Lecture 6: Final presentations
Group presentations · Q&A · wrap-up
6.1 Welcome back
- 6.1 Welcome back
- 6.2 Logistics
- 6.3 Wrap-up & farewell
Welcome to Finance Project — Asset Management
- This is a project course: there is no central exam to register for. Sign up on the course Moodle page by 15 April 2026 so you receive announcements and the data link.
- Submit the project by 30 June 2026 as a single zip — name pattern:
Asset2026_surname1_surname2_surname3. Email it to oliver.padmaperuma@uni-ulm.de, CC andre.guettler@uni-ulm.de and your team-mates.- 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 at a glance (1/2)
Foundations
Course outline · Backtesting fundamentals
- Course aim & organisation
- Backtesting overview & case study
- In-sample tests (Welch & Goyal 2008)
- Out-of-sample (walk-forward, R²_OS)
- Useful predictors & p-hacking
Introduction to R
RStudio · variables · vectors · data frames · live coding
- Why R for empirical asset-management research
- RStudio and the script editor
- Variables, vectors, matrices, data frames, lists
- Functions and loops
- Data import and export
Assessing model accuracy & Ridge regression
Statistical learning · MSE · bias-variance · linear model selection · Ridge
- Statistical learning: Y = f(X) + ε
- Quality of fit and the train/test MSE distinction
- Bias-variance trade-off and overfitting
- OLS limits: prediction accuracy & interpretability
- Ridge regression and the L2 penalty
Lasso, cross-validation & Elastic Net
Sparse regularisation · resampling for honest test error · choosing λ
- Lasso: L1 penalty and exact-zero coefficients
- Cross-validation: validation set, LOOCV, K-fold
- Choosing the optimal λ for Lasso
- OLS post-Lasso for cleaner coefficient inference
- Elastic Net — combining Ridge and Lasso
Prediction markets, the Polymarket Quant Bench & your project
From Welch-Goyal to event-resolved binary contracts
- Prediction markets — definition and Polymarket as the canonical venue
- How prices form: liquidity, resolution, mechanics
- The Polymarket Quant Bench dataset (HuggingFace): access and schema
- First look at the data in R
- Your project: indicator design, back-test, deliverables, R toolbox
Course at a glance (2/2)
Final presentations
Group presentations · Q&A · wrap-up
- Presentation order and time budget
- Q&A rules
- Closing thoughts and feedback
A quick look back over the term
- L1: backtesting fundamentals — the IS-vs-OOS empirical discipline you’ve now applied yourselves.
- L2: hands-on R — the language you’ve used end-to-end.
- L3 + L4: the regularisation toolbox — Ridge, Lasso, Elastic Net, cross-validation.
- L5: prediction markets primer + the Polymarket Quant Bench dataset.
- Today: you present your strategies. Each group: ~20 minutes.
Notes
The course’s arc, in retrospect: we started with the empirical-finance discipline that out-of-sample testing is what matters (Lecture 1, Welch and Goyal). We added the R fluency to do it (Lecture 2). We added the modelling toolkit — bias-variance trade-off, Ridge, Lasso, cross-validation — that lets you pick a model honestly even with many candidate predictors (Lectures 3–4). We applied all of it to a real, messy dataset of resolved prediction markets (Lecture 5). Today you show what you built.
Even if your specific indicator turned out not to work, the intellectual habits — out-of-sample evaluation, walk-forward backtesting, transparent reporting of failures — generalise to any empirical research you do later. That meta-skill is what the course was really teaching; the prediction-market application was the venue, not the destination.
6.2 Logistics
- 6.1 Welcome back
- 6.2 Logistics
- 6.3 Wrap-up & farewell
Presentation order
- Slot order will be drawn from a hat at the start of class so all groups are equally prepared.
- Each slot is 20 minutes presentation + 5 minutes Q&A = 25 minutes in total.
- Switch laptops between groups during the Q&A so we don’t lose time on cable changes.
Bring two laptops per group (primary + backup) and your slides on a USB stick, just in case.
Notes
Three pieces of practical preparation:
- Time-box the practice presentation strictly. A 20-minute deck practiced once-through usually runs 25–30 minutes on the day (nerves slow you down). Practice twice; the second time should hit 18 minutes.
- Bring backup hardware. Laptops crash, projectors don’t recognise USB-C, the file you renamed last night didn’t sync to OneDrive. The two-laptop + USB stick policy is for when one of these inevitably happens.
- Know who’s saying what. A 3-person group has 20 minutes to share — typically 5 minutes per person plus an introduction and a wrap-up. Pre-assign sections; don’t decide on the day.
What we expect to see in your slides
- The question / hypothesis — what predictive idea are you testing?
- Data & universe — which Polymarket markets did you keep? Which did you exclude, and why?
- Indicators — describe at least 5; explain why each is plausibly informative.
- Strategy & back-test — signal construction, walk-forward results, key metrics, one figure that sells it.
- Robustness & limitations — where does it break? What couldn’t you check?
- Reflection — what did you learn that you’ll bring to your Master’s thesis?
Notes
The six-section structure mirrors the report’s structure but compressed for verbal delivery. A few tips per section:
- (1) Question — single slide, single sentence. The audience should know in 30 seconds what you tested.
- (2) Data & universe — a table of inclusion/exclusion criteria; one slide. State the resulting N markets.
- (3) Indicators — one row per indicator with the formula and a one-line motivation. Don’t show every code chunk; the marker reads the Rmd separately.
- (4) Strategy & backtest — the killer chart of cumulative-returns or hit-rate vs benchmark. This is the slide most of the audience will remember.
- (5) Robustness & limitations — three or four bullets. Honest reporting beats triumphant-but-fragile claims. “In the politics-only subsample our hit rate dropped from 58 % to 52 %; we discuss why in the report” is a great kind of bullet.
- (6) Reflection — what’s the next experiment you’d run? What does this make you want to do for your thesis? This is what we want to hear.
Q&A rules
- Q&A is for the group, not the lecturers — we’ll let questions flow before stepping in.
- Other groups: keep it constructive — frame critiques as “I’d be interested to see…” rather than “you should have…”.
- Keep answers short; if you don’t know, say so. The honesty is recorded as a positive.
Notes
The Q&A is graded as part of the presentation — a confident, calibrated handle on questions adds points; defensiveness or evasive answers subtract. Three failure modes to avoid:
- Defensive overreach — “Why didn’t you cluster standard errors?” is not an attack; it’s a probe of your methodological awareness. The right answer is either “we did, here’s where in the report” or “we considered it but chose X for Y reason” — not “the result holds anyway”.
- Inventing answers — making up justifications for choices you can’t actually defend is much worse than saying “we didn’t think about that”. Markers can almost always tell when a justification is post-hoc.
- Talking too long — short, focused answers leave room for follow-ups. A two-minute monologue per question burns the Q&A and leaves the audience disengaged.
For other groups: constructive Q&A makes the session better for everyone. “I’d be interested to see how this performs on sports markets specifically” is a useful question; “you should have used time-series CV” is the same observation phrased better.
6.3 Wrap-up & farewell
- 6.1 Welcome back
- 6.2 Logistics
- 6.3 Wrap-up & farewell
Grading & feedback
- Project report: 50% — Rmd + knitted PDF, 10–15 pages.
- Final presentation: 50% — what you do today.
- We’ll publish individual feedback within two weeks of today.
- For the course-wide debrief we’ll send a short anonymous survey via Moodle — please fill it in. It directly shapes the next iteration.
A short word of thanks
- Thank you for engaging with a non-traditional asset class — prediction markets are messy on purpose.
- Special thanks to the groups who flagged issues with the dataset during the project phase: that flows back into the data dump for next year’s cohort.
- Stay in touch — we’re happy to talk about Master’s thesis topics in the same area, including extending today’s work to Kalshi, NFL futures, or macro-conditional event pricing.
Notes
The thesis-extension paths mentioned are concrete project shapes for anyone who wants to push the work further:
- Kalshi — US-regulated prediction-market exchange with a different (and arguably better-designed) market structure than Polymarket. Comparing your Polymarket strategy on Kalshi data is a natural extension; the dataset is publicly available via Kalshi’s API.
- NFL futures — sports betting markets are a richer environment for some indicator types (player news, injury reports, weather). Bookmaker odds (Pinnacle, Betfair) are accessible.
- Macro-conditional event pricing — combining prediction markets with macro state (interest-rate environment, equity-market regime). Less explored academically; could be thesis-quality.
The institute is happy to supervise theses in this area; reach out by email if you want to discuss extending today’s work into a Master’s project. Several recent institute publications grew out of project-class kernels — there’s a real path from “course project” to “publishable working paper” if you find a thread worth pulling.
Good luck out there
- Make sure your zip submission landed in oliver.padmaperuma@uni-ulm.de’s inbox by 30 June 2026, 18:00.
- Push your code to a private repo (GitHub, GitLab, or Cloudstore) — useful for thesis interviews.
- The most underrated skill we can hand off: be the person who opens the data and runs
glimpse()first. Make it a habit.