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Accounting & Finance FinTech and Financial Analytics

Certificate for Module (Business Forecasting and Predictive Analytics for Financial Decision Making)
證書(單元:財務決策的商業分析與預測)

CEF Reimbursable Course

CEF Reimbursable Course

Course Code
FN082A
Application Code
2360-FN082A

Credit
6
Study mode
Part-time
Start Date
09 Dec 2025 (Tue)
Next intake(s)
Jun 2026
Duration
2 months to 4 months
Language
English
Course Fee
Course Fee: $9900 per programme (* course fees are subject to change without prior notice)
Deadline on 01 Dec 2025 (Mon)
Enquiries
2867 8331 / 2867 8424
2861 0278
Apply Now

Today and Upcoming Events

10
Dec 2025
(Wed)

How to Design a Strong-Stock Analytics Dashboard System (强勢股分析系統)? (10 Dec 2025)

To design a stock price analytics system, we need to do the following: Collect historical stock prices Transform the collected stock price record to an appropriate format for presentation Present the transformed stock price datasets in a useful layout to facilitate analytics and investors’ review.   In this talk (webinar), the speaker will showcase how to design a Strong Stock Analytics Dashboard with a BI approach. This would give you a fresh view of the practical use of data automation and data visualization techniques.   During this webinar, you will explore how a Strong Stock Analytics Dashboard will help you to: review the recent trend of HSI identify the strong stocks and weak stocks based on a specified definition of price momentum compare the ratio between the strong stocks and weak stocks according to your selected price momentum definition do sectoral analysis of the strong / weak stocks   This is an advanced application of data analytics techniques with common financial data.  You will find this webinar inspiring and will give you food for thought on how to make use of learnt data techniques for financial stock investment analysis.   Sample Screenshots below:   Related Programme Links: Certificate for Module (Technical Analysis and Data Analytics for Stock Investment) - HKU SPACE: FinTech and Financial Intelligence, Data Science courses https://hkuspace.hku.hk/prog/exe-cert-in-interpretation-and-visualization-of-business-big-data https://hkuspace.hku.hk/prog/cert-for-module-business-intelligence-and-data-automation

17
Dec 2025
(Wed)

Any correlation between Bitcoin, Ethereum and Gold? And now Stablecoin Explained (17 Dec 2025)

Bitcoin and Ethereum are the most dominant cryptocurrencies, both accumulative account for over 90% in term of market capitalization excluding stablecoin, out of more than two thousands currently in the market. They are both in a form of digital asset that trades via various DEX or centralizated regulated trading platforms. However there are quite many key differences among them though. Bitcoin (BTC) is designed as a monetary storage (some proclaim as alternative of Gold) and medium of transaction and as an alternative to fiat currency. Ethereum (ETH), otherwise, is intended for complex smart contracts or dApps which contribute and act as key infrastructure of the emerging Web3.0 future. In light of recent rapid further innovation (like staking protocol) and adoption, the price of BTC and ETH have been risen more than double in past 12 months and also exhibited a huge volatility. The speaker will give a brief introduction of above crypto with some attention drawn to the relationship and correlation among Bitcoin, Ethereum, Gold, XRP and S&P500 – as shown in below charts. The speaker will then also talk about the latest development and impacts of the recently passed GENIUS Act in U.S. and the Stablecoins Ordinance (Cap. 656) in HK. To say, Stablecoin per se. is NOT referring the price to be fixed or stable, it’s referring to link or collateralize by some tangible asse shifting from no intrinsic value issue. At 10 Oct 2025, one of most selloff in crypto, USDe has experienced flash crash to as low as 0.65 USDEUST, per shown below. Source: Bloomberg   Language: Cantonese (Supplemented with English)

Accept New Applications for Dec 2025 intake! There are practical classes in the computer laboratory. Our professional lecturer will illustrate techniques of business forecasting as well as practical skills in data mining and predictive analytics for financial decision-making. Business trend analysis, market index forecasting, text analytics, web analytics, social network analysis, and the applications of Excel and Python will be discussed. Welcome to your online application!

Highlights

The programme aims to provide students with the basic knowledge of business forecasting and essential skills in predictive analytics for financial decision making. The programme illustrates techniques of data exploration and regression analysis, and discusses computational tools used to perform business forecasting and predictive analytics. Students can learn how to apply business forecasting through case studies and perform predictive analytics through practical classes in computer laboratories.

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Programme Details

On completion of the programme, students should be able to
1. describe the concept of business forecasting and explain data analytics for business;
2. outline various techniques for data exploration, regression analysis and business forecasting;    
3. apply computational tools to perform business forecasting and predictive analytics; and
4. discuss business implications of forecasting and practical applications of predictive analytics for financial decision making.

Application Code 2360-FN082A Apply Online Now
Apply Online Now

Days / Time
  • Tue, Thu, 7:00pm - 10:00pm
Duration
  • 30 hours
Venue
  • Hong Kong Island Campus
  • Kowloon East Campus
  • Kowloon West Campus

Modules

Syllabus:

(1) Fundamentals of business forecasting and data analytics

  • Introduction to data science and related career development
    • Data science powered by machine learning and artificial intelligence
    • Job descriptions of data analyst, business analyst, data scientist and business intelligence engineer
    • Career development in data science
  • Overview of data analytics for business
    • Descriptive analytics
    • Diagnostic analytics
    • Predictive analytics
    • Prescriptive analytics
  • Principles of business forecasting and data analytics
  • Introduction to computational tools for business forecasting and data analytics
  • Managerial challenges and business opportunities with data analytics
  • Ethical use of data and data privity

 

(2) Techniques of business forecasting and predictive analytics

  • Overview of business statistical concepts
  • Sources of data and data integrity
  • Fundamentals of predictive analytics
  • Introduction to business forecasting and model building using Excel
  • Introduction to Python for statistics and data analytics
  • Techniques of data exploration and regression analysis
    • Exploring data patterns and data visualization
    • Simple linear regression
    • Multiple regression
    • Moving averages and smoothing methods
    • Time series and their components
    • Regression with time series data
  • Business forecasting techniques
    • Quantitative techniques
    • Qualitative techniques
  • Applications of predictive analytics and data mining for business
  • Applications of computational tools for business forecasting and predictive analytics

 

(3) Applications of business forecasting and predictive analytics

  • Business implications and issues of forecasting
  • Business trend analysis and model building
  • Market index forecasting and investment
  • Text analytics and sentiment analysis for finance and investment
  • Web analytics and social network analysis for financial market
  • Business forecasting and financial decision making
  • Managerial issues and data-driven decision-making with predictive analytics

 Assessment method: Two in-class exercise + Group Project Presentation

Upon successful completion of the programme, students who have passed the continuous assessment and final assessment with attendance no less than 70% will be awarded within the HKU system through HKU SPACE a Certificate for Module (Business Forecasting and Predictive Analytics for Financial Decision Making).

Teacher

1. M. 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.

2. Mr Percy Kong
Mr Percy Kong is a fintech professional with over 20 years of extensive experience in IT, banks, and listed fintech unicorns. He has participated in and led hundreds of treasury and trading platform projects in different commercial and investment banks. He has been awarded various honors from Bloomberg, Refinitiv, Deutsche Bank, HKEX, IBM, Oracle, Sun Java, and Capital Weekly. In 2023, he led a treasury data science research team at the Hong Kong University Business School. He now actively consults in applying machine learning, deep learning, trading technology, and agentic AI in treasury and banking. Percy earned an Executive Certificate in Data Science from Hong Kong University, a Master's in Financial Engineering from City University of Hong Kong, and a Bachelor's in Computing from Monash University. He is currently a Certified Senior Treasury Management Professional from the Hong Kong Monetary Authority and a Global Talent (Fintech) from Australia.

Class Details

Timetable

Dec 2025
 

Lecture Date Time
1 9 Dec 25 (Tue) 19:00-22:00
2 11 Dec 25 (Thu) 19:00-22:00
3 16 Dec 25 (Tue) 19:00-22:00
4 18 Dec 25 (Thu) 19:00-22:00
5 23 Dec 25 (Tue) 19:00-22:00
6 30 Dec 25 (Thu) 19:00-22:00
7 6 Jan 26 (Tue) 19:00-22:00
8 8 Jan 26 (Thu) 19:00-22:00
9 13 Jan 26 (Tue) 19:00-22:00
10 15 Jan 26 (Thu) 19:00-22:00

-Tentative timetable is subject to change, and course commencement is subject to sufficient enrollment numbers.
-For the health and safety of teachers and students, the School may substitute face-to-face classes with online teaching if the face-to-face classes cannot be held. 

Fee

Application Fee

HK$150 (Non-refundable)

Course Fee
  • Course Fee: $9900 per programme (* course fees are subject to change without prior notice)

Entry Requirements

Applicants should hold an Advanced Diploma, a Higher Diploma or an Associate Degree awarded by a recognised institution. Those with a business, IT or computer science background would have an advantage.

Applicants with other equivalent qualifications will be considered on individual merit.

CEF

  • The CEF Institution Code of HKU SPACE is 100
CEF Courses
Certificate for Module (Business Forecasting and Predictive Analytics for Financial Decision Making)
證書(單元:財務決策的商業分析與預測)
COURSE CODE 33C140357 FEES $9,900 ENQUIRY 2867-8331
Continuing Education Fund Continuing Education Fund
This course has been included in the list of reimbursable courses under the Continuing Education Fund.

Certificate for Module (Business Forecasting and Predictive Analytics for Financial Decision Making)

  • This course is recognised under the Qualifications Framework (QF Level [5])

Apply

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 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.