Master of Science

Elective courses (15 units):

Courses are categorized into “Technology-Oriented Electives”,  “Business/Application-Oriented Electives” and “Enrichment Training”. Details are illustrated as follows:

Technology-Oriented Electives

Business/Application-Oriented Electives

Enrichment Training

# Students who register for the course must (1) attend the regulatory workshops provided by regulatory authorities, such as the Hong Kong Monetary Authority, Securities & Futures Commissions, and Insurance Authority, and (2) engage in a technology-related post for at least 12 continuous weeks as approved by the Professor-in-Charge.

Required Courses

FINA6122 Financial Markets and Instruments

Financial markets play a pivotal and central role in the formation of investment capital and, therefore, societal wealth. This function, in addition to many others, is explored in this course. Attention will also focus on the forms and structures of financial markets (i.e., stock, futures, gold and foreign exchange markets) both locally and internationally. Within this brief, detailed consideration is also given to the various institutions participating in the markets and to the form and functions of instruments quoted.

FTEC5510 Advanced Financial Infrastructure

This course provides an advanced examination of financial infrastructures, which include trading venues and platforms, securities settlement systems, payment systems, cross-border transactions interlinks, central counterparties and clearance, cybersecurity, and systemic risks. It requires students to undertake project work with a view to make concrete their understanding of the underlying technologies enabling Hong Kong’s financial infrastructure.  The student projects will be required to explore the implications of new financial services technology, in particular, the licensing prerequisites, regulatory controls and supervision such new services necessitate.  Students will have to form groups and select a particular infrastructure area, e.g. electronic payments or lending, and conduct a deep analysis of its operations, and thereby creating new/enhanced service propositions. It is intended that project teams will be able to create prototype solutions through collaborating with, for example, the Hong Kong and Science Technology Park (HKSTP), Cyberport, ASTRI, or other CUHK labs. Upon completing this course, students should have acquired advanced understanding of components of financial infrastructure, and its significance in the financial services sector.

FTEC5520 Applied Blockchain and Cryptocurrencies 

The course introduces the basic underlying cryptographic concepts and technologies of blockchain as a method of securing distributed ledgers in all digital transactions. The significances of trust, anonymity and consensus mechanisms are discussed. The course also further explains the application of blockchain to cryptocurrencies and smart contracts. Examples of public and private blockchains are discussed with emphasis on the applications of blockchain in Finance.

Elective courses

ACCT6141 Accounting Data Analytics and Business Intelligence

While the production of data is expanding at an astonishing pace, companies are on average using only a fraction of the data out there. Recent survey by IMA (Institute of Management Accountants) in 2016 reveals significant skill gaps in many areas employers need most, especially identifying key data trends, data mining and extraction, operational analysis, technological acumen, statistical modeling and data analysis. This course targets towards accounting students who are interested in getting hands-on experience with data analytics and rising to the challenges and opportunities big data presents to the profession.

The course will provide a basis for handling and analyzing large-scale data stored within and outside an organization and finding business insights from analyses. In the first two-thirds of the course, we will study the basic concepts and techniques related to data preprocessing, data exploration, model building and results interpretation. Instead of digging deep into technical details, the course will provide a broad survey of common approaches and focus on the pragmatic implementation of data analytics. In the latter one-third of the course, we will discuss the emerging trends in big data and their applications in the accounting profession as well as other industries. The course will try to utilize real datasets related to accounting and finance and enable students to “learn by doing” through hands-on experience with data.

CMSC5741 Big Data Technology and Applications

This course aims at teaching students the state-of-the-art big data technology, including techniques, software, applications, and perspectives with massive data.  The class will cover, but not be limited to, the following topics: advanced techniques in distributed file systems such as Google File System, Hadoop Distributed File System, and map-reduce technology; similarity search techniques for big data such as minhash, locality-sensitive hashing; specialized processing and algorithms for data streams; big data search and query technology; recommendation systems for Web applications.  The applications may involve business applications such as online marketing, computational advertising, location-based services, social networks, recommender systems, healthcare services.

cmsc5743 Efficient Computing of Deep Neural Networks

The high computational demands of deep neural networks (DNNs) coupled with their pervasiveness across both cloud and IoT platforms have led to a rise in specialized hardware and software techniques to accelerate DNN executions. This course will present techniques that enable efficient applications and computing of DNNs. The course will start with an overview of DNNs, and then will introduce various frameworks and architectures that support DNNs, as well as the implementations and optimizations on some particular computing platforms.

Dsme6751 Database and Big Data Management

This course focuses on both business data and contemporary big data modeling and management. We will examine the different natures of data and big data, selection and representation as well as use of suitable methods and tools for storing and accessing them. Topics such as data integrity, DBMS, data warehousing, NoSQL and MapReduce are covered. 

dsme6756 Business Intelligence Techniques and Applications

This course emphasizes on the applications of business intelligence techniques in the era of big data. The techniques will include data preparation, dimensionality reduction, clustering, classification, market basket analysis, and performance evaluation. Business applications such as customer segmentation and financial analysis will be discussed throughout the course.

ECLT5720 Electronic Payments Systems 

This course covers various methods of transferring payments over the Internet and compares their functionality. Topics include electronic money, electronic contracts, micro-payments, authenticity, integrity and reliability of transactions, encryption and digital signature techniques needed to support electronic cash, and technologies available to support secure transactions on the Internet. 

ENGG5108 Big Data Analytics

This course aims at teaching students the state-of-the-art big data analytics, including techniques, software, applications, and perspectives with massive data. The class will cover, but not limited to, the following topics: advanced techniques in distributed file systems such as Google File System, Hadoop Distributed File System, CloudStore, and map-reduce technology; similarity research technique for big data such as minhash, locality-sensitive hashing; specialized processing and algorithms for data streams; big data search and query technologies; managing advertising and recommendation systems for Web applications. The applications may involve business applications such as online marketing, computational advertising, location-based services, social networks, recommender systems, healthcare services, or other scientific applications.

FINA6250 Fundamentals of Derivatives Trading Strategies

This course intends to offer students insight into the implementation of options theory in the practical trading environment.  The main strategies for trading derivatives will be discussed and the students are to apply their knowledge in practical trading sessions.

FTEC4006 Internet Finance

This course provides students with the fundamentals in the operations and management of Internet finance. It will cover overall applications of Internet-based technologies such as mobile payments, social network, search engines, cloud computing, and big data on the financial sector. Specific topics include third-party payments, Internet currency, P2P lending, crowdfunding, and the use of big data in financial services.  The course adopts case studies as the major means of teaching and learning. 

FTEC5530 Quantitative and Algorithmic Trading

The industry landscape of investment, trading, and risk management has been revolutionized by computing technologies, data science, and financial engineering. To progress in tandem with the changes in the industry, the topics covered in this course leverage on the recent developments in portfolio theory, empirical finance, and quantitative and algorithmic trading. In addition to mathematical modeling, an important part of this course is the practical aspect: computational implementations with statistical tests. Given that implementation and test procedures are involved, this quantitative finance course is algorithmic and hands-on in nature.

FTEC5540 Financial Technology Internship

The objective of the course is to allow students to acquire a basic understanding and the skills of the practical aspects of the financial technology profession.  To qualify for the award of the subject credits, the student must (a) attend regulatory workshop provided by such regulatory authorities as the Hong Kong Monetary Authority, Securities & Futures Commission, and Insurance Authority, and (b) attach to a company in a financial technology related post as approved by the Professor-in-Charge for no less than 12 weeks.  The student will have an academic supervisor as assigned by the Professor-in-Charge and an industry supervisor from the company.  There will be a mid-term company visit by the academic supervisor.  At the end of the internship, the student must give a presentation to the academic and industry supervisors, and submit a report summarizing what the student has done and learnt from the internship.  The student’s grade will be determined by (1) the presentation, (2) the student report and (3) a testimonial from the industry supervisor.

The internship should normally take place in the summer term after a student has finished the first two semesters of studies.  Part-time students can decide to undertake the internship in the summer term of either the first or second year of studies.

Students are recommended to seek the Professor-in-Charge's comment on potential internship opportunities before enrolling in the course.

FTEC5550 Elements of RegTech for the Financial Market

Financial markets must be safe, efficient and fair. It is a primary goal of market regulation to achieve this. Financial regulations touch the market at a number of key points of inflection in order to maintain market quality. These points include the qualifications of market participants, their ongoing compliance with law, and the manner in which they trade and transact, as well as the information (public or private) they use for trading and the way they deal with clients. Information Technology, data analytics and machine learning are allowing market regulation to advance in ways previously not possible, such as through monitoring of trading and transactions, detection of abusive activities and focused enforcement, among others. This course will bring experts from information technology, finance and law together in order to give students the fundamental elements of both financial regulation and information technology as they meet in the important new discipline of RegTech.

FTEC5560  Topics in Financial Technology Innovations

This course aims to provide students with the views of global, and in particular China and HK FinTech innovation landscape, including virtual banking and other business model innovation, associated opportunities and challenges. It covers topics including digital lending, digital payment, big data analysis application, FinTech partnership & incubation, Open API, digital product propositions, organizational design to support innovation. This is relevant for students who are interested to work in innovation-related departments in traditional institutions, FinTechs or intend to initiate a start-up. The course also covers the latest development in financial technology innovations and offers case studies.

FTEC5570 Financial Technology Entrepreneurship*

The aim of this course is to turn innovative, creative, and inquisitive FinTech students into entrepreneurs through multi-disciplinary, fully-immersive, hands-on, and project-based learning. The course covers topics in disruptive innovation and business models, human centered and design-driven innovation, business plan formulation, start-up pitch, how technology spur entrepreneurship, fundraising, growth, acquisition and IPO, making critical decisions during the journey, in order to equip participants to become entrepreneurs in the FinTech world. Course participants are expected to:
1. Learn the basic knowledge on patents, intellectual property, contracts, copyright, and trademarks
2. Acquire refined communication, organizational, and leadership skills for entrepreneurs
3. Conduct case-study research on how FinTech entrepreneurship plays an important role in the development of the high-tech industry
4. Participate in a field-trip to visit organizations (e.g. in Silicon Valley or Bay Area) on various FinTech domains for a close-up on the operation of these organizations
5. Take part in a class group project and present the research finding on FinTech entrepreneurship and the learning outcome from the field trip

* The arrangement of field-trip to visit overseas organizations will be subject to the situation of COVID-19 pandemic and may be substituted by other learning activities.

FTEC5580 Data Analytics for Financial Technology

The aim of this course is to equip students with essential data analytics techniques for problems in financial technology. Topics covered by this course include linear and general regression, classification techniques such as logistic regression and discriminant analysis, decision trees, support vector machines, principal component analysis, clustering methods, time series models, resampling methods and deep learning models. Various applications in asset management, risk management, asset pricing and financial prediction will be used to illustrate the methods throughout this course. Students also learn how to implement these methods in the popular data analytics software R to analyze financial data.

FTEC5590 Intelligent Automation - Robotic Process Automation (RPA) & Artificial Intelligence (AI)

Intelligent Automation (IA) is software-based automation which executes business processes on behalf of knowledge workers with minimal human intervention. IA combines methods and various technologies, in particular, robotic process automation (RPA) and artificial intelligence. As the core and foundational technology of intelligent automation (IA), robotic process automation (RPA) has been described as a first step on the stairway of intelligent automation towards building digital workers. The aim of this course is to provide learners comprehensive coverage of intelligent automation – including RPA development skills and AI fundamentals– in the context of financial services. This course will build the capabilities of students to an advanced level and will enable participants to
actively engage in the full lifecycle of "bot" development from assessment of automation opportunities through the building/deployment of functional automations. Course participants are expected to:
1. Gain a clear understanding of the intelligent automation (RPA + AI) industry and benefits and understand the limits and constraints of process automation.
2. Learn about the role of business analysis in the context of process automation implementation including: (a) how to conduct an automation opportunity assessment; (b) how to map the requirements and deliverables of a process automation project; (c) best practices during the Process Deep Dive stage including ways of compiling comprehensive process-related documentation; (d) testing strategies for an process automation solution; (e) how to conduct trainings and create a User Manual during Go-Live preparations; and (f) how to manage changes throughout the entire process automation development process.
3. Understand the delivery methodology of process automation project/program management including: (a) steps of a process automation implementation, (b) choosing processes suitable for automation, (c) driving/managing process automation projects, and (d) raising intelligent automation awareness throughout an organization.
4. Obtain a solid grasp of the technical skills required to create software robots.
5. Be able to build and deploy software robots to automate a variety of manual tasks/process encountered in financial operations. 

FTEC5910/5920 Financial Technology Project I / II

The two courses, Financial Technology Project I & II, involve a significant research and/or development project under the supervision of an academic staff in any area of financial technology.  Unless otherwise approved by the Professor-in-Charge, the project should involve an industrial partner with a co-supervisor from the industry.  

At the end of the course, the student must give a presentation to the academic and industry supervisors, and submit a report detailing the findings and deliverables.  The student’s grade will be determined by the presentation and the report.

Unless otherwise approved by the Programme Director, the credits for Financial Technology Project I are recognized if and only if the student takes also Financial Technology Project II in a subsequent semester.

If a student has prior industrial background and has already secured a project topic with an industrial practitioner and the agreement of an academic staff, Financial Technology Project I can be taken in the first semester of studies.  Otherwise, the two project courses can be taken in any consecutive semesters, including the summer term.

Students are encouraged to find industrial projects by themselves and talk to various academic staffs for project ideas before enrolling in the courses.

FTEC5610 Computational Finance 

This course will introduce pricing models and algorithms in managing the risks and making decisions regarding the trading and analysis of financial assets. Main areas that will be covered include, but not limited to: (i) Models for asset allocation; (ii) Derivative models and products; (iii) Practical applications of financial modelling. Assignments will be set up to help students develop an intuitive understanding of how these models can be implemented.

IEMS5710 Cryptography, Information Security and Privacy

This course aims to enhance students' knowledge in cryptography as well as information security and privacy, in both theoretical and practical ways. The course introduces cryptography at an elementary level, enabling students to appreciate on its application to information security and privacy. Daily applications of cryptography will be discussed, including digital certificate and Public Key
Infrastructure (PKI), Virtual Private Network (VPN), wireless communication security, as well as security and privacy issues in online social networks.

IEMS5730 Big Data Systems and Information Processing

This course aims to provide students an understanding in the operating principles and hands-on experience with mainstream Big Data Computing systems. Open-source platforms for Big Data processing and analytics would be discussed. Topics to be covered include:
• Programming models and design patterns for mainstream Big Data computational frameworks ;
• System Architecture and Resource Management for Data-center-scale Computing ;
• System Architecture and Programming Interface of Distributed Big Data stores ;
• High-level Big Data Query languages and their processing systems ;

Advisory: This course contains substantial hands-on components which require solid background in programming and hands-on operating systems experience.

IERG5310 Security & Privacy in Cyber Systems

This course discusses the design and realization of security and privacy services in practical large-scale systems. Topics include: Online Identity and Authentication Management ; Safe Browsing ; Geolocation privacy ; Mobile payment systems with Smartcard/ Near Field Communications (NFC) ; e-cash ; Best privacy practices for Online Social Networks and Mobile applications ; Cloud Computing security and privacy: Trustworthy Cloud Infrastructure; Secure Outsourcing of Data and Computation ; Data Provenance; Virtual Machine security. Additional cyber security services/applications such as e-voting systems, secure and anonymous routing systems, digital rights management will also be covered. 

Advisory: Students are expected to have basic background in Cyber Security.

IERG5320 Digital Forensics

This course is an introduction to digital forensics and cyber crime investigation. It will discuss techniques, methods, procedures and tools for applying forensic science and practice to the acquisition and analysis of evidence existing in digital form for the purposes of cyber crime investigation. Specific topics include computer (hard disk, file-systems) forensics, network/intrusion forensics, mobile device forensics, and a brief introduction to multimedia forensics. Techniques for detecting, tracking, dissecting and analyzing malware and other malicious cyberspace activities will also be covered. 

Advisory: Students are expected to have basic background in C/C++ programming skills.

ierg5350 Reinforcement Learning

This course aims to cover the fundamental topics relevant to Reinforcement Learning (RL), a computational learning approach where an agent tries to maximize the total amount of reward it receives while interacting with the complex and uncertain environments. The course content includes the basics of Markov Decision Processes, model-based and model-free RL techniques, policy optimization, RL distributed system design, as well as the case studies of RL for game playing such as AlphaGo, traffic simulation, and other robotics applications.

Advisory: Students are expected to have solid foundation on signal processing.

seem5730  Information Technology Management

The challenges, techniques and technologies associated with the management of information technology (IT) in a competitive environment. The linkage of IT to business strategy and business process re-engineering. Different types of information systems: MIS, DSS, TPS.  Information technology concepts: networking, database, batch and distributed processing.  Development Process. Information system planning. Systems project management and control. IT acquisition, budgeting and deployment. Performance evaluation and audit.  Operations management, privacy and security.

SEEM5820 Introduction to Financial Engineering*

Models of risks. Utility functions, and mean-variance theory. Probability models and price dynamics of securities. Geometric Brownian motion, Ito’s lemma, Black-Scholes model. Capital asset pricing. Risk hedging. Optimization techniques. Applications to investment and portfolio management. The emphasis is on mathematical modelling, analysis and computation.

*This course is previously known as "Models and Decisions with Financial Applications".

seem5840 Quantitative Risk Management

Introduction of financial risk, standard deviation, tail risk, extreme value theory, correlation and copula, risk attribution and capital allocation, credit risk, liquidity risk, operational risk.