Margin variations in support vector regression for the stock market prediction.

• Other Authors: Yang, Haiqin.
• Format: Others
• Language: English; Chinese
• Published: 2003
• Subjects: Stock price forecasting > Mathematical models; Regression analysis
• Online Access: http://library.cuhk.edu.hk/record=b5891624; http://repository.lib.cuhk.edu.hk/en/item/cuhk-324265

Yang, Haiqin. === Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. === Includes bibliographical references (leaves 98-109). === Abstracts in English and Chinese. === Abstract --- p.ii === Acknowledgement --- p.v === Chapter 1 --- Introduction --- p.1 === Chapter 1.1 --- Time Series Prediction and Its Problems --- p.1 === Chapter 1.2 --- Major Contributions --- p.2 === Chapter 1.3 --- Thesis Organization --- p.3 === Chapter 1.4 --- Notation --- p.4 === Chapter 2 --- Literature Review --- p.5 === Chapter 2.1 --- Framework --- p.6 === Chapter 2.1.1 --- Data Processing --- p.8 === Chapter 2.1.2 --- Model Building --- p.10 === Chapter 2.1.3 --- Forecasting Procedure --- p.12 === Chapter 2.2 --- Model Descriptions --- p.13 === Chapter 2.2.1 --- Linear Models --- p.15 === Chapter 2.2.2 --- Non-linear Models --- p.17 === Chapter 2.2.3 --- ARMA Models --- p.21 === Chapter 2.2.4 --- Support Vector Machines --- p.23 === Chapter 3 --- Support Vector Regression --- p.27 === Chapter 3.1 --- Regression Problem --- p.27 === Chapter 3.2 --- Loss Function --- p.29 === Chapter 3.3 --- Kernel Function --- p.34 === Chapter 3.4 --- Relation to Other Models --- p.36 === Chapter 3.4.1 --- Relation to Support Vector Classification --- p.36 === Chapter 3.4.2 --- Relation to Ridge Regression --- p.38 === Chapter 3.4.3 --- Relation to Radial Basis Function --- p.40 === Chapter 3.5 --- Implemented Algorithms --- p.40 === Chapter 4 --- Margins in Support Vector Regression --- p.46 === Chapter 4.1 --- Problem --- p.47 === Chapter 4.2 --- General ε-insensitive Loss Function --- p.48 === Chapter 4.3 --- Accuracy Metrics and Risk Measures --- p.52 === Chapter 5 --- Margin Variation --- p.55 === Chapter 5.1 --- Non-fixed Margin Cases --- p.55 === Chapter 5.1.1 --- Momentum --- p.55 === Chapter 5.1.2 --- GARCH --- p.57 === Chapter 5.2 --- Experiments --- p.58 === Chapter 5.2.1 --- Momentum --- p.58 === Chapter 5.2.2 --- GARCH --- p.65 === Chapter 5.3 --- Discussions --- p.72 === Chapter 6 --- Relation between Downside Risk and Asymmetrical Margin Settings --- p.77 === Chapter 6.1 --- Mathematical Derivation --- p.77 === Chapter 6.2 --- Algorithm --- p.81 === Chapter 6.3 --- Experiments --- p.83 === Chapter 6.4 --- Discussions --- p.86 === Chapter 7 --- Conclusion --- p.92 === Chapter A --- Basic Results for Solving SVR --- p.94 === Chapter A.1 --- Dual Theory --- p.94 === Chapter A.2 --- Standard Method to Solve SVR --- p.96 === Bibliography --- p.98