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Accounting & Finance FinTech and Financial Intelligence

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

CEF Reimbursable Course (selected modules only)

CEF Reimbursable Course (selected modules only)

Course Code
FN092A
Application Code
2085-FN092A

Study mode
Part-time
Start Date
03 May 2023 (Wed)
Duration
1 year to 2 years
Language
English
Course Fee
Module fee: $11,000 (course fees are subject to change without prior notice)
2 installment-:1st- three modules: $33,000 and 2nd- three modules: $33,000
Deadline on 19 Apr 2023 (Wed)
Enquiries
25204612
28610278
Apply Now

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 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.

Programme Structure 

Module 1: AI and Financial Computing (42 hours)

Module 2: Financial Analysis and ESG Investing (33 hours)

Module 3: Financial Risk Analysis and Portfolio Optimisation (33 hours)

Module 4: Machine Learning for Financial Analytics (33 hours)

Module 5: Web Scraping and Text Analytics in Quantitative Finance (33 hours)

Module 6: Algo Trading and Quantitative Investment Strategies (42 hours)

 

Postgraduate Diploma in Financial Analytics and Algo-trading Apply Online Now
Application Code 2085-FN092A
Start Date 3 May 2023
Apply Online Now

Venue

Module 1: AI and Financial Computing 

  • Principles of finance
  • Overview of AI in finance
  • Introduction to Python programming
  • Mathematical and computational methods for finance

 

Module 2: Financial Analysis and ESG Investing 

  • Introduction to financial analysis
  • Evaluation of financial performance
  • Introduction to ESG and the ESG market
  • ESG factors, engagement and stewardship
  • ESG analysis, valuation and integration
  • ESG integrated portfolio construction and management
  • Investment mandates, portfolio analytics and client reporting
  • Integrated financial analysis and ESG investing

 

Module 3: Financial Risk Analysis and Portfolio Optimisation

  • Overview of financial risk analysis
  • Interest rate risks
  • Market risks
  • Credit risks
  • Portfolio optimisation

 

Module 4: Machine Learning for Financial Analytics 

  • Introduction to machine learning and financial analytics
  • Machine learning and algorithms
  • Financial modelling and financial analytics

 

Module 5: Web Scraping and Text Analytics in Quantitative Finance 

  • Web scraping and data wrangling
  • Text analytics and text mining
  • Applications of web scraping and text analytics for quantitative finance

 

Module 6: Algo Trading and Quantitative Investment Strategies 

  • Introduction to algorithmic trading
  • Technical analysis and algorithmic trading
  • Performance evaluation and strategy optimization
  • AI, algo trading and quantitative investment strategies

 

 

 

1. Mr. Hong Lin

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 the Hong Kong University of Science and Technology with a Master of Science degree in Business Analytics. Mr. Lin is passionately sharing his Fintech knowledge and contributing to the digital progress of Hong Kong’s banking and finance industry.

 

2. Mr. Stephen Chan

Mr. Stephen Chan has more than 20 years of experience as a professional accountant and business consultant. He holds his BSc degree in Science in Economics awarded by the University of London and the Professional Diploma in Accountancy awarded by the Hong Kong Polytechnic University. Also, he is a graduate of the Executive MBA from the City University of Hong Kong. He started his first auditing job at PricewaterhouseCoopers. Moreover, he has served the Hong Kong SAR Government, the Jockey Club, banks, airlines and various listed companies. In recent years, he has offered advisory services for various companies on compliance, corporate finance and taxation. He has also worked as a lecturer at various universities and tertiary institutions.

 

3. Mr. Ken Liu

Mr. Liu, co-founder and CTO of Datatact Ltd, a startup focus on AI, Machine Learning and Big Data analytics. He is a hands on expert in his specialized area for over 10 years.  Prior to Datatact, Ken worked at Citi, HSBC, Goldman Sachs, Deutsche Bank and Credit Suisse as Algo-Trading developer. Mr. Liu earned a Master in Computer Science from USC and a Bachelor in Computer Science from University of Warwick.

 

4. Dr. Simon Yiu

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

 

5. Mr. Ferrix Lau

Mr Lau has over 10 years’ teaching experience in business, accounting and finance modules at tertiary level. He teaches Financial Analysis, Financial Risk Management, Quantitative Analysis, Financial Accounting, Cost and Management Accounting as well as Corporate Governance. Moreover, he is a co-author of a Statistics book, Quantitative Analysis for Professional Studies and Projects. Furthermore, he has strong interests in the areas of Statistical Analysis, Quantitative Finance and Machine Intelligence. Mr. Lau has earned a Bachelor's Degree in Social Science from The Chinese University of Hong Kong, major in Economics and minor in Computer Science. Besides, he holds a Master's Degree in Business Administration with Distinction from The University of Hong Kong, concentrating on the theme of Accounting Control and Financial Management.

 

Award

Students who complete all six modules with over 70% attendance and pass all individual assignments and group presentations will be awarded the Postgraduate Diploma in Financial Analytics and Algo Trading within the HKU system through HKU SPACE.

 

 

Class Schedule (May 2023)

Remark: Tentative timetable is subject to change and module commencement is subject to sufficient enrollment numbers.

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.

 

Application Fee

HK$150 (Non-refundable)

Course Fee
  • Module fee: $11,000 (course fees are subject to change without prior notice)
    2 installment-:1st- three modules: $33,000 and 2nd- three modules: $33,000
  • The CEF Institution Code of HKU SPACE is 100
CEF Courses
new AI and Financial Computing (Module from Postgraduate Diploma in Financial Analytics and Algo Trading)
COURSE CODE 33Z144216 FEES $11,000 ENQUIRY 2520-4612
new Financial Analysis and ESG Investing(Module from Postgraduate Diploma in Financial Analytics and Algo Trading)
COURSE CODE 33Z144224 FEES $11,000 ENQUIRY 2520-4612
new Machine Learning for Financial Analytics (Module from Postgraduate Diploma in Financial Analytics and Algo Trading)
COURSE CODE 33Z144232 FEES $11,000 ENQUIRY 2520-4612
new Algo Trading and Quantitative Investment Strategies (Module from Postgraduate Diploma in Financial Analytics and Algo Trading)
COURSE CODE 33Z144240 FEES $11,000 ENQUIRY 2520-4612
Continuing Education Fund Reimbursable Course Continuing Education Fund Reimbursable Course (selected modules only)
Some modules of this course have been included in the list of reimbursable courses under the Continuing Education Fund.

Postgraduate Diploma in Financial Analytics and Algo Trading

  • This course is recognised under the Qualifications Framework (QF Level [6])

Online Application Apply Now

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 the 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 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 (course 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, continuing students of award-bearing programmes, if their programmes offer online service, may also pay their course fees by Online WeChat Pay, Online Alipay and 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 Enrolement Centres and avoid making cheque payment under this circustance.
  • 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 or PPS (for online payment only) will normally be reimbursed by a cheque, and fees paid by credit card will normally be reimbursed to the payment cardholder's credit card account.
  • 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.
  • Receipts will be issued for fees paid but HKU SPACE will not be repsonsible for any loss of receipt 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. 

HKIECA

Students under this programme are eligible to apply free membership for Hong Kong Internet and Ecommerce Association (HKIECA).

The association offers talks, workshops and/or seminars related to the Internet and Ecommerce as well as Big Data.

Link of HKIECA: www.hkieca.org