Research in Finance
Institute of Strategic Management and Finance, Ulm University
About this course
Research in Finance equips Master’s students with the practical toolkit and conceptual grounding needed to conduct empirical finance research. Across four lectures and a final Brown Bag Seminar, students learn to handle financial data in R (import, cleansing, merging, transformation, visualisation), run statistical analyses (correlations, parametric and non-parametric tests, linear regression with fixed effects, clustered standard errors), write up empirical results in LaTeX / Overleaf with publication-ready tables and figures, and read and referee a finance research paper. The course is project-based: a group problem set replicates a complete empirical workflow on CFTC futures data; an individual referee report critiques a doctoral presentation at the Institute’s Brown Bag Seminar.
Course at a glance
| Term | Winter 2025/2026 |
| Level | Master / PhD |
| ECTS | 6 |
| Language | English |
| When | Wednesdays 14:15–15:45 |
| Where | Helmholtzstraße 22, Ulm |
| Format | 4 lectures + 1 Brown Bag Seminar, group work + individual referee report |
| Assessment | Problem set (50%) + Referee report (50%) |
| Registration | “Exam” 13337 in campusonline by 30 November 2025 |
Learning outcomes
By the end of the course, students will be able to:
- Wrangle financial data sets in R end-to-end (import, clean, merge, transform, visualise).
- Run and interpret core statistical analyses for empirical finance — including OLS with fixed effects and clustered standard errors.
- Draft a publication-ready empirical write-up in LaTeX / Overleaf, with
stargazertables andggplot2figures. - Critically read a finance research paper and write a structured referee report.
Required materials & setup
Bring a laptop to every session — Lecture 1 is hands-on. Install the following before Lecture 1:
R (≥ 4.3) — https://cran.r-project.org/
RStudio Desktop — https://posit.co/download/rstudio-desktop
An Overleaf account (free tier) — https://www.overleaf.com
Nasdaq Data Link API key (free) — https://data.nasdaq.com; copy the personal API key from your profile after registration.
Reference texts: @james2021introduction (free PDF + exercises at https://www.statlearning.com), @scheuch2023tidy (free at https://www.tidy-finance.org/r/), @wickham2023r4ds (free at https://r4ds.hadley.nz).
R packages (install once, load per project):
Bucket Packages Data wrangling tidyverse,lubridate,data.tableFinance APIs tidyquant,QuandlVisualisation ggplot2,kableExtraStatistics stargazer,lmtest,sandwich,fixestReporting rmarkdown,knitr
Schedule
| Week | Date | Session |
|---|---|---|
| 1 | Oct 29, 2025 | Lecture 1: Basics |
| 2 | Nov 5, 2025 | Lecture 2: Data Handling & Visualization |
| 3 | Nov 12, 2025 | Lecture 3: Statistical Analysis |
| 4 | Nov 19, 2025 | Lecture 4: Academic Publishing & Refereeing |
| 13 | Jan 20, 2026 | Brown Bag Seminar |
Assessment & deadlines
- Assignment I — Problem Set —
50%of the final grade. Documented.Rscript + PDF write-up (Overleaf). Due 19 January 2026. See the problem-set brief. - Assignment II — Referee Report —
50%. 2.5–3 page referee report on a Brown-Bag presentation. Due 3 February 2026. See the referee-report brief. - Group size: up to 5 students.
- Registration: you must register for “exam” 13337 in campusonline by 30 November 2025. Without this registration you cannot submit; registered students who fail to submit receive a fail grade (5.0).
Instructors
- Prof. Dr. Andre Guettler — Director of the Institute · Helmholtzstraße 22, Room 205 · andre.guettler@uni-ulm.de
- Oliver Padmaperuma — Doctoral Candidate · Helmholtzstraße 22, Room 203 · oliver.padmaperuma@uni-ulm.de
Quick links
- Syllabus
- Assignment I — Problem Set (50% · due 19 January 2026)
- Assignment II — Referee Report (50% · due 3 February 2026)
- Gradebook (staff only)
Access & contact
- Materials: all student-facing materials are linked from the course Moodle page. The links above are for staff convenience.
- Course-content questions: ask in class (preferred) or email oliver.padmaperuma@uni-ulm.de, CC andre.guettler@uni-ulm.de.
- Studies / exam-eligibility: studiensekretariat@uni-ulm.de.
- Technical (Moodle / IT): helpdesk@uni-ulm.de.