PhD in Digital Financial Technology

Course Works

PhD students are required to complete a minimum of 38 Units courses (For Intake 2024 onwards) / 36 Units (For intakes before 2024).

The objective of the curriculum design in the first year is to encourage students to learn important subjects related to FinTech from three different traditional disciplines (1) Computing, (2) Finance and Economics, (3) Data Science and Quantitative Methods. At the same time, the curriculum design maintains the flexibility for students who have strong background in one area to focus on taking courses from other areas. PhD students are required to explore different areas in the first year and nominate their thesis supervisor by the end of the first year.

Specifically, in the first year, students are required to take a minimum of 16 Units courses including the 4 Units lab rotation course, one 4 Units course from Computing, one 4 Units course from Finance and Economics, and one 4 Units course from Data Science and Quantitative Methods. Students are to also complete the compulsory 4 Units course; NG50001 and 2 Units course; NG5002.

The list of recommended courses is provided below.

  1.  (4 Units) Recommended Essential Courses in Computing. Students are expected to acquire graduate-level training in computing that are most relevant to their research.
    • CS5218 Principles and Practice of Program Analysis
    • CS5231 Systems Security
    • CS5232 Formal Specification and Design Techniques
    • CS5233 Simulation and Modelling Techniques
    • CS5246 Text Processing on the Web
    • CS5331 Web Security
    • DSA5204 Deep Learning and Applications
    • or any 6XXX courses from School of Computing
    • or FT5XXX courses upon approval of PhD program director
  1. (4 Units) Recommended Essential Courses in Finance and Economics. Students are expected to acquire advanced PhD-level training for empirical or theoretical research in financial economics.
    • EC5101 Microeconomic Theory
    • EC5102 Macroeconomic Theory
    • EC5103 Econometric Modelling and Applications I
    • EC6101 Advanced Microeconomic Theory
    • EC6102 Advanced Macroeconomic Theory
    • EC6103 Econometric Modelling and Applications II
    • or any FIN6XXX courses
  1. (4 Units) Recommended Essential Courses in Data Science and Quantitative Methods. Students are expected to acquire advanced PhD-level training in statistics or quantitative methods in finance.
    • DSA5205 Data Science in Quantitative Finance
    • MA5248 Stochastic Analysis in Mathematical Finance
    • MA5269 Optimal Stopping and Stochastic Control in Finance
    • MA6235 Topics in Financial Mathematics
    • QF5210 Financial Time Series: Theory and Computation
    • or any 6XXX courses from FoS related to finance
  1. (4 Units) Lab Rotation
    • The lab rotation will follow prevailing Integrative Sciences and Engineering Programme (ISEP) or NUS Graduate School Rules.
    • All new students must complete two (2) lab rotations within their first semester with one AIDF-PI and one AIDF-affiliated supervisor. Each rotation can last 6-8 weeks, with a minimum of 3 months required for two lab rotations. For example, the first rotation will run from week 1 to week 6 of the NUS academic calendar. The second rotation will run from week 7 to week 12.
    • Students have to submit a report for each lab rotation to the supervisor at the end of each rotation. The report should fulfil the following:
      1. 600 words (min) including a background of the research project, objective(s) of the project, methodology, results and discussion.
      2. 5 pages (max) including tables, figures, references, etc.
  1. (6 Units) Compulsory Course
    • NG5001 (Graded with CS/CU option), Academic Communication for Graduate Researchers (4 Units)
    • NG5002 (Graded with CS/CU option), Research Ethics for Graduate Researchers (2 Units) - For 2024 Intake onwards

By the time of graduation, the student needs to take 38/36 Units courses that meet the following conditions.

  1. Pass a minimum of 8 Units courses at 6000-level.
  2. In principle, students need to take at least 8 Units from SoC or FoE, 4 Units with course code starting with EC, FIN, or BZD, and 4 Units from FoS (with course code starting with DSA, MA, ST, or QF).

The curriculum is designed to encourage students to have a balanced training in three reference disciplines of FinTech so that students can conduct high-quality research projects by using knowledge and techniques from more than one traditional discipline. Examples of electives in 3 areas are

  • Computing and Technologies
    1. CS6203 Advanced Topics in Database Systems
    2. CS6207 Advanced Natural Language Processing
    3. CS6208 Advanced Topics in Artificial Intelligence
    4. CS6211 Analytical Performance Modelling for Computer Systems
    5. CS6216 Advanced Topics in Machine Learning
    6. CS6231 Advanced Topics in Security and Privacy
    7. CS6234 Advanced Algorithms
    8. CS6240 Multimedia Analysis
    9. CS6285 Topics in Computer Science: Bridging System and Deep Learning
  • Finance and Economics
    1. EC5322 Industrial Organization
    2. EC5326 Policy Impact Evaluation Methods
    3. EC6322 Advanced Industrial Organization
    4. BZD6003 Applied Econometrics I
    5. BZD6004 Applied Econometrics II
    6. BZD6005 Applied Econometrics III
    7. FIN6001 Empirical Corporate Finance and Financial Intermediation
    8. FIN6002 Corporate and Financial Intermediation Theory
    9. FIN6003 Asset Pricing and Microstructure Theory
    10. FIN6004 Empirical Asset Pricing and Microstructure
  • Data Science and Quantitative Methods
    1. ST5210 Multivariate Data Analysis
    2. ST5215 Advanced Statistical Theory
    3. ST5224 Advanced Statistical Theory II
    4. ST5222 Advanced Topics in Applied Statistics
    5. ST5223 Statistical Models: Theory/Applications
    6. MA5243 Advanced Mathematical Programming
    7. MA5248 Stochastic Analysis in Mathematical Finance
    8. QF5210 Financial Time Series: Theory and Computation