Turning Quantitative Approaches into Financial Solutions. As the sequel course in the Quantitative Data Science sequence, MATH 582 is a project-based course that guides students through conducting original quantitative research and building it into a working solution or trading strategy. Rather than following a fixed syllabus of lectures, students spend the semester defining a real-world financial problem, researching it rigorously, and implementing a testable solution — with the instructor acting as a mentor throughout.

Course Information

• Instructor: Shen-Ning Tung (tung@math.nthu.edu.tw)
• Lecture Time: Wednesdays, 14:20 – 15:10 and Fridays, 10:10 – 12:00
• Office Hours: By appointment
• Target Audience: Upper-level undergraduate and graduate students aiming to understand real-world finance.

Course Description

Each student (or team) selects a real-world financial problem and works through it in three stages:

• Context (25%): Frame the problem — analyze its real-world significance and survey the influential academic papers that address it.
• Theory (25%): Search, read, and select the quantitative techniques best suited to solving the problem.
• Implementation (50%): Build and test a working solution through code, simulation, or live market interaction.

Progress is driven by the student, not the lecture schedule: expect to spend most of your time researching, coding, and iterating on your own project, with regular checkpoints to discuss progress with the instructor and the class.

Learning Objectives

By the end of this course, students will be able to:

1. Research — identify and analyze a real-world financial problem using academic literature.
2. Solve — apply quantitative techniques to develop a testable solution or strategy.
3. Deliver — communicate findings and results clearly through code, reporting, and presentation.

Prerequisites

• Mathematics: Strong proficiency in Multivariate Calculus, Linear Algebra, and Probability Theory.
• Programming: Basic proficiency in Python (specifically NumPy and Pandas).

Evaluation & Projects

• Course Interaction (50%): This course runs on active participation. Students are expected to attend every session, engage with discussion, contribute ideas and feedback to their peers’ projects, and present their own progress regularly.
• Final Project (50%): A substantial independent or group project. You will choose a real-world financial problem and work to solve it through quantitative methods, culminating in a final presentation and written report.

The Instructor’s Role

This is not a lecture-driven course — the instructor’s primary role is as a mentor, not a lecturer. Rather than delivering content, the instructor will:

• Help you scope and refine your chosen problem into a tractable research question.
• Point you toward relevant papers, data sources, and techniques.
• Give feedback on your progress at regular checkpoints.
• Help troubleshoot the theoretical and technical obstacles you run into along the way.

The quality of the course experience depends heavily on how proactively you engage — come prepared with questions, progress, and ideas to discuss.

Logistics & AI Policy

• Communication: All announcements are made via the course Discord. Please use public channels for material-related questions to foster collaborative learning.
• Submissions: All work (notes and reports) must be submitted through GitHub.
• AI Usage: Unrestricted use of AI tools (e.g., ChatGPT, Claude) is permitted for all assignments. No formal disclosure or citation is required. However, students remain 100% responsible for the accuracy, logic, and originality of their final submissions.

Course Schedule

Resources

• Primary: Instructor-provided Course Notes.