. Strength of Type-2 Fuzzy Logic ............................................................ 52 4. CANDLESTICK CHART TECHNIQUES .............................................................. 54 4.1. BUILDING A CANDLESTICK CHART .......................................................