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

The Master of Science Programme in Finance is available as a part-time option. Like the full-time mode, it offers world-class faculty and accomplished senior professionals. Featuring international education standards, English immersion learning environment, and equipped with financial laboratories and other first-class teaching facilities, our programme supplements these with external industry resources and real-world experiences. Outside of the classroom, the programme offers an abundance of extracurricular activities helping students improve their personal connections, enhance their professional skills and boost their core competitiveness. Qualified graduates will be awarded the master degree from CUHK, which will be certified by the Chinese Service Center for Scholarly Exchange (CSCSE).

Curriculum

The programme offers Financial Management Concentration courses for part-time students.


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

Required Core Courses

Required Concentration Courses (3 courses, 9 Credits)

Elective Courses (15 Credits)

Before Enrollment (Around August)

* Subject to the actual courses

Pre-term Courses (No Credits)
Accounting for Finance


本课程旨在向研究生介绍会计的基本概念和理论,使学生能够了解报表的编制方法,并运用财务报表将会计知识应用于分析金融企业和进行商业决策。


Introduction to Finance


本课程介绍金融学的基本理论与实践,了解金融系统的整体框架,学习相关经济政策。系统掌握市场和机构如何形成全球金融体系。


Quantitative Methods


本课程主要学习分析金融、经济问题所需的数学技巧,巩固对数学认知基本概念,进一步加深相关知识的理解。课程分为两个部分,第一部分为学习数理经济学的基本方法,帮助理解金融和经济模型;第二部分为概率与统计,帮助学生在该领域进行实证分析。本课程除了教授数学原理外,还强调这些数学技术在金融和经济领域的相关应用。


Python for Financial Data Analysis


本课程介绍使用 Python 分析财务数据。它为学生提供实务的 Python 技能,以处理行业中常见的问题。主题将包括 Python 的基本数据结构、重要的软件包(如Pandas、Numpy、SciPy、Matplotlib 等),以及它们如何分析财务回报行为。物件导向程式设计和它的应用,以解决各种投资问题。课程末部还将介绍优化技术和一些重要的机器学习算法。


1st Academic Year
Term 1
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.


Elective Courses

Students can choose one elective course.


Term 2
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.


Elective Courses


Students can choose two elective courses.


2nd Academic Year
Term 3
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.


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.


Elective Courses


Students can choose one elective course.


Term 4
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.


Elective Courses


Students can choose one elective course.


Elective Courses (4 courses, 12 Credits)
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.
">
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.
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.

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.

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.

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 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.

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.

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.

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.

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.

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.

* Part-time Students can choose one of the following courses as an elective course

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.

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.

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.

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

*Swipe left or right to see more

Round Application Date Assessment
1 2023.09.01~2023.10.29 2023.11.04
2 2023.10.30~2023.12.03 2023.12.09
3 2023.12.04~2024.01.14 2024.01.20
4 2024.01.15~2024.02.25 2024.03.03
5 2024.02.26~2024.03.31 2024.04.06
6 2024.04.01~2024.04.30 2024.05.11
7 2024.05.01~2024.05.31 2024.06.08
Tuition Fee and Scholarships

Normative study period: Three 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;
  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