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Accounting & Finance FinTech and Financial Analytics

Certificate for Module (Digital Asset Valuation and Tokenomics)
證書(單元 : 數碼資產估值與代幣經濟學)

Course Code
FN187A
Application Code
2460-FN187A
Credit
6
Study mode
Part-time
Start Date
08 Dec 2026 (Tue)
Next intake(s)
Mar 2027
Duration
30 hours
Language
English
Course Fee
Course Fee: HK$10,800 per programme (* course fees are subject to change without prior notice)
Deadline on 27 Nov 2026 (Fri)
Enquiries
2867 8331 / 2867 8424
2861 0278
Apply Now

Accept New Applications for Dec 26 intake! There will be practical classes in the computer laboratory.

Highlights

The programme aims to equip students with foundational knowledge of the Web3 ecosystem, network economics, and tokenomics, as well as Python programming and generative artificial intelligence (GenAI) skills tailored for digital asset valuation. Based on a logical progression from digital asset taxonomy, on-chain data analytics, and protocol cash flow models to Python-based valuation agents, large language model (LLM)-driven sentiment analysis, and economics of consensus mechanisms, students will develop the ability to evaluate AI-enhanced valuation frameworks for pricing complex crypto-assets and optimising intgrated multi-asset portfolios. The programme also develops students’ ability to apply LLMs and quantitative tools to address real-world investment challenges, while integrating macroeconomic risk management, smart contract auditing, and regulatory compliance to deliver robust, risk-adjusted digital asset portfolio management.

Programme Details

On completion of the programme, students should be able to

  1. critically examine the diverse spectrum of digital assets, and explain their underlying tokenomics and regulatory issues;
  2. apply Python and on-chain data analytics to extract, visualise, and interpret quantitative network economics metrics;
  3. analyse protocol revenue streams, consensus mechanisms, and intangible asset market sentiment by leveraging large language models (LLMs) to develop automated, GenAI-powered valuation agents and dynamic cash flow models;
  4. evaluate macroeconomic drivers, market liquidity, and protocol-specific vulnerabilities by utilising AI-assisted smart contract auditing and GenAI-driven modelling to discount risks in digital asset valuations; and
  5. design and construct portfolios with digital assets, and discuss hedging strategies and portfolio optimisation techniques.
Application Code 2460-FN187A Apply Online Now
Apply Online Now

Days / Time
  • Tue, Thu, 6:45pm - 9:45pm
Duration
  • 30 hours per programme
Venue
  • Hong Kong Island Learning Centre
  • Kowloon West Campus
  • Kowloon East Campus

Modules

Course Content :

(1) Foundational taxonomy and asset classifications

  • Overview of financial technology (FinTech) ecosystem and Web3 in Hong Kong
  • Artificial intelligence (AI), generative artificial intelligence (GenAI) and large language models (LLMs) in FinTech
  • The digital asset spectrum and taxonomy
  • LLM-driven whitepaper analysis
  • Infrastructure valuation: Layer-1 vs Layer-2
  • Stablecoin mechanics and risk-adjusted valuation models
  • Real-World Asset (RWA) tokenisation

(2) Network economics and quantitative metrics

  • Introduction to network economics and tokenomics 
  • Overview of natural language to structured query language (SQL)
  • Introduction to Python and on-chain data analytics
  • Metcalfe’s Law and network effects
  • Network value-to-transactions (NVT) ratio
  • Automating total value locked (TVL) and liquidity analytics
  • Velocity of token supply
  • On-chain metric interpretation and data visualisation
  • Growth trajectory forecasting and anomaly detection

(3) Protocol revenue, cash flow models, and automated valuation agents

  • Decentralised exchange (DEX) valuation
  • Decentralised lending and borrowing valuation
  • GenAI-powered discounted cash flow (DCF) and automated protocol revenue projections
  • Discount rates and crypto cost of capital
  • Building Python-based valuation agents: real-time scraping for price-to-sales (P/S) and price-to-fees (P/F) ratios
  • Revenue stream analysis and sustainability assessment
  • Sensitivity analysis and backtesting of valuation models

(4) Staking, consensus and security-based valuation models

  • Proof of stake (PoS) yield and valuation
  • Proof of work (PoW) mining economics
  • Security spend and network defense valuation
  • Liquid staking derivatives (LSDs)
  • Maximal extractable value (MEV)
  • Yield optimisation strategies and emerging consensus mechanisms
  • Security budgeting and economic security models

(5) Intangible assets and sentiment-driven valuation

  • Non-fungible token valuation frameworks
  • Web3 domain names and digital identity (DID)
  • Social tokens and creator economies
  • Decentralised finance (DeFi) governance premium
  • LLM sentiment analysis
  • Option pricing models for cryptocurrencies
  • Valuation of digital identity and social capital assets
  • Governance token mechanics and voting power premiums

(6) Risk, governance, and macroeconomic drivers

  • Regulatory issues of digital assets in Hong Kong
  • AI for first-pass smart contract auditing
  • GenAI for regulatory and compliance risk modelling
  • Liquidity, slippage, and market depth analysis
  • Macroeconomic drivers and crypto-correlations
  • Custody, security, and key management valuations

(7) Integrated portfolio management for digital assets

  • Portfolio construction for digital assets and diversification across asset classes
  • Sharpe ratio optimisation and correlation analysis
  • Risk-adjusted performance measurement
  • Dynamic rebalancing and hedging strategies
  • AI-augmented investment decision frameworks
  • Backtesting and simulation of multi-asset portfolios

Assessment method: In-class Exercise + Group Project Presentation

 

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 "Certificate for Module (Digital Asset Valuation and Tokenomics)".

Class Details

Timetable

Lecture

Date

Time

1

8 Dec 26 (Tue)

18:45-21:45

2

10 Dec 26 (Thu)

18:45-21:45

3

15 Dec 26 (Tue)

18:45-21:45

4

17 Dec 26 (Thu)

18:45-21:45

5

5 Jan 27 (Tue)

18:45-21:45

6

7 Jan 27 (Thu)

18:45-21:45

7

12 Jan 27 (Tue)

18:45-21:45

8

14 Jan 27 (Thu)

18:45-21:45

9

19 Jan 27 (Tue)

18:45-21:45

10

21 Jan 27 (Thu)

18:45-21:45

Teacher Information

Dr Simon Yiu

Background

Simon is the IT Department Head of a financial institution in Hong Kong who has handled many FinTech initiatives and projects, such as Algo trading, financial big data analytics, and robo-advisors. Before that, he also worked for an AI, and Machine learning startup as co-founder and CTO which was located at a Hong Kong Science Park and participated in the University-organized Entrepreneurship Center in 2010, focusing on AI, Machine Learning, Big Data analytics and Natural language processing. Furthermore, he has hands-on programming experience in FinTech areas for over 10 years. Simon earned a Doctoral Degree in Business Administration from the City University of Hong Kong and a Master’s Degree in Data Science and Business Statistics from The Chinese University of Hong Kong.

Fee

Application Fee

HK$150

Course Fee
  • Course Fee: HK$10,800 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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