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Accounting & Finance FinTech and Financial Analytics

Certificate for Module (Time Series Methods and Technical Analysis)
證書(單元 : 時間序列方法與技術分析)

Course Code
FN153A
Application Code
2360-FN153A

Credit
6
Study mode
Part-time
Start Date
To be advised
Next intake(s)
Feb 2026
Duration
30 hours
Language
English
Course Fee
Course Fee: HK$10,500 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

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 25 intake! There will be practical classes in the computer laboratory. The contemporary knowledge of time series methods and technical analysis will be discussed. The practical skills to apply computational tools and software to perform technical analysis will be illustrated. Practical applications of time series methods and technical analysis in quantitative investing will be covered.

Highlights

The programme is designed to impart contemporary knowledge of time series methods and technical analysis to students. It equips them with the practical skills to apply computational tools and software to perform technical analysis. In addition, the programme explores the practical applications of time series methods and technical analysis in quantitative investing.

Programme Details

On completion of the programme, students should be able to

  1. examine the key elements and applications of technical analysis;
  2. identify time series methods and apply relevant techniques in technical analysis;
  3. use computational tools and software to analyse financial data; and
  4. discuss backtesting and investment strategies to support investment decision-making.
Application Code 2360-FN153A Apply Online Now
Apply Online Now

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

Modules

Course Content :

(1) Introduction to technical analysis

  • Assumptions of technical analysis
  • Overview of price charts, chart types, trend analysis and chart patterns
  • Differences between technical and fundamental analysis
  • Technical indicators and oscillators, volume analysis
  • Basic behavioural finance and applications of technical analysis

(2) Introduction to methods and analysis of time series

  • Time series and financial data
  • Introduction to computational tools and software
  • Time series visualisation
  • Descriptive statistics for time series
  • Stationarity and transformation
  • Autocorrelation and partial autocorrelation
  • Smoothing techniques
  • Time series models and forecasting techniques

(3) Financial time series: methodology, analysis and modelling

  • Introduction to financial time series and time series components
  • Time series models: autoregressive (AR), moving average (MA), autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average (SARIMA)
  • Volatility modelling: autoregressive conditional heteroskedasticity (ARCH), generalised autoregressive conditional heteroskedasticity (GARCH), exponential generalised autoregressive conditional heteroskedasticity (EGARCH), and threshold generalised autoregressive conditional heteroskedasticity (TGARCH)
  • Logarithmic returns, historical volatility and distribution of financial returns

(4) Applications of time series methods and technical analysis

  • Overview of artificial intelligence (AI), machine learning (ML) and deep learning (DL) for finance and investment
  • ML techniques for time series forecasting
  • Bayesian time series models and market prediction
  • Construction of technical indicators for technical analysis: simple moving average (SMA), exponential moving average (EMA), moving average convergence divergence (MACD), relative strength index (RSI), and average directional index (ADX)
  • Backtesting and trading strategies
  • Quantitative investing strategies and investment decision-making

Assessment method: In-class Exercise + Group Project Presentation

 

Award

Upon successful completion of the programme, students who have passed the assessments with attendance no less than 70% will be awarded within the HKU system through HKU SPACE a "Certificate for Module (Time Series Methods and Technical Analysis)".

Class Details

Timetable

Lecture Date Time
1 11 Dec 25 (Thu) 19:00-22:00
2 16 Dec 25 (Tue) 19:00-22:00
3 18 Dec 25 (Thu) 19:00-22:00
4 23 Dec 25 (Tue) 19:00-22:00
5 30 Dec 25 (Tue) 19:00-22:00
6 6 Jan 26 (Tue) 19:00-22:00
7 8 Jan 26 (Thu) 19:00-22:00
8 13 Jan 26 (Tue) 19:00-22:00
9 15 Jan 26 (Thu) 19:00-22:00
10 20 Jan 26 (Tue) 19:00-22:00

Remarks: Tentative timetable is subject to change, and course commencement is subject to sufficient enrollment numbers 

Progression Path

Teacher Information

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 acquired from working for 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 the management reporting processes. In addition to industry experiences, Dr Lau also has extensive exposures 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, financial markets, risk management, and sustainability. 

Fee

Application Fee

HK$150

Course Fee
  • Course Fee: HK$10,500 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 where the language of teaching and assessment is English. Those with a business, accounting, finance, economics, mathematics, statistics, science, engineering, IT or computer science background would have an advantage.

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

**Please upload copy of HKID and proof of degree while applying online

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.
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