FTEC
Financial Technology
• FTEC 5030
Statistical Methods for Financial Technology
[3-0-0:3]
Description
This course will survey modern financial technology, through the lens of statistics, which is the science of the analysis of data. Students will learn how statistical methodology, in conjunction with advances in technology, is used to efficiently acquire, utilize and interpret data, as it relates to innovations in the financial services sector. This course will develop skillsets for Big Data analytics and Predictive modelling, for better understanding of the financial markets.
Intended Learning Outcomes

On successful completion of the course, students will be able to:

• 1.
Apply statistical methods to analyze ﬁnancial data.
• 2.
Utilize appropriate analytical and quantitative techniques to critically compare and evaluate different models.
• 3.
Use R programming language effectively in ﬁnancial applications.
• FTEC 5031
[3-0-0:3]
Description
The course will give students a deeper understanding of the foundations of probability theory, such as probability theory from a measure-theoretic perspective, convergences of distributions and probability measures, and conditional expectations. During the course, important theorems, such as Radon-Nikodym theorem, Fubini theorem, and general central limit theorems, will be investigated.
Intended Learning Outcomes

On successful completion of the course, students will be able to:

• 1.
Derive advanced probability-theoretic results of importance for statistical inference.
• 2.
Independently prove important theorems in probability theory.
• 3.
Independently solve advanced problems in probability theory.
• 4.
Perform mathematically stringent probability-theoretic reasoning.
• 5.
Critically apply central results in probability theory on typical problems related to FinTech.
• FTEC 5032
Optimization Theory
[3-0-0:3]
Description
The objective of this course is to provide students with optimization theory and concepts. Main topics cover linear optimization, simplex method, duality theory, convex analysis, and dynamic programming. The emphasis will be on methodology, modelling techniques and mathematical insights.
Intended Learning Outcomes

On successful completion of the course, students will be able to:

• 1.
Gain an overall knowledge of what optimization is and some theoretical understanding of some optimization concepts and methodologies, such as convexity, weak and strong duality, Karush–Kuhn–Tucker (KKT) conditions, Bellman’s equation, etc..
• 2.
Identify and analyze real-life problems with optimization insights.
• 3.
Construct mathematical models of standard optimization problems.
• 4.
Apply basic theories and principles for solving optimization problems or determining the optimality of a given solution.
• 5.
Understand and conduct scientific research on many important decision problems in finance, economy, and operations quantitatively.
• FTEC 5040
Financial Technology Research
[3-0-0:3]
Description
The objective of this course is to provide students with an extensive exposure to important research in financial technology and a rigorous training in related research methodologies. Main topics include cryptocurrencies, blockchain, P2P lending, crowdfunding, robo-advisors, regulatory technology (RegTech), and insurance technology (InsurTech). This course also enables students to gain an appreciation for how research in financial technologies improves traditional financial services and overcomes various difficulties inherent in the current financial system.
Intended Learning Outcomes

On successful completion of the course, students will be able to:

• 1.
Identify and analyze deficiencies and challenges of traditional financial services in financial industry.
• 2.
Conduct scientific research on financial technologies quantitatively.
• 3.
Analyze the financial or economic principles of important financial technologies.
• 4.
Apply the quantitative methodologies involved in important financial technologies to analyze real problems.
• 5.
Utilize in-depth financial technology research to improve traditional financial services.
• 6.
Develop financial technologies to overcome difficulties arising in financial industry.
• FTEC 5050
Machine Learning and Artificial Intelligence
[3-0-0:3]
Description
This course covers the fundamentals of machine learning and artificial intelligence, and their applications in computer vision, image processing, natural language processing, and robotics. The topics include major learning paradigms (supervised learning, unsupervised learning and reinforcement learning), learning models (such as neural networks, Bayesian classification, clustering, kernels, feature extraction), and other problem solving techniques (such as heuristic search, constraint satisfaction solvers and knowledge-based systems) in AI.
Intended Learning Outcomes

On successful completion of the course, students will be able to:

• 1.
Examine and formulate problems as AI heuristic search problem.
• 2.
Model and design autonomous agents.
• 3.
Examine and formulate multiagent problems as games.
• 4.
Examine and formulate problems as machine learning problem.
• 5.
Acquire working knowledge in machine learning in vision, image processing, and natural language understanding.
• FTEC 5060
Stochastic Processes
[3-0-0:3]
Description
The objective of this course is to provide students with fundamentals of stochastic processes. Main topics cover Poisson processes, renewal theory, discrete-time Markov chains, continuous-time Markov chains, and martingales. The emphasis will be on methodologies, fundamental concepts, and mathematical insights.
Intended Learning Outcomes

On successful completion of the course, students will be able to:

• 1.
Gain an overall knowledge of what optimization is and some theoretical understanding of some concepts and techniques about stochastic processes, such as Poisson processes, renew theory, discrete-time Markov chains, continuous-time Markov chains et al.
• 2.
Identify and analyze real-life problems under uncertainty with mathematical insights.
• 3.
Construct mathematical business models for solving problems under uncertainty.
• 4.
Apply basic theory and principles for analyzing and solving problems under uncertainty.
• 5.
Understand and conduct scientific research on many important decision problems in finance, economy, and operations quantitatively.
• FTEC 5100
Research in Corporate Finance
[3-0-0:3]
Description
This course introduces the main issues in corporate finance, identifies principal theoretical tools and empirical approaches, and fosters thinking about current research questions. The theoretical part includes classic theories such as Modigliani‐Miller theorem, Coase theorem, and Fisher separation theorem, with a focus on financing decisions of firms, corporate governance, and their implications. The empirical part reviews econometric methods commonly used in corporate finance research and covers selected topics.
Intended Learning Outcomes

On successful completion of the course, students will be able to:

• 1.
Discuss the nature of the corporate finance issues.
• 2.
Formulate problems related to corporate finance.
• 3.
Solve problems analytically.
• 4.
Handle data work with computer software.
• 5.
Write reports on theoretical and empirical findings.
• FTEC 5101
Microeconomic Theory
[3-0-0:3]
Description
This is a course in graduate level microeconomic theory for PhD students in financial technology and other related fields. This course covers topics including consumer theory, producer theory, uncertainty, general equilibrium,and matching. The required background knowledge for the course are intermediate microeconomic theory and mathematics through calculus of several variables and introductory real analysis. Additional mathematical tools will be explained briefly as the course proceeds. This course serves as the first rigorous training in economics and finance and helps lay down a solid foundation in economic modelling for future research.
Intended Learning Outcomes

On successful completion of the course, students will be able to:

• 1.
Understand rigorous economic modelling.
• 2.
Read and evaluate research papers independently.
• 3.
Use economics to analyze important real-world phenomenon.
• 4.
Write rigorous models to express the intuition of an insight.
• 5.
Develop new techniques to existing research.
• FTEC 5110
Research in Asset Pricing
[3-0-0:3]
Description
This course addresses issues in both theoretical development and empirical studies of asset pricing. The theoretical part covers portfolio theory, arbitrage pricing theory with large numbers of assets, the intertemporal asset pricing model and the production-based asset pricing model. Topics related to derivative pricing are also covered. The empirical part covers asset return predictability, volatility-return relationship, asset pricing testing methodology, popular factor models used by practitioners and empirical findings in derivative markets.
Intended Learning Outcomes

On successful completion of the course, students will be able to:

• 1.
Discuss the nature of the asset pricing issues.
• 2.
Formulate problems related asset pricing.
• 3.
Solve problems analytically.
• 4.
Handle data work with computer software.
• 5.
Write reports on theoretical and empirical findings.
• FTEC 6000
FinTech Attachment
[2-4 credits]
Description
This course provides an opportunity for students to develop and apply FinTech research in an industrial organization. Students will work in a designated organization conducting FinTech research-related work under the supervision of their supervisors. Graded P or F.
Intended Learning Outcomes

On successful completion of the course, students will be able to:

• 1.
Identify important FinTech research and latest advances in the industry.
• 2.
Compare and contrast the FinTech projects undertaken by the industry and the academia.
• 3.
Apply the FinTech research knowledge and capability to address a real-life problem.
• 4.
Develop professional analysis, presentation, and report writing skills.
• FTEC 6101
FinTech Program Seminar
[0-1-0:0]
Description
Advanced seminar series presented by guest speakers and faculty members on selected topics in Financial Technology. This course is offered every regular term. Graded P or F.
Intended Learning Outcomes

On successful completion of the course, students will be able to:

• 1.
Apply essential skills in communicating research results.
• 2.
Prepare professional presentation with the necessary presentation aids.
• 3.
Ask intellectual questions in professional settings.
• 4.
Interact with audience in research seminars effectively.
• FTEC 6900
Independent Study
[1-3 credit(s)]
Description
Independent study in a designated subject under direct guidance of a faculty member to provide students the advanced knowledge and research skill sets on a topic of Financial Technology. Required readings, tutorial discussions, and submission of report(s) will be used for assessment. The course may be repeated for credit if different topics are studied. Graded P or F.
Intended Learning Outcomes

On successful completion of the course, students will be able to:

• 1.
Have in-depth knowledge of financial technology, leading to independent substantive scholarly research.
• 2.
Apply an interdisciplinary approach in examining the selected topics.
• 3.
Critically evaluate different aspects of the selected topics.
• 4.
Communicate findings effectively in written reports.
• FTEC 6910
Special Topics in Fintech
[1-3 credit(s)]
Description
This course examines areas of current interest and special topics in financial technology. It employs a mix of lectures, case studies, and projects. Topics may vary from year to year and will be announced at the beginning of each term. This course may be graded by letter or P/F for different offerings.
Intended Learning Outcomes

On successful completion of the course, students will be able to:

• 1.
Express in-depth understanding of key Fintech concepts and their applications in financial markets.
• 2.
Demonstrate necessary skills to analyze the issue and make contribution to the discussion.
• 3.
• 4.
Relate the concepts and methodologies in the course topic to practical implementation in the financial technology industries.
• FTEC 6990
MPhil Thesis Research
Description
Master's thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.
Intended Learning Outcomes

On successful completion of the course, students will be able to:

• 1.
Design, develop and conduct cross-disciplinary research in Financial Technology.
• 2.
Communicate research findings effectively in written and oral presentations.
• FTEC 7990
Doctoral Thesis Research
Description
Original and independent doctoral thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.
Intended Learning Outcomes

On successful completion of the course, students will be able to:

• 1.
Design, develop and conduct cross-disciplinary research in Financial Technology.
• 2.
Communicate research findings effectively in written and oral presentations.