Program Curriculum

The HKUST MScFinTech program is offered in both one-year full-time and two-year part-time study modes. Both full-time and part-time students are required to complete a total of 30 course credits.

 

Core Courses (8 courses, 16 credits)

AI for FinTech (2 credits)
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.
Blockchain (2 credits)
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.
Corporate Finance (2 credits)
Valuation of cash flow streams (PV of cash flow streams, annuities, and perpetuities); valuation of bonds; valuation of stocks using dividend discount model; capital budgeting decisions (NPV, IRR, payback); capital structure; limits to the use of debt (trade-off models); estimation of cost of debt and equity; WACC; terminal value.
Data Analysis (2 credits)
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.
Financial Data Mining (2 credits)
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 network mining.
FinTech Regulations and Compliance (2 credits)
This course provides students with frameworks, concepts, and background to understand the role of regulation, compliance and assurance in FinTech markets from both technology and business perspectives. The course will also examine the perspectives of government officials, investors, managers, and consumers in how they benefit from, guide, and influence the evolution of regulation and associated compliance activities.
Foundations of FinTech (2 credits)
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.
Investment Analysis (2 credits)
An introduction to fundamental concepts in investment analysis. The first part covers risk and return tradeoff, portfolio diversification, and modern portfolio theory including the capital asset pricing model and arbitrage pricing theory. The second part covers basic analytical tools used in analyzing fixed income securities. Topics include interest rates and yield curve mathematics, duration and convexity.

 

Electives* (a selection of 14 credits)

School of Business and Management
Cryptocurrency, Blockchain, and Their Business Applications  2 credits
Economics of Financial Technology 2 credits
Entrepreneurship and Innovation in FinTech 2 credits
FinTech: Algorithmic Trading 2 credits
Statistics for Financial Analysis 2 credits
FinTech and Big Data Financial Analytics 2 credits
School of Engineering
Decision Analytics for FinTech 3 credits
Optimization in FinTech 3 credits
Technology and Analytics of Alternative Finance 3 credits
School of Science
Mathematical Foundation of FinTech# 2 credits
Statistical Machine Learning 3 credits
Statistical Methods in Finance 3 credits
 

* The electives offered in a particular year will be announced at the beginning of each academic year. Students have to take at least ONE elective course from each of SBM, SENG and SSCI. The offering of elective courses is subject to availability.

# Subject to the decision of the Program Director, promising students who do not have a strong mathematics background may be required to take this elective.