R代写-MFIT 5008

Hong Kong University of Science and Technology
MSc in Financial Technology

MFIT 5008: Decision Analytics in FinTech (L1) (3 credits)
Spring Term 2020-2021

1. GENERAL INFORMATION
Instructor: Alfred Ma
Email:

Zoom ID for office hours:
Office hours dates:
alfredmakm@ust.hk

Teaching Assistant:
Email:
Office & Office hours:

Class Dates: Feb 1,8,22 Mar 1,8,15,22,29, Apr 12,19,26, May 3, 10
19:00-21:50 (Mon)
Course Prerequisite(s):
Course Exclusion(s): Not applicable
Canvas course site: https://canvas.ust.hk/courses/

2. COURSE DESCRIPTION
This course aims to introduce decision analytics instruments and their applications in FinTech. Main topics covered in this
course include basic probability and statistics, predictive analytics, prescriptive analytics such as linear programming, integer
programming, dynamic programming and sequential decision making, stochastic models, quality control, Monte Carlo
simulation, game theory, and their applications in various areas of FinTech.
3. COURSE OBJECTIVES
The objective of this course is to teach students various important decision analytics tools such that they are equipped with this
important toolkit in FinTech.

4. COURSE LEARNING OUTCOMES
Course Learning Outcomes

CLO1. Apply predictive analytics to modeling and analysis
CLO2. Apply prescriptive analytics to modeling and analysis
CLO3. Understand the applications of decision analytics to various FinTech-related problems

5. COURSE TEACHING AND LEARNING ACTIVITIES

Course Teaching and Learning Activities
Expected
contact hours
(% of study)
Lectures
Learning activities
Projects
39
10
10
66%
17%
17%
Total 100%

Assessment Methods
(Homework assignment, Exam,
Quiz, Class participation etc)
Brief Description (Optional) Weight (%) Aligned Course Learning
Outcomes
(CLO no in section 4)
Midterm Exam

Final Exam

Group project

20%

40%

40%

1

1,2,3

1,2,3

Total 100%

6. STANDARDS FOR ASSESSMENT
A+, A, A-
B+, B,
B-, C+, C
F

Excellent Performance
Good Performance
Marginal Performance
Failure

7. COURSE CONTENT AND TENTATIVE TEACHING SCHEDULE
Week/Session
Week 1
Week 2
Week 3
Week 4
Week 5
Week 6
Week 7
Week 8
Week 9
Week 10
Week 11
Week 12
Week 13
Course Content
Introduction to decision analytics and FinTech
Review of probability theory with applications
Hypothesis testing and linear regression
Predictive analytics
Prescriptive analytics I: linear programming and integer programming
Midterm Exam
Prescriptive analytics II: sequential decision making
Monte Carlo simulation
Stochastic modeling and applications
Decision analysis
Selected applications in FinTech
Group Project Presentation
Final Exam

8. TEACHING MATERIALS (e.g. journals, textbooks, website addresses etc.)
Required Materials:
Class notes and other materials will be distributed via the course website.

9. MEANS/PROCESSES FOR STUDENT FEEDBACK ON COURSE
a) Complete online Student Feedback Questionnaires (SFQ) Survey during two weeks before the course end at:
Canvas website (https://canvas.ust.hk) or
SFQ Mobile website (http://sfq.ust.hk/mobile/) or
HKUST iLearn app at smartphones / tablets
10. COURSE POLICY (e.g. plagiarism, academic honesty, attendance, etc.)

Academic integrity and honesty are critical values in upholding HKUST's reputation as a community of scholars and its claim
to the "intellectual property" created by staff and students. All deliverables presented for assessment must reflect the work
of the individual or the team presenting these as their own. Ideas, passages, data, and analysis from other sources must be
fully acknowledged and properly cited. Failure to do so, or excessive reliance on other sources will lead to uncontestable
failure in the course and possibly the program.

Examination
Cheating will not be tolerated! You should answer all questions individually without assistance from third parties. Any
student caught cheating during the exam will receive a fail grade and may face further disciplinary action. Please refer to
http://www.ust.hk/vpaao/integrity/ for HKUST rules regarding academic integrity.

11. ADDITIONAL COURSE INFORMATION (e.g. e-learning platforms & materials, penalty for late assignments, etc.)

Late assignments will not be accepted unless the student has a genuine reason and has discussed his situation and has
obtained specific approval.