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FTEC

Financial Technology

FTEC 5010
Mathematics for Financial Technology
[3-0-0:3]
Description
This course teaches mathematical and quantitative skills as a technical preparation for the development of financial technology. The topics covered include multivariate calculus, linear algebra, optimization, numerical computation, probability, statistics and other topics, with applications to finance.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Apply theoretical knowledge, principles and techniques to topics from linear algebra, calculus and some other math foundation courses.
- 2.
  Acquire a good appreciation of the roles of mathematics in recent developments in technology.
- 3.
  Apply computer algorithms to real-life problems.
- 4.
  Use a computer for solving some basic problems from linear algebra.
FTEC 5020
Computer Programming
[3-0-2:3]
Description
This course covers programming skills and data structures essential for data manipulation. The topics include basic programming concepts (such as variables and control statements), data structures (such as list, queue, stack), and algorithms (such as recursion, sorting and searching).
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Apply theoretical knowledge, principles and techniques to topics from linear algebra, calculus and some other math foundation courses.
- 2.
  Acquire a good appreciation of the roles of mathematics in recent developments in technology.
- 3.
  Apply computer algorithms to real-life problems.
- 4.
  Use a computer for solving some basic problems from linear algebra.
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 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 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 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
[1-0-0:1]
Description
Advanced seminar series presented by postgraduate students, faculty, and guest speakers on selected topics in Financial Technology. This course is offered once a year. 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 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.
  Ask intellectual questions and communicate effectively in academic and professional settings.
- 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.

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