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Fundamental Core Courses (Four Courses)

This course aims to provide candidates with understanding of (i) fundamental approaches for equity valuation, (ii) fundamental approaches for valuation of fixed income securities, (iii) the knowledge about corporate finance and behavioral approaches in asset valuation, and (iv) the recent development of valuation techniques.  On the theoretical side, this course introduces fundamental knowledge for asset valuation, investment strategies, and portfolio management.  On the practical side, this course covers recent topics that are related to the asset valuation techniques used in both Hong Kong and United States.  Some projects about asset valuation are specially designed to let candidates apply the theoretical knowledge into practice.  This course is highly recommended for candidates who intend to pursue a career or further studies in equity valuation and securities analysis.  Certainly, the knowledge from this course will also be very useful when you make your own personal investment decision. 

This course aims to provide candidates with understanding of (i) fundamental knowledge for asset valuation, (ii) portfolio management techniques for risk management and speculation, (iii) investment strategies adopted in financial market, and (iv) the recent development of portfolio management tools and investment strategies.  On the theoretical side, this course introduces fundamental knowledge for asset pricing, investment strategies, and portfolio management.  On the practical side, this course covers recent topics that are related to the investment strategies and portfolio management in both Hong Kong and United States.  Some projects about portfolio management and asset valuation are specially designed to let candidates apply the theoretical knowledge into practice.  This course is highly recommended for candidates who intend to pursue a career or further studies in investment strategies and portfolio management. Of course, the knowledge will also be very useful when candidates make their own personal investment decision. 

In this course, students will learn the rationales behind the design of blockchain and cryptocurrency, the key technical/cryptographic elements that build up the blockchain technology, classifications of different types of blockchains, the comparisons of different blockchain platforms, what applications fit the best for the blockchain technology, and example applications in a wide range of disciplines.  This course will also introduce some popular cryptocurrencies, e.g. Bitcoin, discuss in details about bitcoin transactions, briefly introduce what a cryptocurrency exchange is, and the evil sides of cryptourrencies (e.g. being the ransoms of ransomeware and money laundry). 

 

The course will examine, from legal and policy perspectives, the fundamentals respecting regulation of the primary financial intermediaries and markets: i.e., money and banking, investment banking, and asset management and insurance.  Emphasis will be on the on-going phenomenon of globalisation and interdependence/interconnection of financial markets and intermediaries, and the need for economies to develop viable and robust financial markets, with a particular focus on the current global financial crisis. Use of international, comparative (especially PRC, US and EU) and interdisciplinary materials will be made. 

Advanced Core Courses (Five Courses)

Derivatives have become a popular hedging and investment tool over the last few decades and derivatives concept are required for every advanced finance topic. This course provides candidates with a framework (1) to understand the fundamental concepts of derivative products (forward and futures, options, swaps, and basic structured products), (2) to develop the necessary skills used in valuing derivative contracts, and (3) to understand a wide variety of issues related to risk management and investment decisions using derivatives. The course intends to provide a solid foundation for other 2 advanced courses of the program such as mathematical finance, risk management, fixed income securities, and financial engineering.

There are three main approaches to mathematical finance: the tree approach, the martingale approach and the partial differential equation approach.  This course will present these three approaches and their applications to pricing and hedging financial derivatives.  The corresponding numerical methods of the three approaches are lattice method, Monte Carlo simulation method, and finite difference method. Along the lectures, necessary mathematics, such as calculus, partial differential equation, applied probability and stochastic calculus will also be reviewed.  After taking this course, candidates should be able to fully understand no-arbitrage theory, risk-neutral probability, martingale, and Black-Scholes equation.  The purpose of this course is to lay down a solid mathematical foundation for candidates to learn more advanced topics in financial engineering and risk management, such as exotic options, interest rate derivatives and credit risk models.  

Prerequisite: MFIN6003 Derivative Securities 

This course provides students a foundation in managing and analyzing financial datasets as well as other datasets.  The first part of the course focuses on building skills – data manipulation using programming languages. The second part introduces various financial databases. Through practice on real-world financial datasets, students will learn methods used to warehouse and retrieve data for statistical computing.  The course then turns to analytical methods with a focus on demonstrating these methods on real-data from various contexts in finance. Methods covered include statistical modeling and inference, machine learning, textual analysis, classification and alternative datasets.  Problem sets and projects will be the primary mode of learning.  Course learning will be supplemented with exposure to industry speakers from the local financial industry. 

Prerequisite: MFIN7005 Corporate Finance and Asset Valuation 

Machine learning and artificial intelligence are the apex technologies of the information era.  These methods are getting increasingly popular in the financial market.  This course provides students the fundamental models and methods of machine learning and apply them to solve real-world financial problems. The topics include regression, classification, clustering methods, model selection, topic modeling and policy search.  The first part of the course focuses on supervised learning techniques for regression and classification.  The second part of the course covers unsupervised learning techniques for clustering and matrix factorization.  The third part of the course covers reinforcement learning algorithm.  The last part provides the fundamental concepts of artificial intelligence and its implications.  The course provides introductions to the latest datasets in financial markets and practices applying learning algorithms to these datasets in a variety of topics.  The primary mode of learning is based on assignments and projects.

This course provides a foundation for advanced quantitative trading in financial markets.  The course has two parts.  First, the course reviews stylized facts and methods used for time-series predictability, cross-sectional asset pricing and strategy performance evaluation.  The second part of the course uses these tools to study recent advances in investment strategies sourcing from academic and practitioner literature.  For example, the course will discuss new theories on risk premia, intermediation-based asset pricing, and quantifiable soft information and alternative data.  The primary method of learning will be a combination of problem sets and projects.  Subject to availability, learning will be supplemented with exposure to industry speakers from the local financial industry. 

Prerequisite: MFIN7002 Investment Analysis and Portfolio Management

Capstone Course

This course provides students a foundation in managing and analyzing large datasets for applications in finance.  The first part of the course focuses on building skills – data custodianship and performance computing. Through practice on real-world financial datasets, students will learn methods used to warehouse and retrieve data for high-performance statistical computing. The course then turns to analytical methods with a focus on demonstrating these methods on real-data from various contexts in finance.  Methods covered include statistical modeling and inference, machine learning, textual analysis, classification and alternative datasets.  Problem sets and projects will be the primary mode of learning.  Course learning will be supplemented with exposure to industry speakers from the local financial industry. 

Prerequisite: MFIN7002 Investment Analysis and Portfolio Management