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FINANCIAL
TECHNOLOGY

> THRUSTS > FTEC > Mphil/PhD Program
PHD OVERVIEW
Program Name
  • Master of Philosophy in Financial Technology
  • Doctor of Philosophy in Financial Technology
Program Short Name
  • MPhil(FinTech)
  • PhD(FinTech)
Mode of Study
  • Full-time
  • 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
Program Advisor
  • Program Head: Prof Ning CAI
Introduction

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.

Learning outcomes
On successful completion of the MPhil program, graduates will be able to:
MPhil
01
Identify and synthesize current research in FinTech;
02
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;
03
Analyze, design, and execute FinTech research by utilizing proper research methodologies; and
04
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:
PhD
01
Identify and synthesize current research in FinTech;
02
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;
03
Analyze, design, and execute FinTech research by utilizing proper research methodologies;
04
Communicate the developed FinTech knowledge and research with the academic and practitioner community; and
05
Create original, substantive, and impactful knowledge to advance the state of FinTech research and practice.
Note:

For MPhil in FTEC, please visit https://vptlo.hkust-gz.edu.cn/ or contact rbmadmit@hkust-gz.edu.cn for details.

PHD PROGRAM SPECIFICS
Program Progression
First Year coursework
Qualifying Written Exam Fin+Tech
Qualifying Oral Exam
First year survey paper + oral presentation
PhD candidacy
Other required coursework
Thesis and oral defense
PhD degree conferred
Curriculum

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.

1
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.

UCMP 6050

Project-driven Collaborative Design Thinking

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 MPhil students are required to complete UCMP 6050.

All PhD 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.

PhD students who are HKUST(GZ) MPhil graduates and have completed UCMP 6010, UCMP 6030 or UCMP 6050 before may be exempted from this requirement, subject to prior approval of the Program Planning cum Thesis Supervision Committee.

2
Two 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.

3
Program Required Courses

Required Course

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.

PQE Courses

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 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 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 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.

* PQE required. Register in the first year.

4
PhD Qualifying Examination (PQE)

The objective of the PhD Qualifying Examination is to assess students’ general knowledge of Fintech area and understanding of fundamental principles, as well as the ability to apply these principles to solve problems. The PQE provides an early assessment of students’ potential to meet the academic requirements to obtain their doctoral degree. PhD students are required to pass the qualifying examination with a maximum of two attempts within 22 months after admission into PhD study.

The PQE at the Fintech Thrust consists of two components: (1) a written comprehensive examination, and (2) a critical survey paper.

PQE guidelines:

Fintech PQE Guidelines

Detailed Guidelines for year 21-22

Detailed Guidelines for year 22-23

Detailed Guidelines for year 23-24

Detailed Guidelines for year 24-25

5
Program Elective Courses

To meet individual needs, students will be taking courses in different areas, which may include but not limited to courses and areas listed below.

Sample Elective Course List

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 5061

Stochastic Calculus for Financial Technology

3 Credit(s)

Description

This is a graduate level course in stochastic calculus for MPhil/PhD students in Financial Technology and other related fields. This course aims to provide a rigorous mathematical introduction to the tools of stochastic calculus used in derivative pricing and financial modeling. Topics include Brownian motion, stopping times, stochastic integral, Itô’s formula, stochastic differential equations, martingales, Girsanov’s theorem, option pricing, etc.

FTEC 5120

Text Mining in Finance and Economics

3 Credit(s)

Description

This course provides an introduction to textual analysis in social science research. It covers text mining models and related statistical tools, including Dictionary Method, SVD, Word2Vec, WEAT, Probabilistic Modeling, Regression Modeling, and Large Language Models in modern social science research. Applications related to the financial market are emphasized, including macro-finance, empirical asset pricing, empirical corporate finance, and ESG.

FTEC 5210

Quantitative Models for Financial Derivatives

3 Credit(s)

Description

This course covers basic pricing theory of financial derivatives and risk hedging of exotic options. The course starts with the fundamental theorem of asset pricing and risk neutral valuation principle. The renowned Black-Scholes pricing theory and martingale pricing theory are introduced. Advanced topics include exchange options, quanto options, implied volatility and VIX.

FTEC 5220

Monte Carlo Simulation in Finance

3 Credit(s)

Description

This course covers Monte Carol simulation methods from the perspectives of derivatives pricing, credit risk modeling and trading strategies. The first topic starts with various sampling methods for generating random variables, like the basic inverse transform method and acceptance-rejection method. Special emphasis is placed on simulation of normal distributions. Next, we consider pricing financial derivatives via simulation. The dynamic price processes include the Geometric Brownian motion and jump diffusion models. Various variance reduction techniques, like the antithetic variate, control variate, conditioning and stratified sampling are considered. The solution of the optimal stopping model of an American option via the Longstaff-Schwartz regression method is discussed. We also consider rare event simulation via various importance sampling methods, like the mean drift method and cross entropy method. Applications in risk measures calculation in credit risk models, like the Gaussian copula models, are considered.

FTEC 5230

Financial Risk Management

3 Credit(s)

Description

This course shows the use of various quantitative techniques and financial engineering principles in the management and modeling of financial risks. The topics include hedging of market risks, immunization of bond risks, Value-at-Risk and expected shortfall, credit yield curve model, credit derivatives pricing, and default correlation models.

FTEC 5310

Blockchain Technology

3 Credit(s)

Description

This course offers a thorough introduction of blockchain technology. Students will explore both technical and non-technical aspects of blockchain, including but not limited to cryptographic techniques, consensus mechanisms, smart contracts, regulatory implications, and real-world applications. Topics covered in this course include the historical development of blockchain, Bitcoin protocol, Ethereum, applications of blockchain, such as decentralized finance, and regulation discussions.

FTEC 5320

Decentralized Finance

3 Credit(s)

Description

This course introduces various novel types of financial instruments enabled by blockchains, collectively known as Decentralized Finance (DeFi). Students will delve into key DeFi elements, gaining both theoretical knowledge and practical skills to effectively navigate and engage with DeFi platforms and protocols.This course introduces various novel types of financial instruments enabled by blockchains, collectively known as Decentralized Finance (DeFi). Students will delve into key DeFi elements, gaining both theoretical knowledge and practical skills to effectively navigate and engage with DeFi platforms and protocols.

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.

6
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.

7
Graduate Teaching Assistant Training

All full-time RPg students are required to complete PDEV 6800. The course is composed of a 10-hour training offered by the Center for Education Innovation (CEI), and session(s) of instructional delivery to be assigned by the respective departments. Upon satisfactory completion of the training conducted by CEI, 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.

PDEV 6800

Introduction to Teaching and Learning in Higher Education

0 Credit(s)

8
Professional Development Course Requirement 

PDEV 6770

Professional Development for Research Postgraduate Students

1 Credit(s)

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 Committee.

SOCH 6780

Professional Development in Innovation, Technology, and Social Responsibility

1 Credit(s)

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 Committee.

9
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)

Description

This course aims to help research postgraduate students to develop skills they need to understand how to successfully communicate research in English to academic, cross-disciplinary and non-specialist audiences. Students who fail to satisfy the University's English language requirement are advised to complete LANG 5000 before enrolling for this course. Graded P or F.

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.

10
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:

  1. Registration in FTEC 6990; and
  2. Presentation and oral defense of the MPhil thesis.

PhD:

  1. Registration in FTEC 7990; and
  2. Presentation and oral defense of the PhD thesis.
PHD ADMISSION
Admission Requirements
1
English Language Admission Requirements

Applicants have to fulfill English Language requirements with one of the following proficiency attainments:

TOEFL-iBT: 80*

TOEFL-pBT: 550

TOEFL-Revised paper-delivered test: 60 (total scores for Reading, Listening and Writing sections)

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

their first language is English, or

they obtained the bachelor's degree (or equivalent) from an institution where the medium of instruction was English.

To qualify for admission, applicants must meet all of the following requirements. Admission is selective and meeting these minimum requirements does not guarantee admission.

2
General Admission Requirements of the University

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;

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.

Admission and Application
1
Do I need to add any relevant publications or experience to make my application more competitive?

In terms of publications, we do not require the applicants to have publications when they apply but it will make you more competitive if you have Fintech-related research experience/publications.

2
How can I submit my reference letters? I wonder if there is a department email address that I can send letters to.

You can fill in your nominated referees’ email address on the application system and the referees will be invited by email to send their reference letter online.

3
Are GRE scores required or preferred (or not considered) when applying?

GMAT/GRE is not compulsory in applying for PhD in Fintech program. But satisfactory GMAT or GRE score will add weight to the application under consideration.

4
I am interested in the program of MPhil in Individualized Interdisciplinary Program. Can I ask some admission questions?

For MPhil in FTEC, please visit https://vptlo.hkust-gz.edu.cn/ or contact rbmadmit@hkust-gz.edu.cn for details.

5
Regarding the research proposal, may I choose the topic that I am interested in or whether your program has specified scope for the topic selection?

We do not have specific requirements on the topics for your research proposal. The key guideline of preparing your research proposal is to show innovative and original ideas that demonstrates your research potential.  

6
I uploaded two files after I officially submit the application. Will the admission committee see my updated application files?

After submissions, only the following changes are allowed:

  1. Change personal contact details,
  2. Input final GPA/average mark after program completion,
  3. Add new scores of IELTS/TOEFL or other public examinations which are not available earlier,
  4. Upload supporting documents, and
  5. Invite referees.

7
I want to know about Language Requirements. My previous education was in English and I have collected the certificate from my previous university. Is it enough for application?

You are not required to take the English Proficiency Test if you obtained a bachelor or master’s degree from an institution where the medium of instruction was English.

8
I would like to apply for a position on your Ph.D. program, when does it starts accepting applications?

Normally, early admission would begin in July of each year and the system would open to accept new applications.

9
Would research experience compulsory to meet the requirements of this program?

There is no strict requirement on research experience, but your research potential will be evaluated during the screening process.

10
When is the application deadline for PhD program?

We are keeping the application system open as we are accepting application on a rolling basis over year-round. Each admission cycle would begin in July of each year.

11
 Is the program offered in Hong Kong or Guangzhou?

The Fintech Thrust and its MPhil/PhD programs are entirely based on Guangzhou campus. All Fintech core/elective lessons will be conducted in Guangzhou campus and all lessons will be conducted during weekdays of regular academic terms.

Interview
1
Does the interview hold in English?

You may expect English as the main language used in the whole interview.

2
May I know the detailed interview process? Is there anything I need to prepare? Do I need to prepare a PowerPoint Presentation for my interview?

There is no need for PowerPoint for your presentation. We got that general information from your application file. You will be expected to talk about your research proposal.

Enquiry
PhD in Fintech
Email: ftect@hkust-gz.edu.cn
Apply Now

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