Preparation
Course Detail
| Key area of study | A03 - BUSINESS AND MANAGEMENT |
| Course name (English) | MASTER OF SCIENCE IN FINANCIAL TECHNOLOGY |
| Course name (Chinese) | 理學碩士(金融科技) |
| Course code | 33M128973 |
| Institution name (English) | THE HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY (HKUST) |
| Institution name (Chinese) | 香港科技大學 |
| Institution code | 003 |
| Institution phone |
請點擊查詢院校電話 Please click here for Enquiry Hotline |
| Award | MASTER |
| Course fee (HK$) | $315000 |
| QR Number | 19/000921/L6 |
| QF Level | 6 |
| Remark |
Notes / 附註: *APPLICANT PURSUING THIS COURSE WITH COURSE COMMENCEMENT DATE FALLING AFTER 13 AUGUST 2024 IS NOT ELIGIBLE TO CLAIM REIMBURSEMENT FROM CEF. / 申請人報讀於二零二四年八月十三日後開課的課程並不能申領基金發還款項。 Entry Requirements / 入學要求: (1) General Admission Requirements of the University: Applicants seeking admission to a master's degree program should have obtained a bachelor’s degree from a recognized institution, or an approved equivalent qualification. (2) English Language Admission Requirements: Applicants have to fulfill English Language requirements with one of the following proficiency attainments: (a) TOEFL-iBT: 80 (b) TOEFL-pBT: 550 (c) TOEFL-Revised paper-delivered test: 60 (total scores for Reading, Listening and Writing sections) (d) IELTS (Academic Module): Overall score: 6.5 and All sub-score: 5.5 *Applicants are not required to present TOEFL or IELTS score if their first language is English, or they obtained the bachelor's degree (or equivalent) from an institution where the medium of instruction was English. Course Outline / 課程大綱: (I) Core Course (16 credits) - Total 224 hours: (1) Corporate Finance: 28 (2) Investment Analysis: 28 (3) FinTech Regulation and Compliance: 28 (4) AI for FinTech: 28 (5) Blockchain: 28 (6) Data Analysis: 28 (7) Financial Data Mining: 28 (8) Foundations of FinTech: 28 (II) Required Course (2 credits) - Total 28 hours: (9) Mathematical Foundation of FinTech: 28 (For those who didn't pass the assessment test before program commerce are required to take this course) (III) Elective Course (a selection of 14 credtis) - Total 196 hours: (10) Economics of Financial Technology: 28 (11) Entrepreneurship and Innovation in FinTech: 28 (12) Portfolio Management with FinTech Applications: 28 (13) FinTech: The Future of the Financial Industry: 28 (14) Decision Analytics for FinTech: 42 (15) Optimization in FinTech: 42 (16) Statistical Machine Learning: 42 (17) Statistical Methods in Finance: 42 Instructors' Qualifications / 導師資歷: Instructor must have a PhD degree. Assessment / 課程評核要求: (A) Assessment Items and Their Weightings: (1) Corporate Finance: (i) Two case assignments (10% each) (20%) (ii) Four problem sets (7% each) (28%) (iii) Peer Evaluation (5%) (iv) Final exam (47%) (2) Investment Analysis: (i) Class participation (15%) (ii) Quizzes (10%) (iii) Presentations (x2) (40%) (iv) Final Exam (35%) (3) FinTech Regulation and Compliance: (i) Three assignments (two in the first part of the course, and one in the second part of the course) – 25% each (ii) Class participation (heavily weighted towards the case discussions in second half of the course) – 25% total (4) AI for FinTech: Course assessment is done by means of a large practical assignment (100%) either in the field of credit scoring or algo trading. Final report and code should be submitted on Canvas. Assignment should be conducted in groups of 3 students, collaborating via GitLab and Overleaf. (5) Blockchain: (i) Assignment (50%) (ii) Project (50%) (6) Data Analysis: (i) In-class assignments (20%) (ii) Project presentation (20%) (iii) Examination (60%) (7) Financial Data Mining: (i) Assignment (20%) (ii) Project (80%) (8) Foundations of FinTech: (i) Homework (30%); Group final project (70%). [For the group final project, each group is required to submit a project report and is required to give a project presentation (15 minutes for each group).] (9) Mathematical Foundation of FinTech: (i) Scheme A: 20% Homework + 30% Midterm + 50% Final (ii) Scheme B: 20% Homework + 0% Midterm + 80% Final [Final grade = max (Scheme A, Scheme B)] (10) Economics of Financial Technology: (i) Case Study Analysis 25% (A group project, about 6 members per group) (ii) Presentation 15% (Presentation of the group project) (iii) Final Exam 60% (Individual exam) (11) Entrepreneurship and Innovation in FinTech: (a) Individual component: (i) Class participation (20%) (ii) Online discussion on topics posted by the instructor (20%) (iii) OPTIONAL: Founder/CEO Interview (20%). If you choose not to do this assignment, the assigned weight of 20% will be distributed across other areas of assessment per their extant weights. (b)Team component: (i) Initial opportunity analysis – presentation only (10%) (ii) Final opportunity analysis 15 pages (all inclusive) write-up with primary + secondary research (15%) + Presentation (15%) (12) Portfolio Management with FinTech Applications: (i) Group Presentation (35%) (ii) Team peer ranking (5%) (iii) Final Exam Group (60%) (13) FinTech: The Future of the Financial Industry: (i) Class participation 10% (attendance and class discussion) (ii) Group Project and Presentation 20% (peer-review group members and course instructor/TA) (iii) Individual Paper 30% (iv) Final exam 40% (14) Decision Analytics for FinTech: (i) Final project (50%) (ii) Homework (35%) (iii) Class participation (15%) (15) Optimization in FinTech: (i) Homework: 35% (ii) Midterm: 15% (iii) Final project: 35% (iv) Final lightening presentation: 15% (16) Statistical Machine Learning: (i) Assignment (30%) (ii) Project (20%) (iii) Final exam (50%) (17) Statistical Methods in Finance: (i) Assignment (20% of total grade) (ii) Course Project (20% of total grade) (iii) 3 hours Final examination (60% of total grade) (B) Completion Requirements: (1) In order to graduate, students must complete 30 credits of coursework, with 16 credits of foundation courses and 14 credits of elective courses, and pass all course requirements. In addition, students must attain a graduation grade average (GGA) of 2.850 or higher (out of a scale of 4.30) as required for all postgraduate students at HKUST (C) CEF Reimbursement Requirements: (1) In order to graduate, students must complete 30 credits of coursework, with 16 credits of foundation courses and 14 credits of elective courses, and pass all course requirements. In addition, students must attain a graduation grade average (GGA) of 2.850 or higher (out of a scale of 4.30) as required for all postgraduate students at HKUST (2) Attendance: 80% Delivery Mode / 授課模式: 420 hours; Both full-time and part-time mode Course Duration / 課時: 12 months for full-time and 24 months for part-time Effective Date / 課程生效日期: 14/08/2020 CEF Registration Invalid From / 基金課程登記失效日期: 14/08/2024 |
STEP 3 Reimbursable Courses
List of Reimbursable Courses