Accounting & Finance Investment Management
The Postgraduate Diploma in Investment Management and Financial Intelligence...
Big Data is important in investment management and AI is widely using in formulating investment decision. This programme offers the academic and practical knowledge in investment management as well as contemporary development in Big Data, AI and FinTech.
The programme aims to:
- impart financial and investment management knowledge and skills to students to enhance their financial decision making;
- facilitate students to understand and analyze contemporary issues as well as the latest development in the financial world;
- prepare students to sit for the CFA examinations based on the Candidate Body of Knowledge;
- equip students with the latest technologies on Big Data and Artificial Intelligence for financial industry;
- stimulate students to apply financial intelligence to perform investment management.
The Postgraduate Diploma in Investment Management and Financial Intelligence programme covers a wide range of knowledge in Economic and Statistical Analysis, Corporate Financial Management, Risk and Portfolio Management as well as Fintech, Big Data, Artificial Intelligence and Investing. As it has a rigorous syllabus and teaching members are all market practitioners, students will learn the concepts and theories to make investment decisions as well as the contemporary and practical cases of Financial Intelligence in the real world. Students will also learn how to apply the knowledge and techniques used by market professionals and refresh their investment knowledge using big data.
Graduates of this programme have satisfied the Institute of Financial Technologist of Asia (IFTA) requirements for qualification and will be admitted to the CFT programme for the exemption of Level 1.
News about Investment Management and Financial Intelligence:
AI搶人大戰 By ETTODAY
摩通CEO︰全速雲計算變革 By oncc.com
金融科技2.0 人才需具「ABCD」技能 《香港經濟日報》
Sky-High Salaries Are the Weapons in the AI Talent War by Bloomberg
Understanding Payment Channels by Chainside.net
What do we mean by “blockchains are trustless”? by Medium.com
CEO’s message to bankers who want to stay employed by efinancialcareers
Carrie Lam’s pledge to invest in technology a smart move as the global AI contest heats up by SCMP
Are fintechs making an impact on treasury functions? by Euromoney
The fintech ecosystem explained by Business Insider
Australian exchange to adopt blockchain technology by RTHK
The blockchain in banking report: The future of blockchain solutions and technologies by Business Insider
Programme Intended Learning Outcomes:
On completion of the programme, students should be able to:
- apply the relevant theories and concepts in statistical and economic analysis to solve contemporary investment management issues;
- interpret financial statements and analyze key decisions in the area of financing and investment;
- evaluate different methodologies on Big Data Analytics and Artificial Intelligence technologies for financial industry;
- make financial decision based on knowledge of financial intelligence such as Big Data, Fintech and Artificial Intelligence;
- evaluate the risk, pricing structure and strategies involved in the management of traditional and alternative assets;
- analyze investment requirements and construct optimal portfolio and perform portfolio management.
Students who complete all six modules will be awarded the Postgraduate Diploma in Investment Management and Financial Intelligence within the HKU system through HKU SPACE.
Students who complete three modules (module 1 or 2, module 3 or 4 and module 5 or 6) can apply to exit with the Postgraduate Certificate in Investment Management and Financial Intelligence within the HKU system through HKU SPACE.
|Application Code||2065-FN044A||Apply Online Now|
|Apply Online Now|
Module 1: Analytical Tools for Investment Management
This module combines Quantitative Methods and Macroeconomics, two basic subject areas that students must master to pursue more in-depth study of investment topics. Quantitative Methods will cover time value of money, discounted cash flow analysis, probability and statistical concepts, regression analysis and time series. Macroeconomics will cover measurement of GDP and inflation, aggregate demand and supply, Keynesian and Monetary policy and exchange rate determination.
Module 2: Financial Management
This module combines Corporate Finance and Financial Statement Analysis. Corporate Finance topics include capital budgeting and cost of capital, capital structure, dividend policy, warrants and convertibles, mergers and acquisition and corporate governance. Financial Statement Analysis teaches the analysis of financial statements from a user's perspective and includes detailed coverage of major items on the income statement, balance sheet and statement of cash flows. Current topics in accounting are also included.
Module 3: Big Data, Artificial Intelligence and Investing
This module aims to provide students with the knowledge in Big Data and Artificial Intelligence technologies and their applications in investment management. Students are expected to be familiar with different big data analyses and A.I. processes. The module provides an insight on how the current development in these two areas assist and influence investment decision and behaviour.
Module 4: Big Data and FinTech
This module aims to provide students with the knowledge in Big Data and FinTech. The latest development and trend of Big Data and FinTech will be discussed. Students are expected to be familiar with different big data analyses, tools and methodologies. This module provides an insight and challenges on how business world is using Big Data and FinTech to improve their business models.
Module 5: Equity, Debt and Alternative Investments
This module introduces the most commonly used methods of valuing equities including the discounted dividend valuation approach, free cash flow approach, residual income valuation and the use of price multiples. It also covers the analysis of various categories of fixed income instruments including bond investments with various embedded options, mortgage-backed securities, asset-backed securities and interest rate derivatives. Strategies for managing a fixed income portfolio will also be discussed. Alternative investments such as real estate investments, private equity, venture capital and hedge funds will also be taught in this module.
Module 6: Risk and Portfolio Management
This module examines the risk management and derivative market. The full range of derivative instruments such as futures, options and swaps will be discussed. This module also provides an in-depth study of portfolio management and asset pricing models. This module covers behavioural finance, strategies for managing personal versus institutional portfolios, ways of rebalancing the investment portfolio, equity indexing, performance measurement and selection of investment managers.
- Dr. Zenki Kwan, FRM, CAIA, CB, is the investment director of a listed company and a family office in Hong Kong, responsible for investment strategy and portfolio management across equities, fixed income, currency, funds and structured products. He has previously worked in J.P. Morgan, UBS, McKinsey and Samsung Securities. In addition to his doctoral degree, Dr. Kwan also holds Master of Finance and Master of Applied Business Research degrees as well as completed executive education programs at Harvard Law School and Oxford University Saïd Business School, respectively.
- Mr. Ken 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. Ken earned a Master in Computer Science from USC and a Bachelor in Computer Science from University of Warwick.
- Mr. Isaac Lo is currently an investment manager for a Chinese-based fund management firm in Hong Kong. He has more than 20 years of experience in the financial industry. He is also a CFA charterholder. Deep in his heart, he is a Big Data enthusiast. Currently, he is leading Big Data effort in his firm's Hong Kong office. He is a graduate of the Northwestern University's Master of Analytics program, one of the premier master’s program in Big Data. His past projects include improving DVD rental performance for the largest DVD rental kiosks company in the US, creating new credit card default model based on more than 2,000 factors.
- Ms. Isa Kwok, is a Chartered Management Accountant with over 20 years of post qualification experience in financial, management accounting & tax planning areas. She had substantial financial analysis & management experiences and held management positions in listed/sizeable organizations & government department. She also has 10 years teaching experiences in accounting & management courses in various local tertiary institutions.
- Ms. Rowena Lai is a practitioner in Business and Data Analytics. With a Bachelor of Science (Major in Mathematics and Minor in Economics) from The Chinese University of Hong Kong, two Master of Science degrees in both International Shipping and Transport Logistics as well as Global Supply Chain Management from The Hong Kong Polytechnic University, she has worked with different industries on business analytics area. Ms Lai is currently working in a leading banking and leading various data analytics projects. She has also worked in an airline industry leader, and shipping industry on Revenue Management and Business Analytics. Thanks to her strong business and analytical sense together with her extended working exposure, she would like to share her academic knowledge and practical experience in Data Science and Analytics.
- Mr. Andy Fung is an experienced educator. For almost 20 years, he has been teaching extensively in various programs and in tertiary institutions such as School of Continuing and Professional Studies, The Chinese University of Hong Kong and The Hong Kong Community College. Prior to joining the teaching profession, Mr. Fung had been working in the business sector for more than 10 years. His job exposure relates to banking, manufacturing and high technology industry. His last position was Finance Manager of Motorola (HK). Mr. Fung obtained his BSBA degree at The Ohio State University and MBA degree at the University of Illinois, Urbana.
- Ms. Agnes Tse, CFA, is adept at analyzing the impacts of politics on global financial markets. With Master’s degrees in Business Administration and International Relations, her specializations are in monetary policies and currency markets, as well as geopolitics and energy markets. Currently the Head of Research of Action Forex, one of the top 10 global forex websites, Agnes previously worked as analyst for China Everbright Asset Management focusing on Greater China equity markets. She was later an advisor on the investment strategies of a mutual fund in Daiwa Asset Management. Agnes has been a guest speaker in universities in Hong Kong and Europe on topics of Hong Kong's political environment and the financial markets, and China's soft power.
2023 January Intake Class Schedule
Analytical Tools for
|Tue||17 Jan 2023|
Big Data, AI and Investing
|Sat||4 Feb 2023|
Equity, Debt and
|Wed, Sat||26 Apr 2023|
Detailed Timetable: 2023 January Class Schedule
2023 May Intake Class Schedule
|Financial Management||Wed, Thur||18 May 2023|
|Big Data and Fintech||Sat||17 Jun 2023|
|Risk and Portfolio Management||Tue, Sat||12 Aug 2023|
Detailed Timetable: 2023 May Class Schedule
2022 September Intake Class Schedule
|Financial Management||Wed||21 Sep 2022|
Detailed Timetable: 2022 September Class Schedule
Remark: Tentative timetable is subject to change and module commencement is subject to sufficient enrollment numbers.
Applicants shall hold a bachelor’s degree awarded by a recognized institution.
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:
i. an overall band of 6.0 or above with no subtests lower than 5.5 in the IELTS; or
ii. 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
iii. HKALE Use of English at Grade E or above; or
iv. HKDSE Examination English Language at Level 3 or above; or
v. equivalent qualifications.
Applicants with other qualifications will be considered on individual merit.
Remark: Applicants with relevant academic and/or professional qualifications may approach the Programme Team for application of exemption.
- Course Fee: $8000 per module (* course fees are subject to change without prior notice)
- The CEF Institution Code of HKU SPACE is 100
|Analytical Tools for Investment Management (Module from Postgraduate Diploma in Investment Management and Financial Intelligence)|
|COURSE CODE 33Z10768A||FEES $8,000||ENQUIRY 2867-8476|
|Financial Management (Module from Postgraduate Diploma in Investment Management and Financial Intelligence)|
|COURSE CODE 33Z107698||FEES $8,000||ENQUIRY 2867-8476|
|Big Data and FinTech (Module from Postgraduate Diploma in Investment Management and Financial Intelligence)|
|COURSE CODE 33Z10771A||FEES $8,000||ENQUIRY 2867-8476|
|Equity, Debt and Alternative Investments (Module from Postgraduate Diploma in Investment Management and Financial Intelligence)|
|COURSE CODE 33Z107728||FEES $8,000||ENQUIRY 2867-8476|
|Risk and Portfolio Management (Module from Postgraduate Diploma in Investment Management and Financial Intelligence)|
|COURSE CODE 33Z107736||FEES $8,000||ENQUIRY 2867-8476|
Continuing Education Fund
- The CEF Institution Code of HKU SPACE is 100
|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 Investment Management and Financial Intelligence
Online Application Apply Now
Application Form Application FormEnrolment Method
Application Form Application Form
We provide online application and payment service for students to make enrolment via the Internet. Enrolment will be confirmed once students have made the payment online by using either PPS or credit card.
For first-come, first-served courses:
- Complete the online application form
Click the "Apply Now" button in the top right-hand corner of the course webpage to make the online application. Follow the instructions to fill in the online application form.
- Make Online Payment
Pay the course fees by either using
PPS via Internet - You will need a PPS account and a PPS Internet password. For information on how to open a PPS account and how to set up a PPS Internet password, please visit http://www.ppshk.com.
Credit Card Online Payment - Course fees can be paid by VISA or MasterCard via a secure online payment gateway for all first-come, first-served courses.
For award-bearing programmes:
Selected award-bearing programmes also provide online enrolment and payment service for its students.
If your programme accepts online enrolment and payment, a re-enrolment icon will be shown on the course webpage. Click the icon and follow the instructions to perform online enrolment and payment. You will receive relevant information from the programme team nearer the time of enrolment.
You may click here directly to access the online enrolment and payment service.
Please note the followings:
- Admission is on a first-come, first-served basis. Enrolment will be confirmed once you have made the payment online. You will receive a payment confirmation after payment has been made successfully. You are advised to keep your payment confirmation for future enquiries.
- Fees paid are not refundable except as statutorily provided or under very exceptional circumstances.
The School provides a platform for online services for a selected range of products it offers. While every effort is made to ensure timeliness and accuracy of information contained in this website, such information and materials are provided "as is" without express or implied warranty of any kind. In particular, no warranty or assurance regarding non-infringement, security, accuracy, fitness for a purpose or freedom from computer viruses is given in connection with such information and materials.
The School (and its respective employees and subsidiaries) is not liable for any loss or damage in connection with any online payments made by you by reason of (i) any failure, delay, interruption, suspension or restriction of the transmission of any information or message from any payment gateways of the relevant banks and/or third party merchants for processing credit/debit/smart card or other payment facilitation mechanism; (ii) any negligence, mistake, error in or omission from any information or message transmitted from the said payment gateways; (iii) any breakdown, malfunction or failure of those gateways in effecting online payment service or (iv) anything arisen out of or in connection with the said payment gateways, including but not limited to unauthorised access to or alternation of the transmission of data or any unlawful act not permitted by the law.
1. CASH OR EPS
Course fees can be paid by cash or EPS at any HKU SPACE enrolment counters.
2. CHEQUE OR BANK DRAFT
Course fees can also be paid by crossed cheque or bank draft made payable to “HKU SPACE”. Please specify theprogramme 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 payment sent by mail.
Applicants may also pay the course fee by VISA or MasterCard, including the “HKU SPACE MasterCard”, at anyHKU SPACE enrolment centres. Holders of the HKU SPACE MasterCard can enjoy a 10-month interest-freeinstalment 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 (FOR THE COURSE/PROGRAMME HAS ONLINE ENROLMENT ONLY)
The course fees of all open admission courses (course enrolled on first come, first served basis) and selected award-bearing programmes can be settled by using PPS via the Internet. Applicants may also pay the relevant course fees by VISA or MasterCard online. Please refer to the Online Services page on the School website.
For general and short courses, applicants may be required to pay the course fee in cash or by EPS, Visa or MasterCard if the course is to start shortly.
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, 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 transferred 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 approved transfers.
- Receipts will be issued for fees paid but HKU SPACE will not be responsible for any loss of receipt sent by mail.
- For additional copies of receipts, please send a stamped, self-addressed envelope with a completed form and a crossed cheque for HK$30 per copy made payable to ‘HKU SPACE’. Such copies will only normally be issued at the end of a course.
- More Programmes of
- Investment Management FinTech and Financial Intelligence