Acting in the dark
“Most generally, noise makes it very difficult to test either practical or academic theories about the way that financial or economic markets work. We are forced to act largely in the dark.”
Fischer Black, American Finance Association Presidential Address (Black 1986, 529)
Black’s line is a useful place to begin. Finance gives us abundant observations without giving us direct sight of value or expected return. Prices carry information, but they also reflect people trading on noise as if it were information. The task is not to promise certainty. It is to make defensible decisions while stating what the evidence cannot settle.
This site treats data science as statistical science: the study of variation and uncertainty in financial data. Computation lets us work at scale; statistical reasoning tells us when the result deserves belief.
What this course does
We use Python and, where available, Bloomberg Terminal data to examine returns, risk, lending, fraud, factor models, and backtests. Each application raises the same practical difficulty: a model can fit the available data and still fail when the population changes, the comparison is misleading, or the variable measures the wrong thing.
The course therefore puts judgement before machinery. You will estimate models and write code, but you will also inspect how the data were produced, test performance beyond the sample used to build the model, and explain the limits of the conclusion. A complicated method earns its place only when it improves the decision.
Start here
- Chapters develop the ideas and evidence.
- Labs turn those ideas into reproducible analysis.
- Slides support the taught sessions.
- Python setup prepares the working environment.
- Reading list collects the principal papers and texts.
Module schedules, assessment briefs, submission links, and deadlines remain on Blackboard.
Contact
Professor Barry Quinn
Professor of Finance and Financial Technology
Director, Centre for Finance and Responsible Technology
b.quinn1@ulster.ac.uk · Room BC-08-205B · Office hours by appointment
Ulster University Business School | BSc Finance and Investment Management/MSc FinTech Management