Postgraduate Courses

This course covers the basic theory of artificial intelligence and machine learning, and their applications to FinTech. Topics include natural language understanding and sentiment analysis using various deep learning architectures. The course also covers basic natural language processing methods for applications such as event and anomaly detection, fraud and fake news detection. The course will also relate sentiment and affect analysis to stock market trading, market monitoring, and to compliance and regulatory-related adverse events.

This course introduces basic concepts and technologies of blockchain from engineering perspectives, such as Bitcoin architecture, consensus protocol of Bitcoin, proof of work, Ethereum, Hyperledger and smart contracts, as well as the blockchain applications. The course also covers the limitations and possible improvements of the blockchain system.

This course covers the basic and advanced statistical approaches to data analysis and shows how to use these techniques to analyze a financial data with a statistical package, such as Python and R. The key topics are reading and describing data, categorical data, time series data, correlation, nonparametric comparisons, ANOVA, multiple regression, general linear models and quantile regression models.

In this course, students will first learn basic concepts and techniques about data mining, including data preprocessing, data cleaning, clustering, classification and outlier detection. Then, students will learn how to apply these techniques to financial data, such as sentiment analysis and social networking mining.

This course aims to provide a foundational introduction to financial technologies. More specifically, this course will cover various important financial technologies and innovations, including investment and financing technologies such as P2P lending and crowdfunding, payment technologies such as mobile payments, wealth management technologies such as robo-advisors, blockchain technologies such as cryptocurrencies, and other technologies such as InsurTech and RegTech.

This course teaches mathematical and quantitative skills as a technical preparation for development of financial technology. The topics covered in this include multivariate calculus, linear algebra, optimization, numerical computation, elementary number theory for cryptography, probability, statistics and other topics, with applications to finance.

This course aims to provide an introduction to technology and analytics related to alternative finance. More specifically, this course primarily covers various alternative finance models, including P2P consumer and business lending, donation-based, equity-based and reward-based crowdfunding, invoice trading, and pension-led funding, and alternative finance instruments, including debt-based securities, SME mini-bonds, social impact bonds, community shares, and shadow banking system.

This course aims to introduce decision analytics instruments and their applications in FinTech. Main topics covered in this course include basic probability and statistics, predictive analytics, prescriptive analytics such as linear programming integer programming, dynamic programming and sequential decision making, stochastic models, quality control, Monte Carlo simulation, game theory, and their applications in various areas of FinTech.

This course introduces the basic theory of convex optimization and illustrates its practical employment in a wide range of FinTech applications. Techniques and applications of nonconvex optimization are also considered. Examples of the problems considered include Markowitz portfolio optimization and its many variations (e.g., maximum Sharpe ratio portfolio, risk-parity portfolio, robust portfolio, sparse portfolio), data fusion, machine learning for classification/estimation, imputation of missing data, big data analysis, outlier detection, data clustering, and deep learning.

This course provides students with an extensive exposure to the elements of statistical machine learning in supervised and unsupervised learning with real world datasets. Topics include basic models in regression and classification, resampling methods, model selection/assessment, and some standard techniques in unsupervised learning such as clustering and dimensionally reduction.

This course addresses fundamental topics in statistics and their applications to financial models. The statistical methods include descriptive and exploratory statistical analysis, statistical inference, linear and non-linear regression, principal components and factor models. Financial applications include statistical analysis of portfolio theory, CAPM and multifactor pricing models and financial time series analysis.

The course aims to broaden MSc(FinTech) students’ horizon and develop students’ essential knowledge and skills related to financial technology through participating in a series of enrichment activities. Different speakers, practitioners, or FinTech professionals may be invited to conduct the workshops or engagement activities. Students are required to attend at least six enrichment activities recognized by the program in order to pass this course. Graded PP, P or F.

This course aims to expose students to various “FinTech” and “TechFin” management issues in both financial services and technology companies from 2016 to present. Whether it is about resolving technology issues at financial services companies or addressing financial or regulatory issues confronting technology firms, this course will put students in the manager’s or protagonist’s role in gathering and analyzing information before ultimately making decisions. We begin with case studies at traditional financial firms before evolving to more non-traditional firms. The financial or regulatory issues challenging technology companies, and the emerging categories of digital assets like crypto currencies in the context of alternative strategy in investment management will also be investigated and studied.

Selected topics in financial technology of current interest in emerging areas. May be repeated for credit if different topics are covered.