Syllabus — Research in Finance
Course information
| Title | Research in Finance |
| Term | Winter 2025/2026 |
| Level | Master / PhD |
| ECTS | 6 |
| Exam number | 13337 |
| Instructors | Prof. Dr. Andre Guettler · Oliver Padmaperuma |
| Contact | oliver.padmaperuma@uni-ulm.de |
| Location | Helmholtzstraße 22, Ulm |
| Time | Wednesdays 14:15–15:45 |
| Language | English |
Course objectives
This course teaches the practical craft of empirical finance research. Students learn to manage and analyze financial data in R, write up results in LaTeX/Overleaf, and critically read finance papers. The course culminates in two graded deliverables: a group problem set replicating a CFTC futures-position study and an individual referee report on a doctoral presentation.
Learning outcomes
By the end of the term, students will be able to:
- Set up R/RStudio and use the language fluently for data analysis (vectors, data frames, functions, loops).
- Import financial data from APIs and CSVs, cleanse, merge, transform, and visualize it with
tidyverse/ggplot2. - Apply standard statistical tests (correlation, t-test, Wilcoxon, Shapiro-Wilk, KS) and run linear regressions with fixed effects and clustered standard errors.
- Produce publication-ready LaTeX tables (
stargazer) and embed analyses in an Overleaf write-up. - Critique an empirical finance paper using a structured referee-report checklist.
Prerequisites
- Bachelor-level statistics / econometrics.
- Comfort with one statistical software environment is helpful but not required — the course teaches R from the ground up.
Required materials
- Laptop with R and RStudio installed (instructions given in Lecture 1).
- A free Nasdaq Data Link (Quandl) API key.
- An Overleaf account (uni email).
Recommended reading
- Wickham and Çetinkaya-Rundel (2023) — practical foundations for data wrangling and visualisation in R.
- Scheuch, Voigt, and Weiss (2023) — a finance-specific application of the tidyverse, freely available online.
- Angrist and Pischke (2009) — the standard graduate text on causal inference and empirical strategies in economics.
- Cochrane (2005) — writing tips by John Cochrane (available on Moodle).
Timetable
| Week | Date | Session | Topics |
|---|---|---|---|
| 1 | Oct 29, 2025 | Lecture 1: Basics | Course objectives, schedule and assignments, Introduction to R and RStudio, Live coding: variables, vectors, matrices, data frames, lists, functions, loops, Data import and export |
| 2 | Nov 5, 2025 | Lecture 2: Data Handling & Visualization | API access (Nasdaq Data Link / Quandl, FRED, Yahoo, Coingecko, Polygon), Import and cleanse: read_csv, mutate, types, Merge and append data (merge, bind_rows), Filter and mutate (dplyr): subset rows, derive variables, Group by and summarise, Pivot wide / long, Data visualization with ggplot2 (six-step pipeline), Introduction to LaTeX and Overleaf |
| 3 | Nov 12, 2025 | Lecture 3: Statistical Analysis | Descriptive statistics in R, Correlation matrix and Pearson correlation test, t-Test and Wilcoxon test, Shapiro-Wilk and Kolmogorov-Smirnov tests, Linear regression with fixed effects, Clustered standard errors, Exporting regression tables with stargazer, Discussion of Assignment I (Problem Set) |
| 4 | Nov 19, 2025 | Lecture 4: Academic Publishing & Refereeing | What makes a good empirical paper (contribution, identification, write-up), The publication process step by step, Top finance and economics journals, Bad outcome vs revise & resubmit, Referee Reports — summary, major issues, minor issues, Referee checklist (question, identification, data, econometrics, results), Discussion of Assignment II (Referee Report) |
| 13 | Jan 20, 2026 | Brown Bag Seminar | Doctoral research presentations, Apply empirical / writing tips for the referee report, Group discussion and Q&A |
Adding a new lecture folder under
lectures/automatically updates this table — no manual edits needed.
Assessment & grading
| Component | Weight | Deadline |
|---|---|---|
| Assignment I — Problem Set (group, R + LaTeX) | 50% | 19 January 2026, midnight |
| Assignment II — Referee Report (group of up to 5) | 50% | 3 February 2026 |
There is no written final exam. Registration in campusonline for exam 13337 is mandatory by 30 November 2025. Without registration you cannot submit either deliverable.
Grading scale: German scale 1.0 (excellent) – 5.0 (fail). 4.0 is passing. Registered students who do not submit receive 5.0.
Submission rules
- Submit by email to oliver.padmaperuma@uni-ulm.de with andre.guettler@uni-ulm.de in CC.
- Use the subject and file-naming pattern
RiF2025_ProblemSet_surname1_surname2_...(or_RefereeReport_). - If attachment size is an issue, share a cloud link in the email body.
Policies
Group work
- Problem set: groups of up to five students.
- Referee report: groups of up to five students.
- Declare group composition on the cover page.
Academic integrity
All work must be your group’s own; cite all sources. Use of LLMs (e.g., ChatGPT) is permitted only to refine writing and ideas — not to generate the substantive content — and must be disclosed.
Questions
Asking questions during or immediately after class is preferred. Administrative matters (study program, exam eligibility, formalities) should go to the registrar’s office: studiensekretariat@uni-ulm.de.
Contact
- Prof. Dr. Andre Guettler — Helmholtzstraße 22, Room 205 · andre.guettler@uni-ulm.de · +49 731 50 31 030
- Oliver Padmaperuma — Helmholtzstraße 22, Room 203 · oliver.padmaperuma@uni-ulm.de · +49 731 50 31 036
- Moodle — all announcements, file drops, problem-set release, and grade postings happen on the course Moodle page.