Brown Bag Seminar

Engage with doctoral research and prepare your referee report

Prof. Dr. Andre Guettler Director of the Institute
Helmholtzstraße 22, Room 205
andre.guettler@uni-ulm.de
+49 731 50 31 030

Oliver Padmaperuma Doctoral Candidate
Helmholtzstraße 22, Room 203
oliver.padmaperuma@uni-ulm.de
+49 731 50 31 036

5.1 Course objectives

• 5.1 Course objectives
• 5.2 The Brown Bag Seminar
• 5.3 Quick refresher — the four-part checklist
• 5.4 Conclusion of the Course

• Welcome to
• Course Objective
• Course at a glance
• Assignments / Exams

Welcome to Research in Finance

• Register for “exam” 13337 in campusonline by 30 November 2025. The registration is what binds you to the course requirements; without it you cannot submit. If you are registered but don’t submit, you receive a fail grade (5.0).
• Ask questions during or right after each session — that is the preferred channel.
• Admin / studies / exam-eligibility questions go to the registrar’s office (Studiensekretariat) at studiensekretariat@uni-ulm.de.
• Course-content questions outside class: email oliver.padmaperuma@uni-ulm.de, CC andre.guettler@uni-ulm.de.
• We also recommend the student advisory service.

Course Objective

Scope

We will:

• Prepare Master students for their empirical thesis
• Hands-on R intro for data management, visualization, cleaning, basic modelling
• Writing tips for theses, including LaTeX & Overleaf
• Referee reviews on research presentations for empirical critique skills

We will NOT:

• Deep dive into advanced stats or ML methods
• Specific finance topics (asset pricing, etc.)
• Full thesis writing / research design training

Approach

Part I — Learn the Basics

• Hands-on R intro: a widely used language for statistical computing
• Manage, visualize and clean data; run and interpret statistical models
• Solve a real empirical problem set in R, in groups

Part II — Apply your learnings

• Mandatory participation in the institute’s Brown Bag Seminar
• Two assignments (group work and individual referee report) — see Assignments / Exams

Course at a glance

Basics

Week 1

29.10.2025

Course objectives, schedule, assignments · Introduction to R · Live coding

• Course objectives, schedule and assignments
• Introduction to R and RStudio
• Live coding: variables, vectors, matrices, data frames, lists, functions, loops
• Data import and export

Data Handling & Visualization

Week 2

05.11.2025

API access, merging, cleansing, transforming and visualising financial data in R · Introduction to Overleaf

• 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

Statistical Analysis

Week 3

12.11.2025

Descriptive · inferential · modelling — applied in R

• 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)

Academic Publishing & Refereeing

Week 4

19.11.2025

What makes a great empirical paper · publication process · how to write a referee report

• 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)

Brown Bag Seminar

Week 13

20.01.2026

Engage with doctoral research and prepare your referee report

• Doctoral research presentations
• Apply empirical / writing tips for the referee report
• Group discussion and Q&A

Assignments / Exams

Assignment I — Problem Set 50% of your grade

Documented .R script + PDF write-up (Overleaf)

Group of up to 5.

Submit by emailing oliver.padmaperuma@uni-ulm.de, CC andre.guettler@uni-ulm.de. Subject pattern: Research in Finance_assignment-1-problem-set_surname1_surname2_…

19 January 2026

Assignment II — Referee Report 50% of your grade

2.5–3 page referee report on a Brown-Bag presentation

Group of up to 5.

Submit by emailing oliver.padmaperuma@uni-ulm.de, CC andre.guettler@uni-ulm.de. Subject pattern: Research in Finance_assignment-2-referee-report_surname1_surname2_…

3 February 2026

5.2 The Brown Bag Seminar

• 5.1 Course objectives
• 5.2 The Brown Bag Seminar
• 5.3 Quick refresher — the four-part checklist
• 5.4 Conclusion of the Course

• Format
• Activities
• What you’ll receive
• Your deliverable — Assignment II

Format

Logistics

• Date: 20 January 2026
• Time: 13:30–16:00
• Attendance: mandatory for all registered students
• Location: Helmholtzstraße 22, Ulm — Brown Bag room (TBA)

The Brown Bag Seminar is a regular institute event where doctoral candidates present work in progress to faculty and peers. For this course it doubles as the input for Assignment II.

The brown-bag format is a standard institute-internal seminar series — the name comes from the historical convention that attendees brought their lunch (“a brown bag”) and a presenter walked through their work-in-progress over 30–45 minutes plus open discussion. It is the typical first venue where new empirical work gets battle-tested: friendly enough to absorb honest feedback, rigorous enough to surface real issues.

Two reasons brown-bags matter for your career, beyond this course:

• As a presenter, the brown-bag is where you get the first round of substantive critique on a working paper before submitting it to a conference or journal. Expect tough but constructive questions — the institute’s faculty have refereed many papers and recognise common pitfalls. Internalising the feedback and revising before submission saves rounds at the journal.
• As an attendee, brown-bags are how you build pattern recognition for what makes a publishable empirical paper. Watching a presenter defend an identification strategy under fire, or seeing a faculty member point out a control variable that should obviously be there, is more instructive than reading any number of textbooks. The Finance Project course (the sister course to this one) and your eventual master’s thesis both build on this kind of empirical judgment.

Treat today’s session as live training data for your own future presentations and reports.

Activities

• Attend the full session (13:30 – 16:00).
• Take careful notes on each presentation — question, identification strategy, data, results.
• Apply the referee checklist from Lecture 4:
  • the question · identification · data · econometric analysis · results & conclusion.
• Discuss with peers afterwards — sharpen your judgments before writing.

A practical note-taking template that works during a 30-minute seminar:

• Question (1 line) — what specifically is the paper trying to answer? If you can’t write this in one line by minute 5 of the talk, the framing is unclear and that’s a flag.
• Identification (2–3 lines) — what variation in the data is doing the work? FE structure, IV, DiD, RDD? Note any concerns the audience raises.
• Data (1–2 lines) — source, sample, period, key sample-selection choices.
• Headline result (1 line) — sign and rough magnitude of the central effect.
• Robustness checks discussed — list the ones the presenter mentioned; note ones an attentive audience member asks for and the response.
• Q&A capture — the questions that surface in Q&A are often the same ones a referee will raise. Note the question and the presenter’s response (defensive? confident? “we’ll add that”?).

The post-seminar discussion with peers is when raw notes become judgments. Working in a team of 5 helps because each person caught something different — pool the observations, then form a consensus view of the paper’s strengths and weaknesses before drafting the report.

What you’ll receive

• The PDF of each presentation ahead of the session.
• The corresponding working paper when available. If a paper isn’t available, base your report solely on the presentation.

Topics will be announced at least one week before the seminar via Moodle.

Your deliverable — Assignment II

Referee report

• Choose one doctoral presentation.
• Write a 2.5–3 page report in academic referee style (11 pt Times New Roman, 1.5 spaced).
• Submit as a group of up to 5 students.
• Deadline: 3 February 2026, email to oliver.padmaperuma@uni-ulm.de, CC andre.guettler@uni-ulm.de.
• Include your name(s) and the title of the chosen presentation in the document.

5.3 Quick refresher — the four-part checklist

• 5.1 Course objectives
• 5.2 The Brown Bag Seminar
• 5.3 Quick refresher — the four-part checklist
• 5.4 Conclusion of the Course

• The question & identification
• Data, analysis & results

The question & identification

• Topic clearly explained? Question precise?
• Author motivates well in the introduction?
• Is the answer obvious in advance?
• Question original? Reasonable scope?

• Source of variation clearly explained?
• Endogeneity concerns addressed (reverse causality, omitted variables)?
• Error-term assumptions justified?
• Robust to alternative specifications?

Refer back to the longer notes on the Lecture 4 referee-checklist slides (in the handout for that lecture) for the full discussion of each item. The summary here is the in-seminar memory aid — fits on the back of an envelope so you can keep it visible during a presentation.

Two checks that catch the most issues quickly:

• Could you precisely state the question after reading the abstract? If not, the framing problem is the first major issue to flag.
• What’s the first omitted variable that could bias the headline coefficient? If the paper doesn’t address it (with controls, FE, or IV), that’s the first identification major issue.

These two failure-mode questions, asked at every brown-bag, will surface the right concerns 80 % of the time. The remaining 20 % is where careful reading of the empirical specification matters.

Data, analysis & results

• Clear description? Well-suited to the question?
• Could you replicate the study in five years?
• Measurement error / cross-sectional dependence discussed?
• Summary statistics used to motivate the analysis?

• Techniques well-suited? Properties of estimators discussed?
• Alternative specifications and robustness?
• Results clearly presented and used to answer the question?
• Caveats acknowledged? Conclusions well-supported?
• Are you convinced? What did you learn?

Same caveat as the previous slide — these are short prompts; the full discussion is in the Lecture 4 handout. During the seminar these questions act as a hands-on checklist that you tick as the presenter covers each one. A complete tick-off is rare even in published papers; what matters is where the gaps are, because the gaps drive your major-issues bullets.

The “are you convinced?” closing question is the integrative test. If by the end you find yourself thinking “I’d like to see X before I believe this”, that’s a major issue. If you finish with “the result is solid and I’d cite it in my own work”, you have an accept. That subjective judgement, rooted in the four checklist criteria, is what experienced referees ultimately rely on.

5.4 Conclusion of the Course

• 5.1 Course objectives
• 5.2 The Brown Bag Seminar
• 5.3 Quick refresher — the four-part checklist
• 5.4 Conclusion of the Course

• Course at a glance
• Thank you & good luck
• References

Course at a glance

Basics

Week 1

29.10.2025

Course objectives, schedule, assignments · Introduction to R · Live coding

• Course objectives, schedule and assignments
• Introduction to R and RStudio
• Live coding: variables, vectors, matrices, data frames, lists, functions, loops
• Data import and export

Data Handling & Visualization

Week 2

05.11.2025

API access, merging, cleansing, transforming and visualising financial data in R · Introduction to Overleaf

• 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

Statistical Analysis

Week 3

12.11.2025

Descriptive · inferential · modelling — applied in R

• 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)

Academic Publishing & Refereeing

Week 4

19.11.2025

What makes a great empirical paper · publication process · how to write a referee report

• 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)

Brown Bag Seminar

Week 13

20.01.2026

Engage with doctoral research and prepare your referee report

• Doctoral research presentations
• Apply empirical / writing tips for the referee report
• Group discussion and Q&A

Thank you & good luck

Last reminders

• Referee report (Assignment II) is due 3 February 2026 — work in groups of up to 5.
• Problem Set (Assignment I) deadline was 19 January 2026 — make sure your team has submitted.
• Reach out to oliver.padmaperuma@uni-ulm.de (CC andre.guettler@uni-ulm.de) for any course-content questions.
• All the best for your empirical Master’s thesis — apply what you learned, and don’t be shy about identification!

References