Cornell Financial Engineering: Data Sources

Cornell Financial Engineering: Data Sources

Cornell's FE concentration grows out of operations research. The data layer should be as carefully modeled as the stochastic systems built on it.

An Operations-Research Foundation

Cornell's financial engineering concentration sits within its operations research and information engineering tradition, with a strong emphasis on stochastic modeling, simulation, and rigorous systems thinking. Projects from that background often involve carefully specified models, which makes the gap between a model and its empirical validation especially visible. A well-built simulation fed by biased data produces precise, confident, wrong answers.

For a Cornell project, the data pipeline deserves the same systems discipline as the model. Treating data sourcing as an engineering subsystem, with documented inputs and validation, fits the program's culture.

Data Requirements for Model Validation

Validating a stochastic model against history requires that the history be real as it was known. Point-in-time correctness prevents the validation from cheating, a point made in our guide to point-in-time market data, and survivorship-free coverage ensures the model is tested against the full population, not just the winners, as our piece on survivorship bias explains.

The operations-research instinct to question assumptions applies directly here: the most important assumption in an empirical test is usually hidden in how the data was assembled.

Datasets That Fit a Cornell Project

Building the size series correctly is the usual snag, covered in our note on historical market cap data.

A Project That Fits the OR Tradition

A Cornell-style project might simulate an event-driven strategy under realistic borrow and execution assumptions, validating the model against a clean historical event set. The mechanisms and the realistic frictions to model are in Systematic Event-Driven Trading.

Alphanume's historical market cap dataset provides point-in-time size, and the dilution events feed supplies the dated events your simulation reacts to, giving the validation step inputs as carefully specified as the model itself.

Validating the Simulation

An operations-research project often centers on a simulation, and the validation step is where data quality decides the outcome. You would specify the stochastic model, then test it against a clean historical event set under realistic borrow and execution assumptions, checking that the simulated behavior matches what actually happened. If the historical inputs carry lookahead, the validation passes for the wrong reason and the model looks better than it is.

Treating the historical data as a carefully specified input to the simulation, rather than a convenient download, is the engineering instinct the program cultivates. The most important assumption in the whole exercise is usually buried in how the validation data was assembled.

Documenting the Assumptions

An operations-research project is judged partly on the clarity of its assumptions, and the data assumptions deserve the same explicit treatment as the model ones. Document where each input comes from, how survivorship is handled, and what point-in-time means for each variable. A reviewer from that tradition reads the assumptions first, and a clean account of the data is what makes the rest of the work credible.

This documentation is also what makes the simulation reproducible. A model whose inputs are precisely specified can be rerun and extended, while one fed by an undocumented data pull cannot, and reproducibility is a value the operations-research culture takes seriously.

How to Choose

Engineer the data like a subsystem. For a Cornell FE project, use point-in-time, survivorship-free, documented sources so the model is validated against reality rather than against bias. The operations-research mindset that questions every assumption should be aimed first at where the data came from.