• Course Description:

    Algorithmic trading is a trending investment approach nowadays that consists of identification of trading opportunities and implementation via computer algorithms.  This course will cover emerging trend in the quantitative investment field, and introduce various data analysis techniques and methodologies that are commonly employed in the industry.

    The first half of the course focuses on financial data analysis and strategy implementation.  The second half of the course discusses practical techniques to manage and deploy algorithmic trading strategies in real financial world.

    Course Name: Mastering the markets: Financial analytics and algorithmic trading

    Course Stream: Financial Computing

    IsCEF: No

    Course Credit: 6

  • Course Description:

    This course provides an in-depth exploration of blockchain technology and distributed ledger technology (DLT) and their applications in the context of Smart Banking and Innovative Finance.  Students will gain a comprehensive understanding of the underlying principles, functionalities, and potential benefits and challenges of the emerging Financial Technology (FinTech) 3.0.

    The course will cover the emerging trend in Smart Banking and Innovative Finance with various disruptive business-IT (DLT and BlockChain) models in the evolving FinTech ecosystem such as decentralized finance (DeFi), central bank digital currencies (CBDC) and Hong Kong SAR Government’s w-CBDC and rCBDC projects, eHKD/eCNY use cases, Open Banking and API (Application Programming Interface) ecosystem, Virtual Banks and Stored Valued Facility (SVF), Banking as a Service (BaaS), Banking as a Platform (BaaP), Faster Payment System (FPS) and cross-border payment/forex applications, smart contracts, tokenization and tokenomics, WealthTech, InsurTech, Self-Sovereign Identity (SSI), Zero Knowledge Proof (ZKP), and the related regulatory considerations.

    Through lectures, case studies, in-class discussions, group presentations and reflective exercises, students will develop practical skills in designing, implementing, and managing blockchain and DLT solutions for Smart Banking and Innovative Finance.

    Course Name: Smart banking and innovative finance

    Course Stream: Financial Computing

    IsCEF: No

    Course Credit: 6

  • Course Description:

    The course demonstrates ways of implementing Generative AI in various scenarios in a financial institution.  It examines regulatory and ethical requirements as well as the opportunities from harnessing the conversational power of Generative AI for individualized content generation.  We will examine how to use GenAI to improve analytics and especially to augment human collaborators.  A qualified outlook into the future of the technology and its impact will conclude the course.

    Course Name: Generative AI in financial services

    Course Stream: Financial Computing

    IsCEF: No

    Course Credit: 6

  • Course Description:

    This course introduces the tools and technologies widely used in industry for building applications for Quantitative Finance.  From analysis and design to development and implementation, this course covers: modeling financial data and designing financial application using UML, a de facto industry standard for object oriented design and development; applying design patterns in financial application; basic skills on translating financial mathematics into spreadsheets using Microsoft Excel and VBA; developing Excel C++ add-ins for financial computation.

    Pre-requisites: This course assumes basic understanding of financial concepts covered in COMP7802.  Experience in C++/C programming is required.

    Course Name: Software development for quantitative finance

    Course Stream: Financial Computing

    IsCEF: No

    Course Credit: 6

  • Course Description:

    This course introduces the major computation problems in the field of financial derivatives and various computational methods/techniques for solving these problems.  The lectures start with a short introduction on various financial derivative products, and then move to the derivation of the mathematical models employed in the valuation of these products, and finally come to the solving techniques for the models.

    Pre-requisites: No prior finance knowledge is required.  Students are assumed to have basic competence in calculus and probability (up to the level of knowing the concepts of random variables, normal distributions, etc.).  Knowledge in at least one programming language is required for the assignments/final project.

    Course Name: Techniques in computational finance

    Course Stream: Financial Computing

    IsCEF: No

    Course Credit: 6

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