Georgia Tech QCF: Data for Projects

Georgia Tech QCF: Data for Projects

Georgia Tech's program spans business, math, and engineering. The data should hold up across all three lenses at once.

An Interdisciplinary Program

Georgia Tech's Quantitative and Computational Finance program is genuinely interdisciplinary, drawing on the business school, mathematics, and industrial and systems engineering. Projects are often reviewed by faculty with different priorities, where a mathematician asks about rigor, an engineer about implementation, and a finance professor about economic meaning. Data that satisfies one lens can fail another, so QCF projects benefit from a dataset that holds up across all three.

For a QCF project, the unifying requirement is a clean, reproducible dataset whose construction can be explained to any of those audiences without special pleading.

Data Requirements That Satisfy Every Reviewer

The common ground across the three lenses is point-in-time, survivorship-free, reproducible data. Rigor demands no lookahead, addressed in our guide to point-in-time market data; honesty demands including the failures, addressed in our piece on survivorship bias; and a clear economic mechanism demands data that maps to a real-world event.

A dataset that meets all three is harder to assemble than a convenient download, and it is exactly what makes an interdisciplinary project defensible to a mixed committee.

Datasets That Fit a QCF Project

The sources that support credible systematic studies are mapped in our guide to market data sources for systematic research.

A Project That Reads Well to All Three

An event-driven study satisfies every lens: the event windows are mathematically clean, the pipeline is reproducible, and the mechanism is economically clear. The framework is documented in Systematic Event-Driven Trading, with a structured overview in our study guide.

Alphanume's dilution events dataset provides the dated events, and the historical market cap dataset supplies point-in-time size, giving you a dataset that answers the rigor, reproducibility, and mechanism questions at once.

Passing Three Reviews at Once

Imagine the project in front of all three reviewers. The mathematician checks that no variable uses future information and is satisfied because the data is point-in-time. The engineer asks to rebuild the pipeline and can, because the sources are documented. The finance professor asks why the effect should exist and gets a clear mechanism. A single clean, event-anchored dataset is what lets one project pass all three reviews without compromise.

The interdisciplinary review is unforgiving precisely because the weakest lens decides the grade. A dataset that is rigorous but undocumented, or reproducible but economically meaningless, fails one of the three. Meeting all three at once is the whole challenge, and it is a data problem as much as a modeling one.

Communicating Across Disciplines

An interdisciplinary project also has to be communicated across disciplines, which is a skill in itself. The same result has to be explained to a mathematician in terms of rigor, to an engineer in terms of implementation, and to a finance professor in terms of mechanism. A clean, well-documented, event-anchored dataset gives you a common foundation that each audience can interrogate on its own terms.

Practicing those three explanations is good preparation for the defense. The questions will come from different angles, and a project whose data holds up under all of them lets you answer each reviewer in their own language without contradicting yourself, which is the heart of the QCF challenge.

How to Choose

Pick data that survives every lens. For a Georgia Tech QCF project, use point-in-time, survivorship-free, reproducible sources anchored to a clear economic event, so a mixed committee finds nothing to object to. Interdisciplinary review rewards a dataset that is rigorous, auditable, and meaningful all at once.