Course Description:

This course provides a foundation for advanced quantitative trading in financial markets.  The course has two parts.  First, the course reviews stylised facts and methods used for time-series predictability, cross-sectional asset pricing and strategy performance evaluation.  The second part of the course uses these tools to study recent advances in investment strategies sourcing from academic and practitioner literature.  For example, the course will discuss new theories on risk premia, intermediation-based asset pricing, and quantifiable soft information and alternative data.  The primary method of learning will be a combination of problem sets and projects.  Subject to availability, learning will be supplemented with exposure to industry speakers from the local financial industry.

Prerequisite: MFIN7002 Investment Analysis and Portfolio Management

Assessment: 100% coursework

​Course Name: Quantitative trading

Course Type: List-C-2

​IsCEF: TBC

Course Credit: 6

Course Description:

Machine learning and artificial intelligence are the apex technologies of the information era. These methods are getting increasingly popular in the financial market. This course provides students the fundamental models and methods of machine learning and apply them to solve real-world financial problems. The topics include regression, classification, clustering methods, model selection, topic modelling and policy search. The first part of the course focuses on supervised learning techniques for regression and classification. The second part of the course covers unsupervised learning techniques for clustering and matrix factorisation. The third part of the course covers reinforcement learning algorithm. The last part provides the fundamental concepts of artificial intelligence and its implications. The course provides introductions to the latest datasets in financial markets and practices applying learning algorithms to these datasets in a variety of topics. The primary mode of learning is based on assignments and projects.

Assessment: 60% coursework and 40% examination

​Course Name: Machine learning and artificial intelligence in finance

Course Type: List-C-2

​IsCEF: TBC

Course Credit: 6

Course Description:

Money laundering and terrorist financing are examples of financial crimes that can, among other things, undermine the integrity and stability of financial institutions and the economic system at large, deter foreign investment, and distort international capital flows.  Money launderers and terrorist financiers are now deploying increasingly sophisticated methods and schemes to disguise and achieve their illicit purposes, and are particularly attracted to exploit those jurisdictions with weak or ineffective anti-money laundering (“AML”) and counter-terrorist financing (“CTF”) controls.  Thus, developing a solid and comprehensive understanding of the concepts of money laundering and terrorist financing as well as keeping abreast of the respective regulatory frameworks are crucial to appreciating and managing such risks and challenges in the context of a financial services business.

This course is designed to not only provide students with an overview of the legal and regulatory aspects of AML and CTF, but also to equip students with practical skills and best practices to detecting and managing these types of financial crime risks in a financial institution setting.  To achieve these objectives, this course is made up of three main modules.  The first module explores the concepts and typologies of money laundering and terrorist financing.  These concepts will be contextualised against the international efforts that been deployed to combat these illicit activities.  The Hong Kong AML and CTF framework, and the roles of the respective enforcement agencies, will also be discussed.  The second module examines the key components of a sound AML and CTF compliance programme in a financial institution.  The way how this programme should be embedded within the broader internal control, risk management, and governance framework will also be considered.  The third module focuses on some thematic issues of an AML and CTF compliance programme, including customer due diligence, escalation and exit strategies, suspicious activities, suspicious transaction reporting, and dealing with customers and regulators.

In this course, students will be learning through different activities.  Besides the lecture component, students will be provided with an opportunity to deliver presentations and participate in in-class discussion on different case studies and court cases.  Where appropriate, practitioners in the relevant field will be invited to share with students their experience and insights on how different AML and CTF issues come into play and handled in practice.

Assessment: 80% take home examination, 20% group presentation

​Course Name: Law of anti-money laundering and counter-terrorist financing and compliance issues

Course Type: List-C-2

​IsCEF: No

Course Credit: 9

Course Description:

This course will explore privacy and data protection in an increasingly interconnected data economy.  The Personal Data (Privacy) Ordinance and the data protection principles in particular will be studied in depth, making reference to relevant court judgments and Administrative Appeal Board cases.  Privacy protection under other ordinances and common law principles (such as breach of confidence, misuse of private information, nuisance, trespass, copyright infringement and defamation) will also be covered.  Emphasis will be made to the balance between privacy on the one hand and other rights as well as public and social interests on the other. The challenges posed by technological innovations and applications such as the internet, social media, mobile applications, cloud computing and Big Data will be highlighted. Specific topics to be addressed will include: (a) the concept of privacy and the genesis and development of its political, philosophical and economic underpinnings; (b) global development and international cooperation;(c) privacy and media intrusion; (d) regulation of direct marketing; (e) Privacy Commissioner for Personal Data: powers, functions and enforcement.  The course will focus on the Hong Kong situation but reference will be made to relevant international human rights instruments and the global and regional trends and developments.

Assessment: 40% research assignment and 60% take home examination

​Course Name: Privacy and data protection

Course Type: List-C-2

​IsCEF: TBC

Course Credit: 9

Course Description:

Basics of financial markets; cash flow analysis; capital asset pricing model (CAPM); portfolio optimisation; arbitrage and fundamental theorem of asset pricing; types of derivatives including forward, futures and options for various underlying assets; returns, value-at-risk (VaR), utility functions; pricing and hedging of derivative securities; numerical studies.

Assessment: 30% coursework and 70% examination

​Course Name: Financial engineering

Course Type: List-C-2

​IsCEF: TBC

Course Credit: 6

Course Description:

This course provides students with the fundamentals of financial services in the context of e-Commerce and mobile devices.  Payment systems in general and various payment transaction systems in particular will be examined.  Similarly, eFinance has brought new concepts into e-Brokerage, e-Insurance, e-Lending and other fields.  The course covers technology, operations, customer experience as well as demonstrates how regulations and security aspects are impacted by developments like Bitcoin and Blockchain.  Studies of established banks as well as new FinTech Players serve as examples and reinforcements for many of the concepts.

Assessment: 40% coursework and 60% examination

​Course Name: E-financial services

Course Type: List-C-2

​IsCEF: TBC

Course Credit: 6

Course Description:

This course aims at introducing statistical methodologies in analysing financial data.  Financial applications and statistical methodologies are intertwined in all lectures.  Contents include: classical portfolio theory, portfolio selection in practice, single index market model, robust parameter estimation, copula and high frequency data analysis.

Assessment: 40% coursework and 60% examination

​Course Name: Financial data analysis

Course Type: List-C-2

​IsCEF: TBC

Course Credit: 6

Course Description:

This course provides an in-depth exploration of blockchain technology and distributed ledger technology (DLT) and their applications in the context of Smart Banking and Innovative Finance.  Students will gain a comprehensive understanding of the underlying principles, functionalities, and potential benefits and challenges of the emerging Financial Technology (FinTech) 3.0.

The course will cover the emerging trend in Smart Banking and Innovative Finance with various disruptive business-IT (DLT and BlockChain) models in the evolving FinTech ecosystem such as decentralized finance (DeFi), central bank digital currencies (CBDC) and Hong Kong SAR Government’s w-CBDC and rCBDC projects, eHKD/eCNY use cases, Open Banking and API (Application Programming Interface) ecosystem, Virtual Banks and Stored Valued Facility (SVF), Banking as a Service (BaaS), Banking as a Platform (BaaP), Faster Payment System (FPS) and cross-border payment/forex applications, smart contracts, tokenization and tokenomics, WealthTech, InsurTech, Self-Sovereign Identity (SSI), Zero Knowledge Proof (ZKP), and the related regulatory considerations.

Through lectures, case studies, in-class discussions, group presentations and reflective exercises, students will develop practical skills in designing, implementing, and managing blockchain and DLT solutions for Smart Banking and Innovative Finance.

Assessment: 100% coursework

​Course Name: Smart banking and innovative finance

Course Type: List-C-2

​IsCEF: TBC

Course Credit: 6