Stock market modeling a system adaptation approach

STOCK MARKET MODELING: A SYSTEM ADAPTATION APPROACH ZHENG XIAOLIAN (B.Eng, Xiamen University, China; M.Eng, Xiamen University, China) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF ELECTRICAL & COMPUTER ENGINEERING NATIONAL UNIVERSITY OF SINGAPORE 2012 Acknowledgements First and foremost, I would like to express my heartfelt gratitude to my supervisors, Prof Ben M Chen I am very grateful that I was given this chance to pursue my PhD degree under his guidance and in the area of financial market modeling based on systems approach Without his guidance and support, it would have not been possible for me to complete my PhD program His vast knowledge and diligence, his dedication to research has always encouraged me I am much grateful to professors in the department of electrical and computer engineering for their inspiring lectures which provided many in-depth knowledge on my research area Especially, I would like to express my gratitude to Prof Qing-Guo Wang, Prof Cheng Xiang, Prof Tong H Lee, Dr Kai-Yew Lum, Prof Delin Chu and Prof Hai Lin for their valuable suggestions, generous help, and professional knowledge Special thanks are given to the comrades in our research group, including Dr Kemao Peng, Dr Biao Wang, Dr Guowei Cai, Dr Feng Lin, Dr Delin Luo, Dr Miaobo Dong, Dr Ben Yun, Xiangxu Dong, Fei Wang, Swee King Phang, Kevin Ang, Shiyu Zhao, Jinqiang Cui, Kun Li and Jing Lin, for their valuable suggestions and help which are important in making my thesis a reality I would also like to extend my grateful thanks to all of the friends in Control and Simulation Lab, with whom I have enjoyed every minute during these years I would like to give special thanks to the lab officers, Mr Hengwei Zhang and Ms Sarasupathi for being considerate and helpful My deepest gratitude should go to my parents and my Grandma for their love and encouragement I also owe my sincere gratitude to my friends who gave me support during these years Special thanks go to Dr Nan Jiang, Dr Sen Yan, Jie Zheng, and Dr Lichao Cheng for their i ACKNOWLEDGEMENTS ii valuable suggestions and great help in my research and my life I would like to express my deepest love and thanks to them all Finally, I would also like to thank the National University of Singapore for providing me the scholarship to pursue the higher degree Contents Acknowledgements i Contents iii Summary viii List of Tables x List of Figures xii Nomenclature xvi Introduction 1.1 Introduction 1.2 Stock Market Analysis 1.3 Motivation and Contribution of This Work 1.4 Preview of Each Chapter 12 iii CONTENTS iv A System Adaptation Framework 16 2.1 Introduction 16 2.2 Design of System Adaptation Framework 17 2.3 Internal Model Design 21 2.4 Adaptive Filter Design 25 2.5 Case Study: Dow Jones Industrial Average 30 2.5.1 Data Description 30 2.5.2 Internal Model Estimation 31 Conclusion 33 2.6 Market Input Analysis 34 3.1 Introduction 34 3.2 Influential Factor Selection 35 3.2.1 Causality Test 36 3.2.2 Redundant Variable Test 41 Influential factors of Dow Jones Industrial Average 42 3.3.1 Empirical Selection 42 3.3.2 Time-varying Causality Test Results 47 3.3.3 Nonlinear Causality Test Results 50 3.3.4 Redundant Variable Test Results 51 Conclusion 52 3.3 3.4 CONTENTS v Analysis of Dow Jones Industrial Average 53 4.1 Introduction 53 4.2 Measurement of Predicting Performances 55 4.3 Market Predicting Performance and Analysis 55 4.3.1 Preliminary Analysis 56 4.3.2 One-step-ahead Predicting Performances 58 4.3.3 Performances Analysis and Discussion 59 Conclusion 63 4.4 Selected Asian Markets 64 5.1 Introduction 64 5.2 Shanghai Stock Exchange Composite Index 65 5.2.1 Data Description and Preparation 65 5.2.2 Input Selection 66 5.2.3 Market Predicting Performance and Analysis 76 Hong Kong Hang Seng Index 80 5.3.1 Data Description and Preparation 80 5.3.2 Input Selection 81 5.3.3 Market Predicting Performance and Analysis 89 Singapore Strait Times Index 95 5.4.1 Data Description and Preparation 95 5.4.2 Input Selection 96 5.4.3 Market Predicting Performance and Analysis 105 Conclusion 110 5.3 5.4 5.5 CONTENTS vi Forecasting of Major Market Turning Periods 111 6.1 Introduction 111 6.2 Market Turning Periods Forecasting 113 6.2.1 Turning Periods Forecasting Procedure 113 6.2.2 Frequency Domain Identification Rules 114 6.2.3 Parameter Selection 116 6.2.4 Dow Jones Industrial Average 116 6.2.5 Result Analysis and Discussion 124 Structural Changes In Macroeconomic Situation 124 6.3.1 Macroeconomic Indicators Selection 124 6.3.2 Testing For Structural Breaks 125 Other Stock Markets 129 6.4.1 Shanghai Stock Exchange Composite Index 129 6.4.2 Hong Kong Hang Seng Index 131 6.4.3 Singapore Strait Times Index 134 System Instability Detection 136 6.5.1 Motivation 136 6.5.2 Detection Method 137 6.5.3 Detection Results 139 6.5.4 Discussion 150 Conclusion 151 6.3 6.4 6.5 6.6 CONTENTS vii Technical Analysis Toolkit 152 7.1 Introduction 152 7.2 Functions of T-TAS 154 7.2.1 User Management 154 7.2.2 Stock Data Manipulation 154 7.2.3 Data Loading System 157 7.2.4 Technical Analysis 159 7.2.5 System Adaptation Framework 166 Implementation 168 7.3.1 MATLAB GUI 170 7.3.2 Using Java in MATLAB 171 Conclusion 172 7.3 7.4 Conclusions and Future Research 173 8.1 Conclusion 173 8.2 Further Research 175 Bibliography 178 Appendix: Publication List 193 Summary The modeling of financial markets has aroused great interests in recent decades Financial market is a complex system involving various interacting factors including psychological, social and political aspects This calls for a comprehensive study of the market behavior Systems theory provides a promising research direction This thesis aims to develop a general framework based on systems theory to depict and analyze the financial markets It is designed to combine various foundations together and thus provide more meaningful insights into the market behavior Firstly, a system adaptation framework has been proposed for modeling the stock market, or the financial market in general, from a dynamic system point of view Feedback and force are two fundamental elements considered in its design Based on a feedback adaptation scheme, we modeled the movement of stock market indices within this closed-loop framework that is composed of an internal dynamic model and an adaptive filter The output-error model was adopted as the internal model to track the price trend It deals with the internal force contained in the historical stock prices To analyze the external force which was defined as the differences between actual and internal prices, the adaptive filter used a time-varying state space model as a cycle generator Through this framework, the slow and fast dynamics of the market are respectively captured We then introduced the estimation processes of both the internal model and the adaptive filter The input-output behavior, and internal as well as external forces were identified accordingly The fast changing external force is generated by the information outside the stock market which is usually considered as the market input As the inputs have been proven to be essential in obtaining a good predicting performance in our framework, a double selection method based on viii SUMMARY ix both empirical and statistical knowledge was proposed to select the influential factors of a market as its inputs Specifically, this selection procedure consists of an empirical selection followed by both time-varying and nonlinear causality tests, and then a multicollinearity test Influential factors from both economic and sentiment aspects were considered in this work After establishing this system adaptation framework, its predictive ability was assessed by the one-step-ahead prediction of closing prices This framework has been applied to the stock markets of U.S., mainland China, Hong Kong and Singapore represented by the Dow Jones Industrial Average (DJIA), Shanghai Stock Exchange Composite Index (SSE), Hong Kong Hang Seng Index (HSI) and Singapore Strait Times Index (STI) respectively With its particular inputs, prediction results in each market supported that our framework has a much better predicting performance than certain traditional models especially in complicated economic situations The selected four markets include both developed and emerging markets, comparison between which reveals many special market properties The predicting performance of our framework was also found to be better in the developed market Related analysis was presented regarding to each market With this framework in hand, an application of this framework has been introduced focusing on the forecasting of major market turning periods A frequency pattern-based forecasting method was first carried out to capture the characteristic frequency patterns that always appear during the major market turnings The forecasting accuracy was greatly improved by detecting the instability of the internal model as the confirmations This forecasting has also been successfully applied to the above selected four markets To facilitate the analysis of the stock market, a MATLAB toolkit with a user-friendly graphical interface and advanced functionalities has been developed The toolkit provides basic and advanced technical analysis of stocks as well as some functions from our system adaptation framework In conclusion, the complete system adaptation framework is established based on the systems theory and statistical knowledge It provides a comprehensive analysis and more meaningful insights of the market behavior Some prospective directions for future research are also included BIBLIOGRAPHY 179 [10] H Roberts, Statistical Versus Clinical Prediction Of The Stock Market, Unpublished Manuscript, Crsp, University Of Chicago, 1967 [11] B G Malkiel, A random walk down wall street, New York: W W Norton & Company, 1973 [12] A L Turner and E J Weigel, An Analysis of Stock Market Volatility, Russell Research 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