published_or_final_version / Business Administration / Master / Master of Business Administration

published_or_final_version / abstract / Electrical and Electronic Engineering / Master / Master of Philosophy

published_or_final_version / Mathematics / Master / Master of Philosophy

The problem of selecting a multicriterion decision making (MCDM) technique for watershed resources management is investigated. Of explicit concern in this research is the matching of a watersned resources management problem with an appropriate MCDM technique. More than seventy techniques are recognized while reviewing the area of MCDM. A new classification scheme is developed to categorize these techniques into four groups on the bases of each algorithm's structural formulation and the possible results obtained by using the algorithm. Other standard classification schemes are also discussed to better understand the differences and similarities among the techniques and thereby demonstrate the importance of matching a particular multicriterion decision problem with an appropriate MCDM technique. The desire for selecting the most appropriate MCDM technique for watershed resources management lead to the development of 49 technique choice criteria and an algorithm for selecting a technique. The algorithm divides the technique choice criteria into four groups: (1) DM/analyst-related criteria, (2) technique-related criteria, (3) problem-related criteria and (4) solution-related criteria. To analyze the applicability of MCDM techniques to a particular problem, the levels of performance of the techniques in solving the problem are, at first, evaluated with respect to the choice criteria in each criterion group resulting in four sets of preference rankings. These four sets are then linearly combined using a set of trade-off parameters to determine the overall preference ranking of the techniques. The MUM technique selection process is itself modeled as a multiobjective problem. In this research, for example, a set of 15 techniques, the author is familiar with, are analyzed for their appropriateness to solve a watershed resources management problem. The performance levels of the 15 MCDM techniques in solving such a problem are evaluated with respect to a selected set of technique choice criteria in each criterion group leading to a set of four evaluation matrices of choice criteria versus alternative techniques. This technique choice problem is then analyzed using a two-stage evaluation procedure known as composite programming. The final product of the process resulted in a preference ranking of the alternative MCDM techniques.

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D.Ing. / The task of managing an investment portfolio is one that continues to challenge both professionals and private individuals on a daily basis. Contrary to popular belief, the desire of these actors is not in all (or even most) instances to generate the highest profits imaginable, but rather to achieve an acceptable return for a given level of risk. In other words, the investor desires to have his funds generate money for him, while not feeling that he is gambling away his (or his clients’) funds. The reasons for a given risk tolerance (or risk appetite) are as varied as the clients themselves – in some instances, clients will simply have their own arbitrary risk appetites, while other may need to maintain certain values to satisfy their mandates, while other may need to meet regulatory requirements. In order to accomplish this task, many measures and representations of performance data are employed to both communicate and understand the risk-reward trade-offs involved in the investment process. In light of the recent economic crisis, greater understanding and control of investment is being clamoured for around the globe, along with the concomitant finger-pointing and blame-assignation that inevitably follows such turmoil, and such heavy costs. The reputation of the industry, always dubious in the best of times, has also taken a significant knock after the events, and while this author would not like to point fingers, clearly the managers of funds, custodians of other people’s money, are in no small measure responsible for the loss of the funds under their care. It is with these concerns in mind that this thesis explores the potential for utilising the powerful tools found within the disciplines of artificial intelligence and machine learning in order to aid fund managers in the balancing of portfolios, tailoring specifically to their clients’ individual needs. These fields hold particular promise due to their focus on generalised pattern recognition, multivariable optimisation and continuous learning. With these tools in hand, a fund manager is able to continuously rebalance a portfolio for a client, given the client’s specific needs, and achieve optimal results while staying within the client’s risk parameters (in other words, keeping within the clients comfort zone in terms of price / value fluctuations).This thesis will first explore the drivers and constraints behind the investment process, as well as the process undertaken by the fund manager as recommended by the CFA (Certified Financial Analyst) Institute. The thesis will then elaborate on the existing theory behind modern investment theory, and the mathematics and statistics that underlie the process. Some common tools from the field of Technical Analysis will be examined, and their implicit assumptions and limitations will be shown, both for understanding and to show how they can still be utilised once their limitations are explicitly known. Thereafter the thesis will show the various tools from within the fields of machine learning and artificial intelligence that form the heart of the thesis herein. A highlight will be placed on data structuring, and the inherent dangers to be aware of when structuring data representations for computational use. The thesis will then illustrate how to create an optimiser using a genetic algorithm for the purpose of balancing a portfolio. Lastly, it will be shown how to create a learning system that continues to update its own understanding, and create a hybrid learning optimiser to enable fund managers to do their job effectively and safely.

A new estimator for calculating the optimal expected return of a self-financing portfolio is proposed, by considering the joint impact of the sample mean vector and the sample covariance matrix. A shrinkage covariance matrix is designed to substitute the sample covariance matrix in the optimization procedure, which leads to an estimate of the optimal expected return smaller than the plug-in estimate. The new estimator is also applicable for both p < n and p ≥ n. Simulation studies are conducted for two empirical data sets. The simulation results show that the new estimator is superior to the previous methods. / By the seminal work of Markowitz in 1952, modern portfolio theory studies how to maximize the portfolio expected return for a given risk, or minimize the risk for a given expected return. Since these two issues are equivalent, this thesis only focuses on the study of the optimal expected return of a self-financing portfolio for a given risk. / Finally, under certain assumptions, we extend our research in the framework of random matrix theory. / The mean-variance portfolio optimization procedure requires two crucial inputs: the theoretical mean vector and the theoretical covariance matrix of the portfolio in one period. Since the traditional plug-in method using the sample mean vector and the sample covariance matrix of the historical data incurs substantial estimation errors, this thesis explores how the sample mean vector and the sample covariance matrix behave in the optimization procedure based on the idea of conditional expectation and finds that the effect of the sample mean vector is an additive process while the effect of the sample covariance matrix is a multiplicative process. / Liu, Yan. / Adviser: Ngai Hang Chan. / Source: Dissertation Abstracts International, Volume: 73-06, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 76-80). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

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

by Wan-Lung Ng. / Thesis submitted in: November 1997. / On added t.p.: January 19, 1998. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 114-119). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Organization Outline --- p.5 / Chapter 2 --- Literature Review --- p.7 / Chapter 2.1 --- Modern Portfolio Theory --- p.7 / Chapter 2.1.1 --- Mean-Variance Model --- p.9 / Chapter 2.1.2 --- Setting-up the relationship between the portfolio and its component securities --- p.11 / Chapter 2.1.3 --- Identifying the efficient frontier --- p.12 / Chapter 2.1.4 --- Selecting the best compromised portfolio --- p.13 / Chapter 2.2 --- Stochastic Optimal Control --- p.17 / Chapter 2.2.1 --- Dynamic Programming --- p.18 / Chapter 2.2.2 --- Dynamic Programming Decomposition --- p.21 / Chapter 3 --- Multiple Period Portfolio Analysis --- p.23 / Chapter 3.1 --- Maximization of Multi-period Consumptions --- p.24 / Chapter 3.2 --- Maximization of Utility of Terminal Wealth --- p.29 / Chapter 3.3 --- Maximization of Expected Average Compounded Return --- p.33 / Chapter 3.4 --- Minimization of Time to Reach Target --- p.35 / Chapter 3.5 --- Goal-Seeking Investment Model --- p.37 / Chapter 4 --- Multi-period Mean-Variance Analysis with a Riskless Asset --- p.40 / Chapter 4.1 --- Motivation --- p.40 / Chapter 4.2 --- Dynamic Mean-Variance Analysis Formulation --- p.43 / Chapter 4.3 --- Auxiliary Problem Formulation --- p.45 / Chapter 4.4 --- Efficient Frontier in Multi-period Portfolio Selection --- p.53 / Chapter 4.5 --- Obseravtions --- p.58 / Chapter 4.6 --- Solution Algorithm for Problem E (w) --- p.62 / Chapter 4.7 --- Illstrative Examples --- p.63 / Chapter 4.8 --- Verification with Single-period Efficient Frontier --- p.72 / Chapter 4.9 --- Generalization to Cases with Nonlinear Utility Function of E (xT) and Var (xT) --- p.75 / Chapter 5 --- Dynamic Portfolio Selection without Risk-less Assets --- p.84 / Chapter 5.1 --- Construction of Auxiliuary Problem --- p.88 / Chapter 5.2 --- Analytical Solution for Efficient Frontier --- p.89 / Chapter 5.3 --- Reduction to Investment Situations with One Risk-free Asset --- p.101 / Chapter 5.4 --- "Multi-period Portfolio Selection via Maximizing Utility function U(E {xT),Var (xT))" --- p.103 / Chapter 6 --- Conclusions and Recommendations --- p.108 / Chapter 6.1 --- Summaries and Achievements --- p.108 / Chapter 6.2 --- Future Studies --- p.110 / Chapter 6.2.1 --- Constrained Investment Situations --- p.110 / Chapter 6.2.2 --- Including Higher Moments --- p.111

有實證研究表明，傳統的信貸風險結構模型顯著低估了違約概率以及信貸收益率差。傳統的結構模型有三個可能的問題：1. 因為正態假設，布朗模型在模擬公司資產價值的過程中未能捕捉到極端事件2. 市場微觀結構噪聲扭曲了股票價格所包含信息3. 在到期日前任何時間，標準BS 期權理論方法不足以描述任何破產的可能性。這些問題在過去的文獻中曾分別提及。而在本文中，在不同的信用風險結構模型的基礎上，我們提出了貝葉斯方法去估算公司價值的跳躍擴散過程和微觀結構噪聲。因為企業的資產淨值不能在市場上觀察，本文建議的貝葉斯方法可對隱藏變量和泊松衝擊作出一定的估算，並就後驗分佈進行財務分析。我們應用馬爾可夫鏈蒙特卡羅方法（MCMC）和吉布斯採樣計算每個參數的後驗分佈。以上的做法，允許我們檢查結構性信用風險模型的偏差主要是來自公司價值的分佈、期權理論方法或市場微觀結構噪聲。我們進行模擬研究以確定模型的表現。最後，我們以新興市場的數據實踐我們的模型。 / There is empirical evidence that structural models of credit risk significantly underestimate both the probability of default and credit yield spreads. There are three potential sources of the problems in traditional structural models. First, the Brownian model driving the firm asset value process may fail to capture extreme events because of the normality assumption. Second, the market micro-structure noise in trading may distort the information contained in equity prices within the estimation process. Third, the standard Black and Scholes option-theoretic approach may be inadequate to describe the consequences of bankruptcy at any time before maturity. These potential problems have been handled separately in the literature. In this paper, we propose a Bayesian approach to simultaneously estimate the jump-diffusion firm value process and micro-structure noise from equity prices based on different structural credit risk models. As the firm asset value is not observable but the equity price is, the proposed Bayesian approach is useful in the estimation with hidden variable and Poisson shocks, and produces posterior distributions for financial analysis. We demonstrate the application using the Markov chain Monte Carlo (MCMC) method to obtain the posterior distributions of parameters and latent variable. The proposed approach enables us to check whether the bias of the structural credit risk model is mainly caused by the firm value distribution, the option-theoretic method or the micro-structure noise of the market. A simulation study is conducted to ascertain the performance of our model. We also apply our model to the emerging market data. / Detailed summary in vernacular field only. / Chan, Sau Lung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 62-65). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese. / List of Tables --- p.vii / List of Figures --- p.viii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background and Intuition --- p.5 / Chapter 2.1 --- Merton Model with Trading Noise --- p.7 / Chapter 2.2 --- Black-Cox Model with Default Barrier --- p.10 / Chapter 2.3 --- Double Exponential Jump Diffusion Model (KJD Model) --- p.11 / Chapter 2.4 --- Equity Value via Laplace Transforms --- p.13 / Chapter 2.5 --- KJD Model with Trading Noises --- p.15 / Chapter 3 --- Bayesian Analysis --- p.17 / Chapter 3.1 --- Gibbs Sampling and Metropolis-Hastings Method --- p.17 / Chapter 3.2 --- Merton Model with Trading Noises (M1) --- p.19 / Chapter 3.2.1 --- Prior Distribution for M1 --- p.19 / Chapter 3.2.2 --- Posterior Distribution for M1 --- p.20 / Chapter 3.3 --- Merton Model with Default Barrier (M2) --- p.22 / Chapter 3.3.1 --- Prior Distribution for M2 --- p.23 / Chapter 3.3.2 --- Posterior Distribution for M2 --- p.23 / Chapter 3.4 --- KJD Model with Trading Noises (M3) --- p.25 / Chapter 3.4.1 --- Prior Distribution for M3 --- p.26 / Chapter 3.4.2 --- Posterior Distribution for M3 --- p.27 / Chapter 3.5 --- KJD Model with Default Barrier (M4) --- p.33 / Chapter 3.5.1 --- Prior Distribution for M4 --- p.34 / Chapter 3.5.2 --- Posterior Distribution for M4 --- p.35 / Chapter 4 --- Numerical Examples --- p.42 / Chapter 4.1 --- Simulation Analysis --- p.42 / Chapter 4.2 --- Empirical Study --- p.46 / Chapter 4.2.1 --- BEA and DBS, 2003-2004 --- p.46 / Chapter 4.2.2 --- HSBC, 2008-2009 --- p.49 / Chapter 5 --- Conclusion --- p.60 / Bibliography --- p.62