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Accounting & Finance FinTech and Financial Analytics

Postgraduate Diploma in Financial Analytics and Algo Trading
金融分析與程式交易深造文憑

Course Code
FN175A
Application Code
2465-FN175A

Credit
60
Study mode
Full-time
Start Date
16 Jan 2027 (Sat)
Duration
12 months
Language
English
Course Fee
Course Fee
HK$180,000
Enquiries
2520 4612
28610278
How to Apply

Today and Upcoming Events

Special Announcement:

 

We look forward to an exciting journey with you!

12 months full-time study in Hong Kong to obtain your “Postgraduate Diploma in Financial Analytics and Algo Trading” awarded within the HKU system through HKU SPACE. 

Applicants with other English-language qualifications may also be eligible for admission. Please contact the programme team for advice. For English Bridging Courses, please click here for more details.

Highlights

This programme aims to impart inter-disciplinary knowledge of quantitative finance and machine intelligence to students who are interested in financial analytics and algo trading. It examines contemporary elements in Environmental, Social and Governance (ESG) investing, financial risks and investment portfolios. It also discusses the applications of computational tools to analyse quantitative data and qualitative data, build financial models, perform financial analysis and text analytics to assist investment decision making. The programme illustrates the applications of artificial intelligence (AI) and machine learning to perform financial analytics as well as the usage of algo trading in implementing quantitative investment strategies.

 

 

 

 

Programme Details

 Programme Intended Learning Outcomes:

On completion of the programme, students should be able to
1. apply quantitative methods to analyse financial data and build financial models; 

2. explain the applications of artificial intelligence in financial data processing, text and financial analytics and algo trading;

3. discuss and evaluate a wide range of ESG factors and financial risks, and integrate them in the investment strategies;

4. critically interpret financial performance, optimise investment portfolios and formulate quantitative investment strategies; and

5. use computational tools to perform financial analytics, implement algo trading as well as solve finance and investment problems.

6. critically evaluate and apply interdisciplinary knowledge, concepts and professional skills across diverse intercultural, organisational and technological contexts, and to exercise informed judgment in addressing complex real-world issues; 

7. critically evaluate, integrate and apply subject knowledge and appropriate methodologies in the planning and execution of a significant project, and to communicate evidence-based findings, conclusions and recommendations in an academic/professional manner.

 

Programme Structure:

Module 1: AI and Financial Computing

Module 2: Financial Analysis and ESG Investing 

Module 3: Financial Risk Analysis and Portfolio Optimisation 

Module 4: Machine Learning for Financial Analytics 

Module 5: Web Scraping and Text Analytics in Quantitative Finance 

Module 6: Algo Trading and Quantitative Investment Strategies 

*Module 7: Executive Leadership Development

*Module 8: Intercultural Communication – Trilingualism and Hong Kong Culture

*Module 9: Intelligent Technologies for ESG Integration and Sustainable Development

*Module 10: Capstone Project

 

*Subject to Approval

 

Award

Upon successful completion of the programme, students who have passed the assessments with attendance of no less than 70% will be awarded within the HKU system through HKU SPACE a ''Postgraduate Diploma in Financial Analytics and Algo Trading'',

Application Code 2465-FN175A -

Class Details

 

 

Career Path

 

 

Teacher Information

Mr Hong Lin

Background

Mr Lin has extensive experience in Fintech development and digital transformation across Citigroup's retail and institutional businesses. Over the past three years, Mr Lin has acted as an innovator by promoting big data analysis and managing a series of automation projects, covering the whole process from streamlining to solution delivery with Automation Anywhere, Python, and VBA. His primary task has recently been to digitalize the bank's prime brokerage business risk management using data and automation technologies.
Mr Lin started his career as a business intelligence engineer, focusing on Fintech solution development and identifying sales opportunities through data analysis. In 2017, he developed AI Financial Advisory solutions by backward-engineering trading strategies and analyzing financial news using Natural Language Processing (NLP) techniques at Ping An Securities. In 2018, Mr. Lin led a market research project to optimize product lines by analyzing more than 100,000 customer reviews on the Internet using web scraping and NLP. 
Mr Lin graduated from the University of California, Davis with a Bachelor of Science degree in Managerial Economics Development under Trade and Development of Agricultural Commodities, and from the Hong Kong University of Science and Technology with a Master of Science degree in Business Analytics.

Mr. Ken Liu

Background

Mr. Liu, co-founder and CTO, is a startup focused on AI, Machine Learning and Big Data analytics. He has been a hands-on expert in his specialized area for over 10 years. Previously,  Ken worked at Citi, HSBC, Goldman Sachs, Deutsche Bank and Credit Suisse as an Algo-Trading developer. Mr. Liu earned a master's degree in Computer Science from USC and a bachelor's degree in Computer Science from the University of Warwick.

Dr. Simon Yiu

Background

Dr.Yiu has over 20 years of IT experience in quantitative algorithmic trading design (data-feed, strategy, back-test, portfolio monitoring and execution), infrastructure management (low latency trading and network, OS and application), implementation, project management to formulate and develop business strategic and operation management.

Mr. Benjamin Lee

Background

Mr. Benjamin Lee is a Chartered Financial Analyst (CFA) Holder and currently serves as the Head of Investment Department at an international financial institution. He has over 15 years of experience in asset management and served as Fund Director at a private equity fund, managing assets for international high-net-worth clients and professional investors. Mr. Lee is an EFFAS Certified ESG Analyst® (CESGA). He specializes in integrating environmental, social, and governance (ESG) and sustainable development concepts into investment decision-making. 

Dr Francis Lau

Background

Dr. Lau is a seasoned financial data analytics practitioner and a professional trainer in finance-related disciplines. He has over 22 years of experience in business planning, data analytics, management information, regulatory compliance, and risk management, gained from working with multinational analytics vendors, banks, consulting firms, and universities. Dr Lau is a subject-matter expert in applying data analytics to enhance business decision-making, constructing quantitative models to gauge business performance, and streamlining management reporting processes. In addition to industry experience, Dr Lau also has extensive experience in developing and delivering academic and professional education programs for financial institutions, professional associations, and universities. Dr Lau is a well-recognized trainer in compliance, data science,

Fee

Application Fee

HK$600 (Non-refundable)

Course Fee
  • Course Fee
    HK$180,000

Entry Requirements

Applicants shall hold a bachelor’s degree in quantitative or computational areas (e.g., economics, finance, mathematics, statistics, science, computer science, IT or engineering) awarded by a recognized institution or equivalent.

 

If the degree or equivalent qualification is from an institution where the language of teaching and assessment is not English, applicants shall provide evidence of English proficiency, such as:

  1. an overall band of 6.0 or above with no subtests lower than 5.5 in the IELTS; or
  2. a score of 550 or above in the paper-based TOEFL, or a score of 213 or above in the computer-based TOEFL, or a score of 80 or above in the internet-based TOEFL; or
  3. HKALE Use of English at Grade E or above; or
  4. HKDSE Examination English Language at Level 3 or above; or
  5. equivalent qualifications.

 

Applicants without the above qualifications but have substantial work experience will be considered on individual merit.

Applicants who do not have a background in quantitative or computational areas are required to take the Certificate for Module (Quantitative Methods in Finance) as the bridging course. They must complete and pass the module before the commencement of the programme.

Apply

Application Form Download Application Form

Enrolment Method
Payment Method
1. Cash, EPS, WeChat Pay Or Alipay

Course fees can be paid by cash, EPS, WeChat Pay or Alipay at any HKU SPACE Enrolment Centres.

2. Cheque Or Bank draft

Course fees can also be paid by crossed cheque or bank draft made payable to “HKU SPACE”. Please specify the programme title(s) for application and applicant’s name. You may either:

  • bring the completed form(s), together with the appropriate course or application fees in the form of a cheque, and any required supporting documents to any of the HKU SPACE enrolment centres;
  • or mail the above documents to any of the HKU SPACE Enrolment Centres, specifying “Course Application” on the envelope. HKU SPACE will not be responsible for any loss of personal information and payment sent by mail.
3. VISA/Mastercard

Applicants may also pay the course fee by VISA or Mastercard, including the “HKU SPACE Mastercard”, at any HKU SPACE enrolment centres. Holders of the HKU SPACE Mastercard can enjoy a 10-month interest-free instalment period for courses with a tuition fee worth a minimum of HK$2,000; however, the course applicant must also be the cardholder himself/herself. For enquiries, please contact our staff at any enrolment centres.

4. Online Payment

Online application / enrolment is offered for most open admission courses (enrolled on first come, first served basis) and selected award-bearing programmes. Application fees and course fees of these programmes/courses can be settled by using "PPS by Internet" (not available via mobile phones), VISA or Mastercard. In addition to the aforesaid online payment channels, new and continuing students of award-bearing programmes with available online service, they may also pay their course fees by Online WeChat Pay, Online Alipay or Faster Payment System (FPS). Please refer to Enrolment Methods - Online Enrolment  for details.

Notes

  • If the programme/course is starting within five working days, application by post is not recommended to avoid any delays. Applicants are advised to enrol in person at HKU SPACE Enrolment Centres and avoid making cheque payment under this circumstance.

  • Fees paid are not refundable except under very exceptional circumstances (e.g. course cancellation due to insufficient enrolment), subject to the School’s discretion. In exceptional cases where a refund is approved, fees paid by cash, EPS, WeChat Pay, Alipay, cheque, FPS or PPS by Internet will be reimbursed by a cheque, and fees paid by credit card will be reimbursed to the credit card account used for payment. 

  • In addition to the published fees, there may be additional costs associated with individual programmes. Please refer to the relevant course brochures or direct any enquiries to the relevant programme team for details.
  • Fees and places on courses cannot be transferrable from one applicant to another. Once accepted onto a course, the student may not change to another course without approval from HKU SPACE. A processing fee of HK$120 will be levied on each approved transfer.
  • HKU SPACE will not be responsible for any loss of payment, receipt, or personal information sent by mail.
  • For payment certification, please submit a completed form, a sufficiently stamped and self-addressed envelope, and a crossed cheque for HK$30 per copy made payable to “HKU SPACE” to any of our enrolment centres.
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