UC Berkeley MFE: Capstone Data Sources

UC Berkeley MFE: Capstone Data Sources

Berkeley's program is built around an applied, industry-facing project. The data should be the kind a sponsor would actually trust.

An Industry-Facing Program Needs Industry-Grade Data

Berkeley's Haas MFE is a one-year program organized around an applied finance project, often tied to an industry sponsor. That structure means the deliverable is judged less like a term paper and more like a piece of work a firm might use. A sponsor or a practitioner reviewer will ask whether the backtest reflects what could actually have been traded, which puts the data under the same scrutiny a real desk would apply.

For a Berkeley capstone, the practical question is whether your result would survive contact with a skeptical practitioner. That standard is met or lost in the data layer long before the presentation.

Scoping Data to an Applied Project

An applied project benefits from data that is realistic about costs and constraints, not just clean prices. Survivorship-free coverage and point-in-time correctness are the foundation, covered in our piece on survivorship bias and our guide to point-in-time market data, and for a tradeable strategy you also want to model realistic frictions.

Because the project is time-boxed to roughly a year, scoping matters. A focused, well-sourced study beats an ambitious one that runs out of runway, and event-driven topics are easy to scope precisely.

Datasets That Fit a Berkeley Capstone

The data sources behind credible systematic strategies are mapped in our guide to market data sources for systematic research, which doubles as a scoping checklist.

A Capstone a Sponsor Would Respect

An event-driven strategy makes a strong applied project because the signal has a clear economic rationale and the test can be made realistic. The mechanisms, evidence, and failure modes are documented in Systematic Event-Driven Trading, with a structured overview in our study guide, giving you a defensible narrative as well as a result.

Alphanume's dilution events dataset provides dated financing events, and the historical market cap dataset supplies point-in-time size, so a one-year project can reach a realistic, sponsor-ready result without building the data layer from scratch.

Scoping for the One-Year Timeline

Because the program runs in roughly a year, a Berkeley capstone has to be scoped to finish. A workable shape is a single event catalyst, a defined universe and period, and a clear cost model, with the data requirements pinned down before collection begins. Starting from a ready event dataset rather than scraping filings by hand is often what makes the difference between a polished result and one that runs out of time at the presentation.

Tight scope also plays to the applied framing. A sponsor or practitioner reviewer would rather see one realistic strategy implemented carefully than an ambitious idea left half-tested, and the time pressure of a one-year program makes disciplined scoping a feature rather than a constraint.

Presenting to a Sponsor

An applied project often ends in a presentation to a sponsor or industry panel, who will probe the result the way they would a pitch. They ask whether the backtest reflects what could have been traded, whether costs were realistic, and whether the signal has a reason to exist. A project built on point-in-time, survivorship-free data with explicit costs answers those questions directly, which is what earns credibility in that room.

Anticipating the sponsor's skepticism shapes the project from the beginning. You include the failures, you model the frictions, and you keep the mechanism clear, because each of those is something a practitioner would otherwise raise as a reason to doubt the work.

How to Choose

Choose data a practitioner would trust. For a Berkeley MFE capstone, prioritize survivorship-free, point-in-time sources, model realistic frictions, and scope the project tightly around a clear mechanism. The applied framing of the program rewards a result that would hold up in front of the people who fund the work.