by Wai Chi-kin. / Abstract also in Chinese / Bibliography: leaves 174-175 / Thesis (M.B.A.)--Chinese University of Hong Kong, 1982

Ng Anny. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 139-142). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Problem Definition --- p.2 / Chapter 1.2 --- Thesis Organization --- p.3 / Chapter 2 --- Chinese Text Summarization Using Genetic Algorithm --- p.4 / Chapter 2.1 --- Introduction --- p.4 / Chapter 2.2 --- Related Work --- p.6 / Chapter 2.3 --- Genetic Algorithm Approach --- p.10 / Chapter 2.3.1 --- Fitness Function --- p.11 / Chapter 2.3.2 --- Genetic operators --- p.14 / Chapter 2.4 --- Implementation Details --- p.15 / Chapter 2.5 --- Experimental results --- p.19 / Chapter 2.6 --- Limitations and Future Work --- p.24 / Chapter 2.7 --- Conclusion --- p.26 / Chapter 3 --- Event Extraction from Chinese Financial News --- p.27 / Chapter 3.1 --- Introduction --- p.28 / Chapter 3.2 --- Method --- p.29 / Chapter 3.2.1 --- Data Set Preparation --- p.29 / Chapter 3.2.2 --- Positive Word --- p.30 / Chapter 3.2.3 --- Negative Word --- p.31 / Chapter 3.2.4 --- Window --- p.31 / Chapter 3.2.5 --- Event Extraction --- p.32 / Chapter 3.3 --- System Overview --- p.33 / Chapter 3.4 --- Implementation --- p.33 / Chapter 3.4.1 --- Event Type and Positive Word --- p.34 / Chapter 3.4.2 --- Company Name --- p.34 / Chapter 3.4.3 --- Negative Word --- p.36 / Chapter 3.4.4 --- Event Extraction --- p.37 / Chapter 3.5 --- Stock Database --- p.38 / Chapter 3.5.1 --- Stock Movements --- p.39 / Chapter 3.5.2 --- Implementation --- p.39 / Chapter 3.5.3 --- Stock Database Transformation --- p.39 / Chapter 3.6 --- Performance Evaluation --- p.40 / Chapter 3.6.1 --- Performance measures --- p.40 / Chapter 3.6.2 --- Evaluation --- p.41 / Chapter 3.7 --- Conclusion --- p.45 / Chapter 4 --- Mining Frequent Episodes --- p.46 / Chapter 4.1 --- Introduction --- p.46 / Chapter 4.1.1 --- Definitions --- p.48 / Chapter 4.2 --- Related Work --- p.50 / Chapter 4.3 --- Double-Part Event Tree for the database --- p.56 / Chapter 4.3.1 --- Complexity of tree construction --- p.62 / Chapter 4.4 --- Mining Frequent Episodes with the DE-tree --- p.63 / Chapter 4.4.1 --- Conditional Event Trees --- p.66 / Chapter 4.4.2 --- Single Path Conditional Event Tree --- p.67 / Chapter 4.4.3 --- Complexity of Mining Frequent Episodes with DE-Tree --- p.67 / Chapter 4.4.4 --- An Example --- p.68 / Chapter 4.4.5 --- Completeness of finding frequent episodes --- p.71 / Chapter 4.5 --- Implementation of DE-Tree --- p.71 / Chapter 4.6 --- Method 2: Node-List Event Tree --- p.76 / Chapter 4.6.1 --- Tree construction --- p.79 / Chapter 4.6.2 --- Order of Position Bits --- p.83 / Chapter 4.7 --- Implementation of NE-tree construction --- p.84 / Chapter 4.7.1 --- Complexity of NE-Tree Construction --- p.86 / Chapter 4.8 --- Mining Frequent Episodes with NE-tree --- p.87 / Chapter 4.8.1 --- Conditional NE-Tree --- p.87 / Chapter 4.8.2 --- Single Path Conditional NE-Tree --- p.88 / Chapter 4.8.3 --- Complexity of Mining Frequent Episodes with NE-Tree --- p.89 / Chapter 4.8.4 --- An Example --- p.89 / Chapter 4.9 --- Performance evaluation --- p.91 / Chapter 4.9.1 --- Synthetic data --- p.91 / Chapter 4.9.2 --- Real data --- p.99 / Chapter 4.10 --- Conclusion --- p.103 / Chapter 5 --- Mining N-most Interesting Episodes --- p.104 / Chapter 5.1 --- Introduction --- p.105 / Chapter 5.2 --- Method --- p.106 / Chapter 5.2.1 --- Threshold Improvement --- p.108 / Chapter 5.2.2 --- Pseudocode --- p.112 / Chapter 5.3 --- Experimental Results --- p.112 / Chapter 5.3.1 --- Synthetic Data --- p.113 / Chapter 5.3.2 --- Real Data --- p.119 / Chapter 5.4 --- Conclusion --- p.121 / Chapter 6 --- Mining Frequent Episodes with Event Constraints --- p.122 / Chapter 6.1 --- Introduction --- p.122 / Chapter 6.2 --- Method --- p.123 / Chapter 6.3 --- Experimental Results --- p.125 / Chapter 6.3.1 --- Synthetic Data --- p.126 / Chapter 6.3.2 --- Real Data --- p.129 / Chapter 6.4 --- Conclusion --- p.131 / Chapter 7 --- Conclusion --- p.133 / Chapter A --- Test Cases --- p.135 / Chapter A.1 --- Text 1 --- p.135 / Chapter A.2 --- Text 2 --- p.137 / Bibliography --- p.139

This dissertation aims to investigate the asset pricing implications of the stock option's implied volatility term structure. We mainly focus on two directions: the volatility term structure of the market and the volatility term structure of individual stocks. The market volatility term structure, which is calculated from prices of index options with different expirations, reflects the market's expectation of future volatility of different horizons. So the market volatility term structure incorporates information that is not captured by the market volatility itself. In particular, the slope of the volatility term structure captures the expected volatility trend. In the first part of the thesis, we investigate whether the market volatility term structure slope is a priced source of risk or not. We find that stocks with high sensitivities to the proxies of the VIX term structure slope exhibit high returns on average. We further estimate the premium for bearing the VIX slope risk to be approximately 2.5% annually and statistically significant. The effect cannot be explained by other common risk factors, such as the market excess return, size, book-to-market, momentum, liquidity and market volatility. We extensively investigate the robustness of our empirical results and find that the effect of the VIX term structure risk is robust. Within the context of ICAPM, the positive price of VIX term structure risk indicates that it is a state variable which positively affects the future investment opportunity set. In the second part of the thesis, we provide a stylized model that explains our empirical results. We build a regime-switching rare disaster model that allows disasters to have short and long durations. Our model indicates that a downward sloping VIX term structure corresponds to a potential long disaster and an upward sloping VIX term structure corresponds to a potential short disaster. It further implies that stocks with high sensitivities to the VIX slope have high loadings on the disaster duration risk, thus earn higher risk premium. These implications are consistent with our empirical results. In the last part, we study the relationship between individual stock's volatility term structure and the stock's future return. We use a measure of stock's implied volatility term structure slope, defined as the difference between 3-month and 1-month implied volatility from at-the-money options, to demonstrate that option prices contain important information for the underlying equities. We show that option volatility term structure slopes are significant in explaining future equity returns in the cross-section. And we further find evidence that the implied volatility term structure is a measure of event risk: firms with the most negative volatility term structure are those for which the market anticipates news that may affect stock price within one month. Relevant events include, but are not limited to, earnings announcements.

Tang Him John. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 76-80). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgment --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.2 / Chapter 1.1.1 --- Linear Time Series --- p.2 / Chapter 1.1.2 --- Mixture Models --- p.3 / Chapter 1.1.3 --- EM algorithm --- p.6 / Chapter 1.1.4 --- Model Selection --- p.6 / Chapter 1.2 --- Main Objectives --- p.7 / Chapter 1.3 --- Outline of this thesis --- p.7 / Chapter 2 --- Finite Gaussian Mixture ARMA-GARCH Model --- p.9 / Chapter 2.1 --- Introduction --- p.9 / Chapter 2.1.1 --- "AR, MA, and ARMA" --- p.10 / Chapter 2.1.2 --- Stationarity --- p.11 / Chapter 2.1.3 --- ARCH and GARCH --- p.12 / Chapter 2.1.4 --- Gaussian mixture --- p.13 / Chapter 2.1.5 --- EM and GEM algorithms --- p.14 / Chapter 2.2 --- Finite Gaussian Mixture ARMA-GARCH Model --- p.16 / Chapter 2.3 --- Estimation of Gaussian mixture ARMA-GARCH model --- p.17 / Chapter 2.3.1 --- Autocorrelation and Stationarity --- p.20 / Chapter 2.3.2 --- Model Selection --- p.24 / Chapter 2.4 --- Experiments: First Step Prediction --- p.26 / Chapter 2.5 --- Chapter Summary --- p.28 / Chapter 2.6 --- Notations and Terminologies --- p.30 / Chapter 2.6.1 --- White Noise Time Series --- p.30 / Chapter 2.6.2 --- Lag Operator --- p.30 / Chapter 2.6.3 --- Covariance Stationarity --- p.31 / Chapter 2.6.4 --- Wold's Theorem --- p.31 / Chapter 2.6.5 --- Multivariate Gaussian Density function --- p.32 / Chapter 3 --- Finite Mixture-of-Expert ARMA-GARCH Model --- p.33 / Chapter 3.1 --- Introduction --- p.33 / Chapter 3.1.1 --- Mixture-of-Expert --- p.34 / Chapter 3.1.2 --- Alternative Mixture-of-Expert --- p.35 / Chapter 3.2 --- ARMA-GARCH Finite Mixture-of-Expert Model --- p.36 / Chapter 3.3 --- Estimation of Mixture-of-Expert ARMA-GARCH Model --- p.37 / Chapter 3.3.1 --- Model Selection --- p.38 / Chapter 3.4 --- Experiments: First Step Prediction --- p.41 / Chapter 3.5 --- Second Step and Third Step Prediction --- p.44 / Chapter 3.5.1 --- Calculating Second Step Prediction --- p.44 / Chapter 3.5.2 --- Calculating Third Step Prediction --- p.45 / Chapter 3.5.3 --- Experiments: Second Step and Third Step Prediction . --- p.46 / Chapter 3.6 --- Comparison with Other Models --- p.50 / Chapter 3.7 --- Chapter Summary --- p.57 / Chapter 4 --- Stable Estimation Algorithms --- p.58 / Chapter 4.1 --- Stable AR(1) estimation algorithm --- p.59 / Chapter 4.2 --- Stable AR(2) Estimation Algorithm --- p.60 / Chapter 4.2.1 --- Real p1 and p2 --- p.61 / Chapter 4.2.2 --- Complex p1 and p2 --- p.61 / Chapter 4.2.3 --- Experiments for AR(2) --- p.63 / Chapter 4.3 --- Experiment with Real Data --- p.64 / Chapter 4.4 --- Chapter Summary --- p.65 / Chapter 5 --- Conclusion --- p.66 / Chapter 5.1 --- Further Research --- p.69 / Chapter A --- Equation Derivation --- p.70 / Chapter A.1 --- First Derivatives for Gaussian Mixture ARMA-GARCH Esti- mation --- p.70 / Chapter A.2 --- First Derivatives for Mixture-of-Expert ARMA-GARCH Esti- mation --- p.71 / Chapter A.3 --- First Derivatives for BYY Harmony Function --- p.72 / Chapter A.4 --- First Derivatives for stable estimation algorithms --- p.73 / Chapter A.4.1 --- AR(1) --- p.74 / Chapter A.4.2 --- AR(2) --- p.74 / Bibliography --- p.80

Fung Pui Cheong Gabriel. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 88-93). / Abstracts in English and Chinese. / Abstract (English) --- p.i / Abstract (Chinese) --- p.ii / Acknowledgement --- p.iii / Contents --- p.v / List of Figures --- p.ix / List of Tables --- p.x / Chapter Part I --- The Very Beginning --- p.1 / Chapter 1 --- Introduction --- p.2 / Chapter 1.1 --- Contributions --- p.3 / Chapter 1.2 --- Dissertation Organization --- p.4 / Chapter 2 --- Problem Formulation --- p.6 / Chapter 2.1 --- Defining the Prediction Task --- p.6 / Chapter 2.2 --- Overview of the System Architecture --- p.8 / Chapter Part II --- Literatures Review --- p.11 / Chapter 3 --- The Social Dynamics of Financial Markets --- p.12 / Chapter 3.1 --- The Collective Behavior of Groups --- p.13 / Chapter 3.2 --- Prediction Based on Publicity Information --- p.16 / Chapter 4 --- Time Series Representation --- p.20 / Chapter 4.1 --- Technical Analysis --- p.20 / Chapter 4.2 --- Piecewise Linear Approximation --- p.23 / Chapter 5 --- Text Classification --- p.27 / Chapter 5.1 --- Document Representation --- p.28 / Chapter 5.2 --- Document Pre-processing --- p.30 / Chapter 5.3 --- Classifier Construction --- p.31 / Chapter 5.3.1 --- Naive Bayes (NB) --- p.31 / Chapter 5.3.2 --- Support Vectors Machine (SVM) --- p.33 / Chapter Part III --- Mining Financial Time Series and Textual Doc- uments Concurrently --- p.36 / Chapter 6 --- Time Series Representation --- p.37 / Chapter 6.1 --- Discovering Trends on the Time Series --- p.37 / Chapter 6.2 --- t-test Based Split and Merge Segmentation Algorithm ´ؤ Splitting Phrase --- p.39 / Chapter 6.3 --- t-test Based Split and Merge Segmentation Algorithm - Merging Phrase --- p.41 / Chapter 7 --- Article Alignment and Pre-processing --- p.43 / Chapter 7.1 --- Aligning News Articles to the Stock Trends --- p.44 / Chapter 7.2 --- Selecting Positive Training Examples --- p.46 / Chapter 7.3 --- Selecting Negative Training Examples --- p.48 / Chapter 8 --- System Learning --- p.52 / Chapter 8.1 --- Similarity Based Classification Approach --- p.53 / Chapter 8.2 --- Category Sketch Generation --- p.55 / Chapter 8.2.1 --- Within-Category Coefficient --- p.55 / Chapter 8.2.2 --- Cross-Category Coefficient --- p.56 / Chapter 8.2.3 --- Average-Importance Coefficient --- p.57 / Chapter 8.3 --- Document Sketch Generation --- p.58 / Chapter 9 --- System Operation --- p.60 / Chapter 9.1 --- System Operation --- p.60 / Chapter Part IV --- Results and Discussions --- p.62 / Chapter 10 --- Evaluations --- p.63 / Chapter 10.1 --- Time Series Evaluations --- p.64 / Chapter 10.2 --- Classifier Evaluations --- p.64 / Chapter 10.2.1 --- Batch Classification Evaluation --- p.69 / Chapter 10.2.2 --- Online Classification Evaluation --- p.71 / Chapter 10.2.3 --- Components Analysis --- p.74 / Chapter 10.2.4 --- Document Sketch Analysis --- p.75 / Chapter 10.3 --- Prediction Evaluations --- p.75 / Chapter 10.3.1 --- Simulation Results --- p.77 / Chapter 10.3.2 --- Hit Rate Analysis --- p.78 / Chapter Part V --- The Final Words --- p.80 / Chapter 11 --- Conclusion and Future Work --- p.81 / Appendix --- p.84 / Chapter A --- Hong Kong Stocks Categorization Powered by Reuters --- p.84 / Chapter B --- Morgan Stanley Capital International (MSCI) Classification --- p.85 / Chapter C --- "Precision, Recall and F1 measure" --- p.86 / Bibliography --- p.88

In this study, a Prediction Accuracy Based Hill Climbing Feature Selection Algorithm (AHCFS) is created and compared with an Error Rate Based Sequential Feature Selection Algorithm (ERFS) which is an existing Matlab algorithm. The goal of the study is to create a new piece of an algorithm that has potential to outperform the existing Matlab sequential feature selection algorithm in predicting the movement of S&P 500 (^GSPC) prices under certain circumstances. The two algorithms are tested based on historical data of ^GSPC, and Support Vector Machine (SVM) is employed by both as the classifier. A prediction without feature selection algorithm implemented is carried out and used as a baseline for comparison between the two algorithms. The prediction horizon set in this study for both algorithms varies from one to 60 days. The study results show that AHCFS reaches higher prediction accuracy than ERFS in the majority of the cases.

This paper develops a framework that analyzes how mergers and acquisitions in Alberta’s oil and gas industry affect stock prices. In this experiment, a multivariate regression is applied to several industry-specific variables to determine if they have impacts on the abnormal stock returns of acquirers. The results show that abnormal returns 5 days prior to the public announcement of the transaction are, in fact, driven by several industry-specific variables. However, the returns immediately after the M & A announcements are similar to previous research done in other industries. Acquirers’ gains 2 days after the announcement are essentially unaffected by the transaction. After a 90-day period, the share performances of acquiring firms tend to beat the index by 7% on average, but this is not thoroughly explained by the variables in the regression analysis.

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 192-198). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 192-198). Also available on the Internet.

published_or_final_version / Statistics / Master / Master of Philosophy