Best Datasets for an MFE Capstone Project

Best Datasets for an MFE Capstone Project

A capstone is judged on whether the result would survive scrutiny. That standard starts with the data, not the model.

What a Capstone Actually Demands From Data

Most capstone projects are not graded on the sophistication of the model. They are graded on whether the finding is defensible and reproducible. A reviewer who has seen a hundred momentum backtests will not be impressed by another one. They will ask whether the universe was constructed without lookahead, whether delisted names were included, and whether the result holds once realistic costs are applied. Those questions are about data discipline, and they decide most capstone grades before the modeling is even discussed.

The practical implication is that your data choices matter more than your algorithm choices. A simple, honestly tested strategy beats a complex one built on biased data, because the simple one can be defended in a committee room and the complex one cannot.

The Data Properties That Earn Marks

Three properties separate a credible capstone dataset from a convenient one. The first is point-in-time correctness, meaning the data reflects what was known on each historical date rather than today's restated values, a discipline explained in our guide to point-in-time market data. The second is survivorship-free coverage, including securities that were later delisted, which our piece on survivorship bias shows can flip a result. The third is reproducibility, meaning a reviewer can rebuild your dataset from a documented source.

A capstone that gets these three right is already in the top tier, because most student projects quietly fail at least one of them.

Datasets That Fit a Capstone

For an event-driven capstone in particular, the data sources that drive real systematic research are mapped in our guide to market data sources for systematic research, which is a useful template for scoping what you need before you collect anything.

A Capstone Worth Defending

The strongest capstones tend to test a specific, mechanism-driven hypothesis rather than a vague signal. Equity offerings, lock-up expirations, and de-SPAC float dynamics are good candidates because the mechanism is clear and the events are dated in public filings. The methodology and the evidence behind these are laid out in Systematic Event-Driven Trading, and a structured walk-through is in our study guide to the book.

Choosing a mechanism with a clear economic story makes your results easier to interpret and your defense far easier, because you can explain why the effect should exist before you show that it does.

The Dataset That Removes the Hard Part

The hardest part of an event-driven capstone is usually building the event dataset itself, parsing filings into dated, machine-readable records without introducing lookahead. Alphanume's dilution events dataset does this for financing events, and the historical market cap dataset supplies the point-in-time size that universe construction needs. Using a ready, documented dataset also helps reproducibility, since a reviewer can see exactly where the data came from.

A Reproducibility Checklist

Before collecting anything, write down how a reviewer would rebuild your dataset. Name the exact source for prices, for size, and for events, note the date each field became available, and decide in advance how delisted names are handled. A capstone that ships this checklist alongside the results answers most of the committee's data questions before they are asked, which is precisely the impression you want to create.

The same checklist also protects you from yourself. Writing down the availability date of each variable forces you to confront lookahead before it contaminates the analysis, and committing to a delisting rule up front stops the quiet temptation to drop awkward names later. The discipline costs an afternoon and saves the defense.

From Data to a Finished Capstone

With the data settled, the capstone almost writes itself, because a defensible dataset constrains the analysis in productive ways. You know your universe, you know your event set, and you know your costs, so the remaining work is measurement and interpretation rather than firefighting data problems at midnight. Students who choose data carelessly spend the back half of the term debugging silent biases instead of refining their findings.

It also helps to write the data section of the report first, while the choices are fresh. Describing exactly how the universe was built and how events were dated turns into the part of the capstone that most clearly signals competence, and it doubles as the answer sheet for the questions a committee will ask in the defense.

How to Choose

Pick data for defensibility first. Prioritize survivorship-free, point-in-time, reproducible sources over whatever is fastest to download, and scope a project around a mechanism you can explain. Get those choices right and the modeling becomes the easy part, which is exactly the order a capstone committee rewards.