Baruch MFE: Data Sources for the Capstone

Baruch MFE: Data Sources for the Capstone

Baruch's program is built around practitioner-grade rigor and a real project. The data you choose should meet the same standard.

What the Baruch Program Rewards

Baruch's MFE has a reputation for practitioner-oriented rigor, strong numerical and C++ foundations, and close ties to the New York quant industry. Projects coming out of that environment tend to be judged the way a desk would judge them, on whether the implementation is correct and the result would hold up in practice. That culture raises the bar on data, because a practitioner audience asks hard questions about how a backtest was actually constructed.

For a Baruch capstone, the implication is that data discipline is part of the deliverable, not a footnote. A clean, well-documented dataset signals the same engineering care the program emphasizes elsewhere.

Data Requirements That Match the Program

A practitioner-grade project needs data that reflects reality as it was tradeable. That means survivorship-free coverage, point-in-time correctness, and realistic cost modeling. The reasoning behind point-in-time data is in our explainer on point-in-time market data, and the cost of getting survivorship wrong is shown in our piece on survivorship bias.

Because Baruch projects often involve careful implementation, they are a natural fit for event-driven studies where the mechanism is explicit and the engineering is visible.

Datasets That Fit a Baruch Capstone

Reconstructing historical size correctly is harder than it looks, which our note on historical market cap data explains, and it is a common place practitioner reviewers probe.

A Project That Plays to the Program's Strengths

An event-driven short study fits Baruch's engineering culture well: it requires a clean event dataset, careful event-window construction, and honest cost modeling, all of which showcase implementation skill. The mechanisms and evidence are laid out in Systematic Event-Driven Trading, with a structured overview in our study guide.

Alphanume's dilution events dataset and the historical market cap dataset provide the dated events and point-in-time size such a project needs, so you can spend your effort on the implementation the program cares about.

A Project Outline for a Baruch Capstone

A capstone that fits the program might short equity issuers around their offerings, implemented with the engineering care Baruch emphasizes. You would build a survivorship-free issuer universe, parse offering dates into a clean event set, construct event windows, model borrow and trading costs explicitly, and present both the result and a clear-eyed account of the failure modes. The implementation quality is visible at every step, which is what a practitioner audience rewards.

Outlined this way, the project doubles as an engineering showcase. The event dataset, the cost model, and the universe construction each demonstrate the numerical and software skills the program is known for, so the data work is not overhead but part of the deliverable.

From Capstone to Desk

A Baruch capstone built this way translates directly into interview material, which suits the program's industry orientation. The same event dataset, cost model, and clean universe that earn marks also answer the questions a trading desk asks, so the project does double duty as a graded deliverable and a portfolio piece. Framing it with that reuse in mind from the start is efficient.

It is worth keeping the pipeline reproducible for exactly that reason. A capstone you can rerun and extend after the program is a living portfolio asset, whereas one frozen in a one-time export is just a grade, and the difference is mostly in how the data was sourced.

How to Choose

Treat data as part of the engineering. For a Baruch capstone, choose survivorship-free, point-in-time, well-documented sources, model costs honestly, and anchor the project to a clear mechanism. That approach matches the practitioner standard the program is known for and gives you a result you can defend to a tough room.