CMU MSCF: Data for Projects and Theses

CMU MSCF: Data for Projects and Theses

CMU's program is heavily computational. The data layer should be as engineered and reproducible as the models built on top of it.

What the MSCF Program Emphasizes

Carnegie Mellon's MSCF is known for its computational depth, drawing on statistics, machine learning, and serious programming across an interdisciplinary faculty. Projects from that environment tend to be code-heavy and methodologically ambitious, which puts unusual pressure on the data pipeline. A sophisticated model trained on biased or non-reproducible data is a sophisticated mistake, and a computational program is exactly where reviewers will notice.

For an MSCF project, the data layer deserves the same engineering rigor as the model. Reproducibility, in particular, is a property the program's culture values and is easy to demonstrate with a documented data source.

Data Requirements for Computational Work

Machine-learning and computational finance projects are especially vulnerable to leakage, because a flexible model will happily exploit any future information that creeps into the features. Point-in-time correctness is therefore not optional, a point developed in our guide to point-in-time market data. Survivorship-free coverage matters just as much, since a model trained only on survivors learns a biased world, as our piece on survivorship bias shows.

The more powerful the model, the more carefully the data has to be constructed, because capacity to fit is also capacity to fit noise and leakage.

Datasets That Fit an MSCF Project

Reconstructing point-in-time size for features is a common stumbling block, addressed in our note on historical market cap data.

A Project That Suits the Computational Bent

Event-driven prediction is a natural fit for an MSCF project: you can frame financing events as labels and study whether features predict the post-event drift, with the mechanisms documented in Systematic Event-Driven Trading. The structure keeps the machine learning honest, because the event dates anchor the labels in time.

Alphanume's dilution events dataset provides dated event labels, and the historical market cap dataset supplies point-in-time features, both designed to keep a flexible model from leaking future information.

Framing the Labels for an MSCF Model

For a computational project, the framing that keeps a model honest is to treat each financing event as a labeled example anchored in time. The label is the post-event drift over a fixed horizon, the features are point-in-time characteristics known before the event, and the train and test split respects chronology so no future information leaks across it. That structure lets you bring the program's machine-learning tools to bear without the leakage that makes flexible models look better than they are.

The discipline matters more here than in a simple linear study, because a high-capacity model will exploit any temporal leak you leave open. Anchoring labels and features to event dates is the single most important safeguard, and it depends entirely on having dated, point-in-time data underneath.

Guarding Against Subtle Leakage

Computational projects fail in subtle ways, and a deliberate leakage audit is worth the time. Check that every feature is computed only from information available before the prediction date, that the train and test split is chronological, and that the universe on each date is point-in-time. A single feature that quietly peeks at the future can produce an impressive, worthless result that an MSCF reviewer will spot immediately.

The audit is easier when the data is already point-in-time, because the temptation to use convenient future-known values never arises. Building on dated, point-in-time inputs removes the most common source of leakage at the root rather than asking you to catch it after the fact.

How to Choose

Engineer the data with the same care as the model. For an MSCF project, prioritize point-in-time, survivorship-free, reproducible datasets, and use dated events to anchor any predictive labels. The more computational the project, the more the data discipline is what makes the result trustworthy.