FINC 672: Workshop in Finance - Empirical Research
Instructor: Matthias Fleckenstein, mflecken@udel.edu
Class Times: Tuesdays and Thursdays, 2.20pm-3:40pm
Class Location: Purnell Hall 114
Office Hours: Mondays, 8.00am-9:30am, 310 Purnell Hall or by appointment.
Table of Contents
Class Times
| FINC-672 (Section 010) | |
|---|---|
| Day(s) | Tue, Thu |
| Time | 2.20pm - 3.40pm |
| Location | Purnell Hall 114 |
Office Hours
Office hours will be held in person on Mondyas, 8.00am-9:30am, 310 Purnell Hall or by appointment.
Course Webpage
https://fleckenstein-m.github.io/FINC672-SP2024/
Canvas Page
CANVASCourse Overview
This course provides an overview of empirical research in finance. The course is divided into two main parts. In the first part, students develop fundamental programming knowledge and data science skills in the Julia programming language. This part of the course is an intensive introduction to procedures for collecting, processing and analyzing financial datasets, which have become increasingly important with the emergence and popularization of "big data" and "FinTech" in finance. The second part of the course covers widely used state-of-the-art statistical methodologies in empirical asset pricing. Students will learn to apply programming techniques to real-world empirical research questions in equity and fixed-income markets.
Learning Goals
After completing this course, students will
have a thorough understanding of fundamental programming concepts in the Julia programming language, including data types, data structures, functions, control flow and loops.
understand tabular data structures and how Julia data frames are used in financial data analysis.
be able to load and save large datasets into data frames and apply advanced data conversion and transformation techniques to financial market data.
have a thorough understanding of data visualization techniques.
understand how to connect to and use online web services to download financial datasets.
be able to form and analyze stock portfolios and implement mean-variance analysis.
implement and estimate the Capital Asset Pricing Model (CAPM) as well as multi-factor models.
implement investment strategies such as a momentum trading strategy.
estimate performance measures to evaluate investment strategies and investment funds.
understand the Value at Risk (VaR) measure and test it on real financial market data.
be able to work with and analyze high-frequency financial market data such as tick-by-tick foreign exchange and Bitcoin data.
Course Syllabus
Course Materials
All course materials will be posted on the course webpage at https://fleckenstein-m.github.io/FINC672-SP2025/.
Grading Policy
Your final grade is based on i) two midterm exams, ii) a final exam, and iii) class participation. Grading is on a curve. This means that you will be ranked relative to all students in the sections based on your two midterms, the final exam, and class participation. The weighting shown below under Final Grade Breakdown determines your final rank among all students enrolled in the sections.
20% Class Participation
25% Midterm 1
25% Midterm 2
30% Final Exam
Exam Schedule
Midterm 1
March 6, 2025
Time: 2.20pm - 3.40pm
Location: Purnell Hall 114
Midterm 2
April 17, 2025
Time: 2.20pm - 3.40pm
Location: Purnell Hall 114
Final Exam
May 18, 2025
Time: 2.00pm - 4.00pm
Location: Purnell Hall 114
Assessment Component: Class Participation
You are expected to come to class and be prepared to answer questions on the material. Homework assignments will be given nearly every week and will be announced in class and posted on the course webpage. You are expected to work on the homework assignments in groups of three. Homework assignments must be submitted before the lecture starts on the date the assignment is due. In addition, I reserve the right to ask the groups to solve up to three questions from the homework assignments in front of the class. The groups can select the problems they would like to present. I also reserve the right to give Quizzes in class. The in-class quizzes will be modeled on the examples in the lecture material after the topic is covered.
License
The copyright notice to be included in any copies and other derivative work of this material is:
Copyright 2025 Matthias Fleckenstein, University of Delaware, Lerner College of Business & Economics, mflecken@udel.edu
This is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
Last modified: May 12, 2025. Website built with Franklin.jl and the Julia programming language.