Topic
Quantitative research
Point-in-time data, back-tests and systematic signals.
- Same-store cohorts: how the weekly market series stay comparableCoverage of a company index expands continuously, and a raw count would carry that expansion into every trend. Here is the cohort rule that keeps it out, and the arithmetic of every measure it feeds.
- Why we write each table once: point-in-time data by designA table corrected in place shows what is known today, not what was known then. The five rules that keep Fokals tables as first written, what they guarantee and how to check them.
- Building a point-in-time company dataset for back-testsA back-test is only as honest as the dates in its data. This guide shows how a dated, write-once record lets you key company signals by when they were known and query as of a date.
- Building thematic baskets from technology adoption dataA basket of adopters is only as good as its membership rule. Here is how to build one from website technology data, rebalance on dated events and test it.
- Mapping alternative data to a security masterJoining company data to securities is a many-to-many problem. This guide covers the identifiers every listed company carries, the join order, parents and brands, delistings and check digits.
- Training and evaluating models on dated company signalsA model on company signals is only as honest as its feature table. This guide builds one as of each date, splits by time and names the properties of the record that keep it safe.
- Look-ahead biasA back-test that uses information from after the date it simulates looks better than any real decision could have been. How it enters a test and how to find it.
- Point-in-time dataData stored as it stood on each past date, with no later corrections. How the two dates of a record work, an as-of query and the mistakes that break a back-test.
- Same-store cohortCompare only the companies present for the whole window, so wider coverage does not look like market growth. The method, a worked example and where it is used.
- Survivorship biasStudying only the companies that lasted makes past results look better than they were. Where the error appears, how to test for it and how to keep delisted names.