POSTGRADUATE
PROGRAMS
PROGRAMS
Postgraduate Programs 2024/25
Master of Philosophy and Doctor of Philosophy Programs in Financial Technology
Award Title
Master of Philosophy in Financial Technology
Doctor of Philosophy in Financial Technology
Program Short Name
MPhil(FinTech)
PhD(FinTech)
Mode of Study
Both full- and part-time
Normative Program Duration
MPhil
Full-time: 2 years
Part-time: 4 years
PhD
Full-time: 3 years (with a relevant research master’s degree), 4 years (without a relevant research master’s degree)
Part-time: 6 years
Offering Unit
Financial Technology Thrust Area
Society Hub
Program Advisor
Program Director:
Prof Ning CAI, Professor of Financial Technology
Enquiry
Financial Technology (FinTech) is an important emerging area that has been developing rapidly in recent years. It refers to the application of cutting-edge technologies and advanced analytics on various financial services, such as mobile banking, peer-to-peer lending, digital payments, blockchain, and cryptocurrencies, aiming to improve service efficiency, promote financial innovations, and increase end-user satisfaction.
The Master of Philosophy (MPhil) and Doctor of Philosophy (PhD) Programs in Financial Technology provide training and education for students to undertake advanced research and have a sound grasp of developments in FinTech. Students graduating from these programs should be able to conduct and apply high-quality research that makes an impact on FinTech research and practice in academia and/or industry. The programs focus on advanced research with an aim to place graduates in academia, research institutes, and industry jobs that appreciate research capability and quality.
On successful completion of the MPhil program, graduates will be able to:
- Identify and synthesize current research in FinTech;
- Compare and contrast state-of-the-art knowledge in FinTech and relevant reference disciplines (e.g., accounting, finance, computer science, mathematics), and apply such knowledge in driving FinTech research, practice, and innovation;
- Analyze, design, and execute FinTech research by utilizing proper research methodologies; and
- Communicate the developed FinTech knowledge and research with the academic and practitioner community.
On successful completion of the PhD program, graduates will be able to:
- Identify and synthesize current research in FinTech;
- Compare and contrast state-of-the-art knowledge in FinTech and relevant reference disciplines (e.g., accounting, finance, computer science, mathematics), and apply such knowledge in driving FinTech research, practice, and innovation;
- Analyze, design, and execute FinTech research by utilizing proper research methodologies;
- Communicate the developed FinTech knowledge and research with the academic and practitioner community; and
- Create original, substantive, and impactful knowledge to advance the state of FinTech research and practice.
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Minimum Credit Requirement
MPhil: 15 credits
PhD: 21 credits -
Credit Transfer
Students who have taken equivalent courses at HKUST or other recognized universities may be granted credit transfer on a case-by-case basis, up to a maximum of 3 credits for MPhil students, and 6 credits for PhD students.
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Cross-disciplinary Core Courses
2 credits
UCMP 6010
Cross-disciplinary Research Methods I
2 Credit(s)
Description
This course focuses on using various approaches to perform quantitative analysis through real-world examples. Students will learn how to use different tools in an interdisciplinary project and how to acquire new skills on their own. The course offers different modules that are multidisciplinary/multifunctional and generally applicable to a wide class of problems.
UCMP 6020
Cross-disciplinary Research Methods II
2 Credit(s)
Description
This course focuses on using various approaches to perform quantitative analysis through real-world examples. Students will learn how to use different tools in an interdisciplinary project and how to acquire new skills on their own. The course offers different modules that are multidisciplinary/multifunctional and generally applicable to a wide class of problems.
UCMP 6030
Cross-disciplinary Design Thinking I
2 Credit(s)
Description
This course focuses on user-collaborative design methods for generating inclusive product solutions that integrate stakeholder and product functionality perspectives. Students will create specified product/process/policy/protocol/plan (5P) concept models through the use of recursive user feedback engagement methods, experimental prototyping, and divergent and convergent ideation strategies. Featured topics include design thinking; stakeholder research; concept development, screening, and selection; and interaction design.
UCMP 6040
Cross-disciplinary Design Thinking II
2 Credit(s)
Description
This course focuses on user-collaborative design methods for generating inclusive product solutions that integrate stakeholder and product functionality perspectives. Students will create specified product/process/policy/protocol/plan (5P) concept models through the use of recursive user feedback engagement methods, experimental prototyping, and divergent and convergent ideation strategies. Featured topics include design thinking; stakeholder research; concept development, screening, and selection; and interaction design.
All students are required to complete either UCMP 6010 or UCMP 6030. Students may complete the remaining courses as part of the credit requirements, as requested by the Program Planning cum Thesis Supervision Committee.
- Hub Core Courses
4 Credits
Students are required to complete at least one Hub core course (2 credits) from the Society Hub and at least one Hub core course (2 credits) from other Hubs.
Society Hub Core Course
SOCH 5000
Technological Innovation and Social Entrepreneurship
2 Credit(s)
Description
This course discusses both opportunities and risks that technological breakthrough has brought to the human society. What would be the policy responses required to maximize its positive benefit and minimize its social costs? In particular, how could we utilize the technological advancement, entrepreneurial thinking to address the challenges our societies are facing, such as job loss/unemployment, income inequality and societal polarization, environmental degradation, health disparity, population aging, and among others. The course uses either case studies or cross-country and time-series data analyses to facilitate the discussion of various social issues and look for innovative solutions of in the real world.
Other Hub Core Courses
FUNH 5000
Introduction to Function Hub for Sustainable Future
2 Credit(s)
Description
This course covers background knowledge in the thrust areas of the Function Hub, including Advanced Materials, Sustainable Energy and Environment, Microelectronics, and Earth, Ocean and Atmospheric Sciences.
INFH 5000
Information Science and Technology: Essentials and Trends
2 Credit(s)
Description
This inquiry-based course aims to introduce students to the concepts and skills needed to drive digital transformation in the information age. Students will learn to conduct research, explore real-world applications, and discuss grand challenges in the four thrust areas of the Information hub, namely Artificial Intelligence, Data Science and Analytics, Internet of Things, and Computational Media and Arts. The course incorporates various teaching and learning formats including lectures, seminars, online courses, group discussions, and a term project.
SYSH 5000
Model-Based Systems Engineering
2 Credit(s)
Description
Model-based systems engineering (MBSE) is a contemporary systems engineering methodology that uses conceptual models for communication between system architects, designers, developers, and stakeholders. Object-Process Methodology (OPM) is an MBSE language and methodology for constructing domain-independent conceptual models of all kinds of systems. The course provides students with basic knowledge and tools for MBSE, focusing on conceptual modeling of systems, giving learners a competitive advantage over their peers.
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Courses on Domain Knowledge
MPhil: minimum 9 credits of coursework
PhD: minimum 15 credits of coursework
Under this requirement, each student is required to take a required course and other elective courses to form an individualized curriculum relevant to the cross-disciplinary thesis research. To ensure that students will take appropriate courses to equip them with needed domain knowledge, each student has a Program Planning cum Thesis Supervision Committee to approve the courses to be taken soonest after program commencement and no later than the end of the first year. Depending on the approved curriculum, individual students may be required to complete additional credits beyond the minimal credit requirements.
Required Course List
FTEC 5040
Financial Technology Research
3 Credit(s)
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.
Sample Elective Course List
To meet individual needs, students will be taking courses in different areas, which may include but not limited to courses and areas listed below.
FTEC 5030
Statistical Methods for Financial Technology
3 Credit(s)
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.
FTEC 5031
Advanced Probability Theory
3 Credit(s)
Description
The course will give students a deeper understanding of the foundations of probability theory, such as probability theory from a measure-theoretic perspective, convergences of distributions and probability measures, and conditional expectations. During the course, important theorems, such as Radon-Nikodym theorem, Fubini theorem, and general central limit theorems, will be investigated.
FTEC 5032
Optimization Theory
3 Credit(s)
Description
The objective of this course is to provide students with optimization theory and concepts. Main topics cover linear optimization, simplex method, duality theory, convex analysis, and dynamic programming. The emphasis will be on methodology, modelling techniques and mathematical insights.
FTEC 5050
Machine Learning and Artificial Intelligence
3 Credit(s)
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.
FTEC 5060
Stochastic Processes
3 Credit(s)
Description
The objective of this course is to provide students with fundamentals of stochastic processes. Main topics cover Poisson processes, renewal theory, discrete-time Markov chains, continuous-time Markov chains, and martingales. The emphasis will be on methodologies, fundamental concepts, and mathematical insights.
FTEC 5100
Research in Corporate Finance
3 Credit(s)
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.
FTEC 5101
Microeconomic Theory
3 Credit(s)
Description
This is a course in graduate level microeconomic theory for PhD students in financial technology and other related fields. This course covers topics including consumer theory, producer theory, uncertainty, general equilibrium,and matching. The required background knowledge for the course are intermediate microeconomic theory and mathematics through calculus of several variables and introductory real analysis. Additional mathematical tools will be explained briefly as the course proceeds. This course serves as the first rigorous training in economics and finance and helps lay down a solid foundation in economic modelling for future research.
FTEC 5110
Research in Asset Pricing
3 Credit(s)
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.
FTEC 6000
FinTech Attachment
2-4 Credit(s)
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.
AIAA 5024
Advanced Deep Learning
3 Credit(s)
Description
This course covers recent developments in deep learning. Topics include meta learning, model compression, federated learning, representation learning, explainable AI, adversarial attack and defense, and advances in deep learning theory.
AIAA 5025
Deep Reinforcement Learning
3 Credit(s)
Description
This course covers recent developments in deep reinforcement learning. Topics include reinforcement learning basics, deep Q-learning, policy gradients, actor-critic algorithms, model-based reinforcement learning, imitation learning, inverse reinforcement learning, hierarchical reinforcement learning, and multi-agent reinforcement learning.
DSAA 5009
Deep Learning in Data Science
3 Credit(s)
Description
In this course, theories, models, algorithms of deep learning and their application to data science will be introduced. The basics of machine learning will be reviewed at first, then some classical deep learning models will be discussed, including AlexNet, LeNet, CNN, RNN, LSTM, and Bert. In addition, some advanced deep learning techniques will also be studied, such as reinforcement learning, transfer learning and graph neural networks. Finally, end-to-end solutions to apply these techniques in data science applications will be discussed, including data preparation, data enhancement, data sampling and optimizing training and inference processes.
DSAA 5013
Advanced Machine Learning
3 Credit(s)
Description
In this course, advanced algorithms for data science will be introduced. It covers most of the classical advanced topics in algorithm design, as well as some recent algorithmic developments, in particular algorithms for data science and analytics.
IOTA 5501
Convex and Nonconvex Optimization I
3 Credit(s)
Description
This course covers fundamental theory, algorithms, and applications for convex and nonconvex optimization, including: 1) Theory: convex sets, convex functions, optimization problems and optimality conditions, convex optimization problems, geometric programming, duality, Lagrange multiplier theory; 2) Algorithms: disciplined convex programming, numerical linear algebra, unconstrained minimization, minimization over a convex set, equality constrained minimization, inequality constrained minimization; 3) Applications: approximation (regression), statistical estimation, geometric problems, classification, etc.
DSAA 5022
Data Analysis and Privacy Protection in Blockchain
3 Credit(s)
Description
This course introduces basic concepts and technologies of blockchain, such as the hash function and digital signature, as well as data analysis and privacy protection over blockchain applications. The students will learn the consensus protocols and algorithms, the incentives and politics of the block chain community, the mechanics of Bitcoin and Bitcoin mining, data analysis techniques over blockchain and user/transaction privacy protection.
IPEN 5130
Economics of Technology Innovation and Entrepreneurship
3 Credit(s)
Description
This course introduces the economics of technology innovation and entrepreneurship through the combined perspectives of microeconomics and macroeconomics. It covers microeconomic core modules concerning consumers, firms, markets, and governments, as well as macroeconomic core modules on economic growth associated with entrepreneurship and innovation.
IPEN 5200
Uncertainty, Information and Decision Making
3 Credit(s)
Description
This course introduces the economic theories of decision making under risk and uncertainty and how agents with heterogeneous information interact strategically. Sample topics include expected and non-expected utility theories, models of strategic communication, and information design. Students will apply the theoretical tools to understand and improve real world institutions, such as employee feedback systems and transparency in organizations.
IPEN 5250
Text Analysis and Machine Learning
3 Credit(s)
Description
This course serves as an applied introduction to machine learning methods for text analysis. Several approaches on text data management and analysis will be covered in this course including basic natural language processing techniques, document representation, text categorization and clustering, document summarization, sentiment analysis, social network and social media analysis, probabilistic topic models and text visualization.
IPEN 5300
Experimental Economics and Organizational Behavior
3 Credit(s)
Description
This course introduces the methodology of experimental economics and related behavioral theories, with an emphasis on social-psychological elements of preference and organizational design. Experiments studied will include ones based on the prisoners’ dilemma, dictator game, ultimatum game, and especially the public goods game and the trust game, along with more complex designs for studying institutional and organizational problems such as creation of centralized punishment schemes and secure property.
IPEN 5900
Policy and Technology for Carbon Neutrality
3 Credit(s)
Description
All industries in China are actively taking effective actions to develop new and clean technologies in order to achieve the carbon peak and neutrality goal of shouldering the common destiny of human beings. This course examines the scientific, technological, and policy approaches that China and the rest of the world can take to achieve carbon peak and carbon neutrality.
UGOD 5020
Quantitative Social Science
3 Credit(s)
Description
This course builds on the knowledge of the linear regression models to introduce students advanced statistical methods to analyze survey, administrative and other types of data of interest to quantitative social scientists. The introduction of statistical methods is integrated into research contexts and designs from a holistic framework and bridge quantitative social science and computational social science (data science). Topics include measurement, prediction, causal inference, natural experiment and program evaluation (difference-in-differences, panel data, instrumental variables, regression discontinuity), applied to both survey and big data.
- Additional Foundation Courses
Individual students may be required to take foundation courses to strengthen their academic background and research capacity in related areas, which will be specified by the Program Planning cum Thesis Supervision Committee. The credits earned cannot be counted toward the credit requirements.
- Graduate Teaching Assistant Training
PDEV 6800
Introduction to Teaching and Learning in Higher Education
0 Credit(s)
Description
The course is designed to strengthen students’ competence in teaching. It comprises 2 parts: Part 1 aims to equip all full-time research postgraduate (RPg) students with basic teaching skills before assuming teaching assistant duties for the department. Good teaching skills can be acquired through learning and practice. This 10-hour mandatory training course provides all graduate teaching assistants (GTA) with the necessary theoretical knowledge with practical opportunities to apply and build up their knowledge, skills and confidence in taking up their teaching duties. At the end of the course, GTAs should be able to (1) facilitate teaching in tutorials and laboratory settings; (2) provide meaningful feedback to their students; and (3) design an active learning environment to engage their students. In Part 2, students are required to perform instructional delivery assigned by their respective departments to complete this course. MPhil students are required to give at least one 30-minute session of instructional delivery in front of a group of students for one term. PhD students are required to give at least one such session each in two different terms. Graded PP, P or F.
All full-time RPg students are required to complete PDEV 6800. The course is composed of a 10-hour training offered by the Institute of Educational Innovation and Practice (IEIP), and session(s) of instructional delivery to be assigned by the respective departments. Upon satisfactory completion of the training conducted by IEIP, MPhil students are required to give at least one 30-minute session of instructional delivery in front of a group of students for one term. PhD students are required to give at least one such session each in two different terms. The instructional delivery will be formally assessed.
- Professional Development Course Requirement
PDEV 6770
Professional Development for Research Postgraduate Students
1 Credit(s)
Description
This course aims at equipping research postgraduate students with transferrable skills conducive to their professional development. Students are required to attend 3 hours of mandatory training on Professional Conduct, and complete 12 hours of workshops, at their own choice, under the themes of Communication Skills, Research Competency, Entrepreneurship, Self‐Management, and Career Development. Graded PP, P or F.
Students are required to complete PDEV 6770. The 1 credit earned from PDEV 6770 cannot be counted toward the credit requirements.
PhD students who are HKUST MPhil graduates and have completed PDEV 6770 or other professional development courses offered by the University before may be exempted from taking PDEV 6770, subject to prior approval of the Program Planning cum Thesis Supervision Committee.
SOCH 6780
Professional Development in Innovation, Technology, and Social Responsibility
1 Credit(s)
Description
This one credit course is intended to provide basic professional training to research postgraduate students in the Society Hub. The course will begin with lectures and a workshop on ethics in research. Students will be asked to focus on a particular theme of their choice that links technological innovation to various social and policy issues, conduct analysis and present their findings. They will also need to work in a team and learn to effectively communicate their ideas in informal and formal settings. Graded PP, P or F.
Students are required to complete SOCH 6780. The 1 credit earned from SOCH 6780 cannot be counted toward the credit requirements.
PhD students who are HKUST MPhil graduates and have completed SOCH 6780 or other professional development courses offered by the University before may be exempted from taking SOCH 6780, subject to prior approval of the Program Planning cum Thesis Supervision Committee.
- English Language Requirement
LANG 5000
Foundation in Listening & Speaking for Postgraduate Students
1 Credit(s)
Description
For students whose level of spoken English is lower than ELPA Level 4 (Speaking) when they enter the University. The course addresses the immediate linguistic needs of research postgraduate students for oral communication on campus using English. To complete the course, students are required to attain at least ELPA Level 4 (Speaking). Graded P or F.
Full-time RPg students are required to take an English Language Proficiency Assessment (ELPA) Speaking Test administered by the Division of Language Education before the start of their first term of study. Students whose ELPA Speaking Test score is below Level 4, or who failed to take the test in their first term of study, are required to take LANG 5000 until they pass the course by attaining at least Level 4 in the ELPA Speaking Test before graduation. The 1 credit earned from LANG 5000 cannot be counted toward the credit requirements.
DLED 5001
Communicating Research in English
1 Credit(s)
Students are required to take DLED 5001. The credit earned cannot be counted toward the credit requirements. Students can be exempted from taking this course with the approval of the Program Planning cum Thesis Supervision Committee.
- Postgraduate Seminar
FTEC 6101
FinTech Program Seminar
0 Credit(s)
Description
Advanced seminar series presented by guest speakers and faculty members on selected topics in Financial Technology. This course is offered every regular term. Graded P or F.
MPhil: Students are required to complete FTEC 6101 for at least two terms.
PhD: Students are required to complete FTEC 6101 for at least four terms.
- PhD Qualifying Examination
PhD students are required to pass a qualifying examination to obtain PhD candidacy following established policy.
- Thesis Research
FTEC 6990
MPhil Thesis Research
0 Credit(s)
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.
FTEC 7990
Doctoral Thesis Research
0 Credit(s)
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.
MPhil:
- Registration in FTEC 6990; and
- Presentation and oral defense of the MPhil thesis.
PhD:
- Registration in FTEC 7990; and
- Presentation and oral defense of the PhD thesis.
Last Update: 20 July 2023
To qualify for admission, applicants must meet all of the following requirements. Admission is selective and meeting these minimum requirements does not guarantee admission.
1. General Admission Requirements of the University
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Applicants seeking admission to a master's degree program should have obtained a bachelor’s degree from a recognized institution, or an approved equivalent qualification;
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Applicants seeking admission to a doctoral degree program should have obtained a bachelor’s degree with a proven record of outstanding performance from a recognized institution; or presented evidence of satisfactory work at the postgraduate level on a full-time basis for at least one year, or on a part-time basis for at least two years.
2. English Language Admission Requirements
Applicants have to fulfill English Language requirements with one of the following proficiency attainments:
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TOEFL-iBT: 80*
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TOEFL-pBT: 550
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TOEFL-Revised paper-delivered test: 60 (total scores for Reading, Listening and Writing sections)
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IELTS (Academic Module): Overall score: 6.5 and All sub-score: 5.5
* refers to the total score in one single attempt
Applicants are not required to present TOEFL or IELTS score if
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their first language is English, or
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they obtained the bachelor's degree (or equivalent) from an institution where the medium of instruction was English.
Admission to HKUST(GZ)
Apply online before the application deadlines.
Application Fee
RMB150
* All international students are required to obtain a student visa (X visa) for studying in China’s mainland. For details on student visa (X visa) requirements, please click here.
