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Certificate for Module (Applications of LLMs and RAG Systems in Investment Analysis)
證書(單元 : 大型語言模型與檢索增強生成系統於投資分析的應用)

Course Code
FN178A
Application Code
2440-FN178A
Credit
6
Study mode
Part-time
Start Date
15 Aug 2026 (Sat)
Next intake(s)
Oct 2026
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 03 Aug 2026 (Mon)
Enquiries
2867 8331 / 2867 8424
2861 0278
finedec@hkuspace.hku.hk
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  • Add to Folders

Accept new applications for Aug 26 intake! There will be practical classes in the computer laboratory.

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Highlights

The programme aims to equip students with the essential capability to integrate artificial intelligence (AI) with modern investment analysis, and to systematically master the core principles and practical implementation techniques of large language models (LLMs) and retrieval-augmented generation (RAG) systems. Students will learn to design and build efficient, reliable, and intelligent investment analysis systems capable of extracting key insights from financial data. Students will also develop the skills to solve real-world financial problems, addressing key challenges such as temporal data processing, multimodal analysis, and the design of methods to mitigate hallucinations. Besides, the programme cultivates students’ ability to apply computational tools and software for generative artificial intelligence (GenAI) and LLMs to perform AI-driven investment analysis.

Programme Details

On completion of the programme, students should be able to 
1.    analyse the architecture of large language models (LLMs) and retrieval-augmented generation (RAG) systems, and explain their core components, capabilities, and inherent limitations in the context of financial data processing and investment analysis;
2.    design a functional RAG system tailored for investment analysis by integrating techniques for processing financial data, executing advanced retrieval, and applying financial domain specialisation to LLMs to generate grounded query responses;
3.    develop computational tools and software for generative artificial intelligence (GenAI) and LLMs to perform AI-driven investment analysis, and implement critical guardrails to mitigate hallucinations and ensure output traceability; and 
4.    critically evaluate the performance, ethical implications, and practical efficacy of LLMs and RAG systems in investment analysis.

Application Code 2440-FN178A Apply Online Now
Apply Online Now

Days / Time
  • Saturday, 1:00pm - 6:00pm
Duration
  • 30 hours per programme
Venue
  • Hong Kong Island Learning Centre
  • Kowloon West Campus
  • Kowloon East Campus

Modules

Course Content :

(1) Foundational concepts of large language models (LLMs) in investment analysis

  • Review of Python programming
  • Introduction to computational tools and software for generative artificial intelligence (GenAI) and LLMs
  • AI ethics and data privacy
  • Transformer architecture and attention mechanisms
  • LLM pre-training, fine-tuning, and inference
  • Prompt engineering and in-context learning
  • Limitations of base LLMs
  • Investment analysis and LLMs
    • Corporation filings and financial data sources
    • Macroeconomic and industry analysis
    • Financial statement analysis and ratio analysis
    • Equity and fixed income valuation models

(2) Introduction to retrieval-augmented generation (RAG) in finance

  • RAG architecture
  • Document chunking strategies
  • Vector embeddings and semantic search
  • Vector databases
  • Sparse and hybrid search techniques
  • Query understanding and transformation
  • Re-ranking models
  • Advanced RAG patterns
  • Applications of RAG to financial data
    • Structuring unstructured financial texts
    • Multi-modal RAG for charts and tables
    • Temporal knowledge handling
    • Multi-source data fusion
    • Financial named entity recognition

(3) LLM specialisation for investment analysis

  • Financial LLMs and specialised fine-tuning
  • Agentic reasoning and tool use
  • Chain-of-thought (CoT) and reasoning frameworks
  • Sentiment and thematic analysis
  • Summarisation and key insight extraction
  • Scenario analysis and what-if question answering

(4) Building and evaluating the RAG systems

  • Evaluation frameworks for RAG
  • Hallucination mitigation and guardrails
  • Prototyping a fill pipeline
  • Development and implementation of RAG systems for investment analysis

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 (Applications of Large Language Models and Retrieval-Augmented Generations Systems in Investment Analysis)".

Class Details

Timetable

Lecture

Date

Time

1

15 Aug 26 (Sat)

13:00-18:00

2

22 Aug 26 (Sat)

13:00-18:00

3

29 Aug 26 (Sat)

13:00-18:00

4

5 Sep 26 (Sat)

13:00-18:00

5

12 Sep 26 (Sat)

13:00-18:00

6

19 Sep 26 (Sat)

13:00-18:00

Teacher Information

Mr Ivan Law

Background

Mr Ivan Law brings over two decades of combined experience in education, applied data science, and the finance industry, with a proven track record of empowering learners to master in-demand technical skills. Holding a BEng in Computer Science from HKUST and an MSc in Financial Management from the University of London, Ivan leverages his interdisciplinary background to make complex data science concepts accessible to students from diverse academic and professional backgrounds. As a part-time lecturer at CityU SCOPE, he has delivered over 2,100 instructional hours across 8 cohorts of adult learners, specializing in Python, Pandas, NumPy, data visualization (Matplotlib/Seaborn/Plotly), and AI/ML foundations. Rooted in rigorous project-based learning, his teaching philosophy prioritizes clarity, practical application, and active engagement. He designs interactive exercises and real-world projects to help learners translate theoretical concepts into actionable skills that meet workplace needs. Complementing his teaching practice is 18+ years of experience in senior roles across the banking and hedge fund industry, where he led teams in operations, risk management, and compliance at both local and US-based hedge fund firms. Serving as Director, Ivan now designs and delivers professional data science training and deploys AI/ML pipelines for SMEs. This hands-on, up-to-date experience building industry-ready solutions ensures his teaching curricula remain current with the latest tools and market demands, which further strengthens his ability to connect analytical rigor with business insights.

Fee

Application Fee

HK$150 (Non-refundable)

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, Higher Diploma or Associate Degree awarded by a recognised institution where the language of teaching and assessment is English. Those with a background in business, accounting, finance, economics, mathematics, statistics, science, engineering, IT or computer science 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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