by Siu-ming Cha. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 123-132). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgements --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Objective --- p.1 / Chapter 1.2 --- Problem --- p.1 / Chapter 1.2.1 --- Motivation --- p.1 / Chapter 1.2.2 --- Approaches --- p.3 / Chapter 1.3 --- Contributions --- p.4 / Chapter 1.4 --- Organization of this Thesis --- p.5 / Chapter 2 --- Independent Component Analysis --- p.8 / Chapter 2.1 --- Overview --- p.8 / Chapter 2.2 --- The Blind Source Separation Problem --- p.8 / Chapter 2.3 --- Statistical Independence --- p.10 / Chapter 2.3.1 --- Definition --- p.10 / Chapter 2.3.2 --- Measuring Independence --- p.11 / Chapter 2.4 --- Developments of ICA Algorithms --- p.15 / Chapter 2.4.1 --- ICA Algorithm: Removal of Higher Order Dependence --- p.16 / Chapter 2.4.2 --- Assumptions in ICA Algorithms --- p.19 / Chapter 2.4.3 --- Joint Approximate Diagonalization of Eigenmatrices(JADE) --- p.20 / Chapter 2.4.4 --- Fast Fixed Point Algorithm for Independent Component Analysis(FastICA) --- p.21 / Chapter 2.5 --- Principal Component Analysis and Independent Component Anal- ysis --- p.23 / Chapter 2.5.1 --- Theoretical Comparisons between ICA and PCA --- p.23 / Chapter 2.5.2 --- Comparisons between ICA and PCA through a Simple Example --- p.24 / Chapter 2.6 --- Applications of ICA in Finance: A review --- p.27 / Chapter 2.6.1 --- Relationships between Cocktail-Party Problem and Fi- nance --- p.27 / Chapter 2.6.2 --- Security Structures Explorations --- p.28 / Chapter 2.6.3 --- Factors Interpretation and Visual Analysis --- p.29 / Chapter 2.6.4 --- Time Series Prediction by Factors --- p.29 / Chapter 2.7 --- Conclusions --- p.30 / Chapter 3 --- Factor Models in Finance --- p.31 / Chapter 3.1 --- Overview --- p.31 / Chapter 3.2 --- Factor Models and Return Generating Processes --- p.32 / Chapter 3.2.1 --- One-Factor Model --- p.33 / Chapter 3.2.2 --- Multiple-Factor Model --- p.34 / Chapter 3.3 --- Abstraction of Factor Models in Portfolio --- p.35 / Chapter 3.4 --- Typical Applications of Factor Models: Portfolio Mangement --- p.37 / Chapter 3.5 --- Different Approaches to Estimate Factor Model --- p.39 / Chapter 3.5.1 --- Time-Series Approach --- p.39 / Chapter 3.5.2 --- Cross-Section Approach --- p.40 / Chapter 3.5.3 --- Factor-Analytic Approach --- p.41 / Chapter 3.6 --- Conclusions --- p.42 / Chapter 4 --- ICA and Factor Models --- p.43 / Chapter 4.1 --- Overview --- p.43 / Chapter 4.2 --- Relationships between BSS and Factor Models --- p.43 / Chapter 4.2.1 --- Mathematical Deviation from Factor Models to Mixing Process --- p.45 / Chapter 4.3 --- Procedures of Factor Model Constructions by ICA --- p.47 / Chapter 4.4 --- Sorting Criteria for Factors --- p.48 / Chapter 4.4.1 --- Kurtosis --- p.50 / Chapter 4.4.2 --- Number of Runs --- p.52 / Chapter 4.5 --- Experiments and Results I: Factor Model Constructions --- p.53 / Chapter 4.5.1 --- Factors and their Sensitivities Extracted by ICA --- p.55 / Chapter 4.5.2 --- Factor Model Construction for a Stock --- p.60 / Chapter 4.6 --- Discussion --- p.62 / Chapter 4.6.1 --- Remarks on Applying ICA to Find Factors --- p.62 / Chapter 4.6.2 --- Independent Factors and Sparse Coding --- p.63 / Chapter 4.6.3 --- Selecting Securities for ICA --- p.63 / Chapter 4.6.4 --- Factors in Factor Models --- p.65 / Chapter 4.7 --- Conclusions --- p.66 / Chapter 5 --- Factor Model Evaluations and Selections --- p.67 / Chapter 5.1 --- Overview --- p.67 / Chapter 5.2 --- Random Residue: Requirement of Independent Factor Model --- p.68 / Chapter 5.2.1 --- Runs Test --- p.68 / Chapter 5.2.2 --- Interpretation of z-value --- p.70 / Chapter 5.3 --- Experiments and Results II: Factor Model Selections --- p.71 / Chapter 5.3.1 --- Randomness of Residues using Different Sorting Criteria --- p.71 / Chapter 5.3.2 --- Reverse Sortings of Kurtosis and Number of Runs --- p.76 / Chapter 5.4 --- Experiments and Results using FastICA --- p.80 / Chapter 5.5 --- Other Evaluation Criteria for Independent Factor Models --- p.85 / Chapter 5.5.1 --- Reconstruction Error --- p.86 / Chapter 5.5.2 --- Minimum Description Length --- p.89 / Chapter 5.6 --- Conclusions --- p.92 / Chapter 6 --- New Applications of Independent Factor Models --- p.93 / Chapter 6.1 --- Overview --- p.93 / Chapter 6.2 --- Applications to Financial Trading System --- p.93 / Chapter 6.2.1 --- Modifying Shocks in Stocks --- p.96 / Chapter 6.2.2 --- Modifying Sensitivity to Residue --- p.100 / Chapter 6.3 --- Maximization of Higher Moment Utility Function --- p.104 / Chapter 6.3.1 --- No Good Approximation to Utility Function --- p.107 / Chapter 6.3.2 --- Uncorrelated and Independent Factors in Utility Ma mizationxi- --- p.108 / Chapter 6.4 --- Conclusions --- p.110 / Chapter 7 --- Future Works --- p.111 / Chapter 8 --- Conclusion --- p.113 / Chapter A --- Stocks used in experiments --- p.116 / Chapter B --- Proof for independent factors outperform dependent factors in prediction --- p.117 / Chapter C --- Demixing Matrix and Mixing Matrix Found by JADE --- p.119 / Chapter D --- Moments and Cumulants --- p.120 / Chapter D.1 --- Moments --- p.120 / Chapter D.2 --- Cumulants --- p.121 / Chapter D.3 --- Cross-Cumulants --- p.121 / Bibliography --- p.123
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_323407 |
| Date | January 2001 |
| Contributors | Cha, Siu-ming., Chinese University of Hong Kong Graduate School. Division of Computer Science and Engineering. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography |
| Format | print, xiv, 132 leaves : ill. ; 30 cm. |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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