金融学理学硕士项目
Master of Science Programme in Finance
CN
Full-time
Programme Overview

The programme aims to cultivate exceptional, high-skills professionals with both deep theoretical training and practical capabilities. Courses are led by a combination of world-class, international-trained faculty, and highly-accomplished senior financial practitioners. The programme offers two concentrations, FinTech and Financial Management, allowing students to develop specialized skills for the financial industry. Students may declare their concentrations with their initial application to the programme.



The FinTech Concentration emphasizes the new applications of technology in financial services. It provides both a broad and in-depth view of various Fintech topics (e.g., machine learning, blockchain, big data) and allows students to develop programming skills. The new FinTech concentration will help students learn the skills to innovate and build the financial technologies of the future. This concentration welcomes students with a quantitative aptitude from a variety of fields, including but not limited to Engineering, Mathematics, Finance and Business.



The Financial Management Concentration offers courses with a deep foundation of theory, supplemented with practical applications. This Concentration is designed to meet the needs of professionals in the rapidly changing fields of finance and investment. The curriculum provides training for a wide variety of financial careers, not only in traditional areas such as banking, mutual funds, brokerage and investment banking firms, but also in related fields of regulatory, government, and academics. This concentration is appropriate for all students who are interested in advanced training across the breath of modern Finance.

Curriculum

The programme offers two concentrations for full-time students, the FinTech Concentration (FinTech) and The Financial Management Concentration (FM).


*Complete Programme Courses (36 Credits) and Civic Education Courses (not applicable to the international students), and reach the required academic performance for graduation.

FM

Required Core Courses (4 courses, 12 Credits)

Required Concentration Courses (3 courses, 9 Credits) Elective Courses (15 Credits)= 36 Credits

FinTech

Required Core Courses (4 courses, 12 Credits)

Required Concentration Courses (4 courses, 12 Credits) Elective Courses (12 Credits)=36 Credits

Pre-term Courses (No Credits)
FM / FinTech
Accounting for Finance


Accounting is the language of business to record the business activities and to measure the financial performance of an organization. This course is designed to introduce graduate-level students to basic concepts and theories in accounting for financial professionals, enabling them to prepare financial statements and to apply accounting knowledge to analyse the financial performance and make business decision for an organization.


Introduction to Finance


This course is intended to introduce students to how markets and institutions shape the global financial system and economic policy.


Quantitative Methods

The objective of this course is to teach the basic mathematical techniques required for analysing issues in economics and finance. As a pre-term course, it is intended to refresh and consolidate your knowledge base in these areas. There are two parts of the course, fundamental methods of mathematical economics which help you to understand economic & financial models; probability and statistics which help you to conduct empirical analysis in the areas. Apart from teaching the mathematical principles, there is also an emphasis on showing the relevant applications in economics & finance of the mathematical techniques.

Required Core Courses (12 Credits)
FM / FinTech
Financial Reporting and Analysis

This course builds upon core accounting knowledge. It equips students with skills to analyze and interpret financial statement data in various industries to make informed business decisions in investment and valuation. Various models will be deployed such as Dividend Discount Model, Free Cash Flows Model, Residual Income Valuation Model, Abnormal Earnings Growth Model as well as interpretation of ratios such as P/E, P/B and PEG with real business context.

Corporate Valuation & Fundamentals of Finance

This course will first cover the fundamentals of corporate finance, including organization forms of businesses, financial statement and cash flows, financial ratios analysis, discounting cash flows, risk and return, cost of capital, and capital budgeting. The course will then cover corporate valuation using the discounted cash flow (DCF) method, including its application in the real world. The course will also cover several special topics such as capital structure, mergers and acquisitions, dividend policy, and bankruptcy. Real‐world applications of corporate theories will be emphasized throughout the course.

Derivatives Markets

The focus of the course is about the financial market basic instruments and derivatives (i.e. forward. Futures and options). It covers the financial market overview, fixed-income instruments, equity derivatives, foreign exchange instruments, commodities products, credit derivatives and structured products.

The lecturer will endeavor to bridge the gap between the theory and the practice in the financial market. After the course, the students are well prepared to work in financial industry as trader, structurer, sales or risk manager.

Investment Management and Analysis

This course provides an introduction to theory and practice of investment management and analysis. Major topics include risk and return, equity markets, asset allocation, portfolio optimization, factor models, and investment performance evaluation.

Required Concentration Courses (12 Credits)
FinTech
FinTech Analytics: Advanced Programming and Big Data

The course presents students with a fundamental understanding of operating principles and provides hands-on experience with mainstream big data computing systems.

The first part of the course aims to build skills in data manipulation using programming languages, with emphasis on newer programming languages and open-source platforms for Big Data processing. The second part focuses on big data analytics. Course topics include both theoretical and practical issues, and introduces students the concept and challenge of big data applications in finance. Students will develop a solid background in the fundamentals of provisioning, programming and application of big data system and software within a finance context.

FinTech Theory and Practice

This course covers the applications of new technologies in financial services such as big data, blockchain, machine learning and artificial intelligence (AI). Students are expected to develop a broad understanding of the recent FinTech developments and the new forms of financial services such as peer-to-peer lending and crowdfunding, robo-advisors, InsurTech and cryptocurrencies. In addition, we will discuss the regulatory challenges and privacy concerns that emerge as part of the FinTech transformation. Representatives from financial firms, big technological companies or insurance companies will be invited to share with the class the recent development in FinTech in the industry.

Artificial Intelligence for FinTech

This course provides an overview of Artificial Intelligence and machine learning applications in finance. The course topics include FinTech areas of crowdfunding, Linear Machine Learning models, deep learning, quantitative investing, the democratization of trading and investments, and etc. The application cases of Artificial Intelligence in FinTech will be assigned to students as course projects.

Blockchain in Finance

This course is designed for students with an interest in the applications of blockchain technology for finance.

The course topics include blockchain design principles, blockchain ecosystem, cryptocurrencies, applications of blockchain on the financial services, and etc. The application cases of blockchain technology for finance will be assigned to students as course projects.

Students are expected to be familiar with programming in Python and Linear Algebra.

FM
Spreadsheet/VBA Modelling in Finance

This course will focus on spreadsheet modelling in computational finance.

It’s designed to teach students to use Excel and VBA for building models and solving financial problems, including portfolio analysis, company evaluation, and option pricing. The course is implementation oriented; we will use a wide range of examples and case studies to help students to apply the skills to solve real-life problems.

Students will also learn to leverage the use of Python for spreadsheets.

Analysis of Fixed-income Securities

This course introduces the analytical tools and concepts needed to price fixed income securities. Topics include the pricing and hedging of bonds, inflation-indexed bonds, derivatives, and other types of fixed income securities. Emphasis will be placed on the student’s ability to price these securities by appropriately discounting future cash flows for time and risk.

Elective Courses (12 Credits)

Students can choose a required concentration course of each other as an elective course

FinTech (4 courses) / FM (5 courses)
Contemporary Issues in Chinese Financial Development (Practitioner Workshops)

The course aims to broaden students’ perspective on the many contemporary issues in Chinese financial development. Practitioner speakers will work with students on both classroom case presentations and projects. By offering students opportunity to integrate theories with existing finance practices, the course helps students gain practical insights in the latest trends of corporate finance and investment management in China.

(* This course is conducted mainly in Chinese in the form of workshops hosted by local financial industry executives.)

Behavioral Finance

Over the past several decades, the field of finance has developed a successful paradigm based on the notions that investors and managers were generally rational and the prices of securities were generally “efficient.” In recent years, however, anecdotal evidence as well as theoretical and empirical research has shown this paradigm to be insufficient to describe various features of actual financial markets.

In this course we will use psychology and more realistic settings to guide and develop alternative theories of financial markets. We will examine how the insights of behavioral finance complement the traditional paradigm and shed light on investors' trading patterns, the behavior of asset prices, corporate finance, and various Wall Street institutions and practices.

Stochastic Models

This course introduces basic techniques for modelling and analysing systems in the presence of uncertainty. It will cover probability preliminaries, Martingales, Brownian motions and applications in financial engineering.

Chinese Financial System

Chinese economy has been growing at an unprecedented speed in the past three decades and it is already the world’s second largest economy. Meanwhile Chinese financial markets are also developing rapidly as the country’s leadership pushes forward its liberalization process and integration with the rest of the world. This course aims to provide an in-depth coverage of Chinese financial system, with a focus on its distinct characteristics. The objective is to understand the role provided by the financial system in Chinese economic development, as well as the investment opportunities and risk presented by the system to the outside world.

Banks and Financial Institutions

The course examines banks, insurance companies and investment funds. Topics include the role of intermediation, the part played by banks in the real economy, regulation and management of banks, valuation of financial institutions, the functions and conduct of insurance companies, behavior and management of mutual funds and hedge funds. The course also covers corporate credit analysis, credit scoring modelling as well as the various credit risk management practices.

Microstructure and Algorithm Trading

This course introduces the foundations of securities trading and discusses market microstructure and optimal trading strategies. It covers the nature of markets and prices, trading mechanism, market microstructure models, trading costs and optimal trading strategies and high frequency trading.

Credit Risk Modelling and Products

The course introduces credit risk modeling and credit derivatives evaluation and management. It covers structural models of default risk, intensity-based modelling, risk structures of interest rates; credit default swaps, CDOs and related products.

Quantitative Risk Management

This course provides students with an in-depth understanding of financial risks by building mathematical models in order to understand the nature of these risks and to manage them using relevant products and instruments. Topics include market risk such as interest rate risk, credit risk, foreign exchange risk at trade level and portfolio level. The course also covers Value at Risk, liquidity risk and an introduction to bank regulations.

Alternative Investment

This course combines theory and practice (with most case studies drawn from Chinese capital markets) to explain alternative assets and their investment management knowledge. The course will analyze how to conduct alternative asset allocation from the perspective of institutional investment managers (such as pension funds, endowment funds, family offices, etc.). Through this course, students will learn about the returns, risks, characteristics and investment strategies of classic alternative asset such as hedge funds, private equity funds, real estate and commodities. Meanwhile, they will also understand, as institutional investors, how to allocate alternative assets properly.

(* This course is conducted mainly in Chinese.)

Internet Finance

The course provides the tools necessary to analyze the opportunities and potential competitive threats in commercial Web-based organizations. To quantify and apply the analysis, a particular focus is on valuing Internet companies based on a careful examination of their business model and environment. The course also covers the basic theory of financial intermediation as it applies to online financial service firms. It discusses the impact of a migration to online financial services and the competitive changes created.

Machine Learning for Business

This course provides introductory knowledge and systematic training for students to explore the field of machine learning which includes basic concepts, methodologies and application tools. The topics will cover the major tasks and major models, tree based models, deep learning models, and etc. Students choosing this course are expected to have a basic foundation of probability theory, matrix algebra and statistics.

Risks and Insurance

This course aims to provide students with the general principles and practices of insurance, with an emphasis on risk analysis. The topics will include the economic theory of insurance, the major types of insurance products, the insurance environment, operations of insurance companies, insurance-related regulations, insurance clauses and related topics. By completion of this course, students will have fundamental knowledge of risk management from insurance product perspective.

Predictive Analytics Using Big Financial Data
This course provides students with the skills to use predictive analytics and Big Data to make informed decisions in the financial sector. It covers topics such as the Fama and French asset pricing model, event studies, forecasting future earnings, and machine learning and textual analysis. Students will learn SQL coding skills necessary to manipulate large-scale financial databases and build predictive models. The course is a hybrid of a traditional seminar course and a computer science course, and is beneficial for students pursuing a variety of careers in the financial sector.
Green Bonds and ESG Practices

The course is amid at introducing and basic learning of  the green bond products and related markets, in respect of the ESG principles, framework, policies, and finance. The course helps to gain understanding of how a green bond is designed, structured, priced and traded, and what the relevant investment characteristics are. The course also combines some basic scientific information and knowledge on climate changes and other environment issues, as well as on modern corporate ESG practices with actual case studies. The course requires some fundamental knowledge on the bond math and bond market. A good understanding of modern environmental issues and scientific backdrop is also essential.

Directed Research in Finance

This course prepares students to engage in productive and original research in the broad area of finance under the instruction of leading professors.

Valuation Analysis

The objective of this course is to value real companies. We start with the foundations and theories of value and pricing, but we focus on real-world applications. This course is designed for potential investors, investment bankers, traders, business owners and managers who intend to understand the value of real companies. Throughout the semester, we seek to understand how to value small firms and large firms, young firms and matured firms, firms with simple or complex businesses and firms in distress. We will apply finance and accounting concepts taught in other courses to value real companies.

Emerging Companies Finance

The course covers financial topics most relevant to newly formed companies, with an emphasis on innovative startups that target large markets and raise outside capital. Includes topics on: (1) valuation, which is the course’s primary theme, underlying all of the topics covered, (2) evaluating business opportunities, which focuses on the underlying economic principles that differentiate large opportunities from small opportunities, (3) funding business opportunities, which covers both identifying a company’s needs and acquiring the capital to finance those needs, and (4) discussing how successful entrepreneurial ventures “exit.”

Quantitative Portfolio Analysis

The course covers quantitative portfolio management techniques and strategies with hands-on computing using Python. It is specially designed for students with a career endeavor in the quantitative asset management field or systematic trading strategies. Topics will include fundamental investment concepts, classical portfolio theories, state-of-the-art smart beta strategies formulation, factors modelling and risk optimization, active portfolio management and design, dynamic hedging etc. Programming and numerical algorithm will be a focus and students are expected to learn and apply Python to real practice problems encountered widely in the industry.

Finance within Macroeconomy

Financial decisions are made within a broader macroeconomic context. The course topic generally include: why people use financial markets, national income accounts, interest rate and central bank policy, banking, and international capital flows.

Financial Econometrics and Applications

This course covers econometrics used in empirical finance. Topics include univariate and multivariate linear models, time series models, parametric and nonparametric models of volatility, risk management models. The course considers applied problems in financial data analysis and makes extensive use of computer-based applications to draw inferences.

Information Strategy and Management

This course emphasizes on the object-oriented analysis techniques, examines economic principles of information systems strategy, and highlights applications of economic and management principles to the unique environment of information services. Various key business activities will be discussed. This course will also explain the importance of business IS controls in contemporary IT governance.

Business Modeling and Optimization    

This course presents modelling techniques in optimization that are known as linear programming, integer programming, and nonlinear programming. Applications to managerial decision problems in different industries including finance, accounting, human resources, and marketing will be discussed. Students can model managerial decision problems using spreadsheet optimization, e.g., Excel and Excel Solver.

Mining Massive Datasets

This course will survey state-of-the-art topics in Big Data, looking at data collection (smartphones, sensors, the Web), data storage and processing (scalable relational databases, Hadoop, Spark, etc.), extracting structured data from unstructured data, systems issues (exploiting multicore, security), analytics (machine learning, data compression, efficient algorithms), visualization, and a range of applications.

Theory of Statistics

A comprehensive advanced course in the theory of statistics covering topics in random variable, probability, distributions, hypothesis testing, linear regression models, matrix algebra, optimization and other essential topics. This course is intended for students considering a career in quantitative modelling or data analysis. Apart from discussing the statistical principles, the emphasis of this course is on the relevant and practical applications in economics and finance of these statistical techniques.

*Subject to the actual courses. Our programme reserves the right of final interpretation for the courses and content hereinabove.

Application Requirements

1、Graduated from a recognized university and obtained a Bachelor's degree, normally with honours not lower than Second Class; or Graduated from an honours programme of a recognized university with a Bachelor's degree, normally achieving an average grade of not lower than "B" ; or Completed a course of study in a tertiary educational institution and obtained professional or similar qualifications equivalent to an honours degree.

2、To fulfill the University’s minimum English language requirements for admission to postgraduate programmes, applicants should have:

  1. obtained a degree from a university in Hong Konga or taken a degree programme of which the medium of instruction was English; or
  2. achieved scores in the following English Language testsb as indicated:
  3. TOEFL: 550 (Paper-based)/79 (Internet-based);

    IELTS (Academic): 6.5;

    GMAT: Band 21 (Verbal); or

  4. obtained a pass grade in English in one of the following examinations:
  5. Hong Kong Advanced Level Examination (AS Level);

    Hong Kong Higher Level Examination;

    CUHK Matriculation Examination;

    General Certificate of Education Examination (GCE) Advanced Level (A-Level) /Advanced Subsidiary Level (AS-Level)

  6. achieved Level 4 or above in the English Language subject of the Hong Kong Diploma of Secondary Education (HKDSE) Examination; or
  7. obtained a recognized professional qualification, provided that the examination was conducted in English.

  8. a. This is based on the understanding that English is the medium of instruction of degree programmes offered by universities in Hong Kong. Moreover, graduates from universities in Hong Kong should have fulfilled the English language requirements of the institution concerned when they were admitted to the degree programmes. The CUHKSZ Graduate School may request applicants to provide additional supporting documents to prove their English proficiency.

    b. TOEFL and IELTS scores are considered valid for two years from the test date. GMAT scores are considered valid for five years from the test date.

Required Documents

Applicants should submit the following supporting documents:

  1. A copy of Bachelor’s and/or Master’s Degree Certificate and Graduation Certificate, or Student Status Certificate;
  2. A copy of Official transcript(s) with grading system;
  3. A copy of TOEFL/IELTS/GMAT score sheet;
  4. Curriculum Vitae;
  5. Personal Statement;
  6. Two Reference Letters;
  7. A copy of ID card;
  8. A copy of Professional Certificates (if applicable);
  9. Other supporting documents (Honor certificates etc.)


Applicants shall submit their applications by the deadlines. Once the applicants’ files are received, the Admission Panel will review them and render its decisions.

Application Deadline

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Round Application Date Assessment
1 2023.9.01~2023.09.19 Written exam and Interview will be notified by email. Interview will be notified by email after passing the written exam, the specific time is subject to email notification
2 2023.09.20~2023.10.31
3 2023.11.01~2023.12.10
4 2023.12.11~2024.01.14
5 2024.01.15~2024.02.26
6 2024.02.27~2024.03.12
7 2024.03.13~2024.04.14
8 2024.04.15~2024.05.31
Tuition Fee and Scholarships

Normative study period: Two Years.

The tuition fee for the entire programme (2024 entry) is RMB 288,000.

*The tuition fee is for the full 36 credits of the major. The total tuition fee is based on the current year of enrollment. In addition, according to the relevant regulations of the Ministry of Education and others, schools that have been approved to implement a credit-based fee system may charge fees for taking additional courses in other majors or retaking courses according to the credit-based fee rates specified for the courses taken.

Scholarships:

  1. Merit-based entrance scholarships are granted to qualified candidates who may receive tuition waiver up to 100%;
  2. Scholarships for Excellence will be awarded subject to satisfactory academic performance.
Application Method

Applicants should submit their applications and upload documents via the Online System for Applicants of Postgraduate Programmes.

Application website is: https://pgapply.cuhk.edu.cn/, applicants are required to pay a non-refundable application fee of RMB 300.

Master of Science Programme in Finance
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