Factor research

Factor Research

A factor is a claim that an ordering of assets today predicts their returns tomorrow. Rankings are cheap — every column of numbers orders a universe. This skill measures whether the ordering carries information, how fast it decays, and whether it survives its own turnover.

Position in the pipeline

point-in-time-research guards the data that builds the factor panel. This skill judges the panel. Survivors go to backtest-validation, where costs and selection bias get their turn. A factor evaluated on contaminated data has a fictional IC — run PIT discipline first if the panel provenance is unclear.

What gets measured (one way, no options)

1. Rank IC series — per-period cross-sectional Spearman of factor(t) vs next-period returns. Rank, not Pearson: factors are orderings, and Pearson IC is one outlier away from flattery. Mean IC, IC-IR, t-stat, hit rate.
2. Decay — mean IC at 1/5/10/21-period horizons. Fast decay + high turnover = the edge pays the broker.
3. Quantile discipline — mean forward return per quintile and the share of ordered adjacent steps. A real factor orders the middle of the book, not just the two extreme buckets.
4. Stability — first-half vs second-half IC (a sign flip is fatal) and factor rank autocorrelation (turnover proxy).

Workflow

1. Establish the panel: factor values as-of each date (long format date,symbol,value), the return matrix, the frequency, and where the factor values came from. If provenance is unclear, route through point-in-time-research before trusting any IC.
2. Run python scripts/factor_evaluate.py --factor factor.csv --returns returns.csv --freq daily --json report.json (or --demo to show the mechanics).
3. Report judgement first, then the evidence: IC/IR/t/hit-rate, decay curve, quantile spread, turnover. Interpretation thresholds live in references/methodology.md.
4. Route by verdict: valid / valid_but_moderate → hand to backtest-validation (the factor is a hypothesis, not yet a strategy); weak / invalid → the deliverable is the rejection and which check failed. Do not "fix" a dead factor by trying variants until one passes — that is selection bias, and backtest-validation's DSR will ask how many variants were tried.
5. When comparing multiple factors, evaluate each on the same universe and window, and report the count of factors examined alongside the winner.

Guardrails

• IC below noise threshold is reported as "no signal", never rounded up to "slightly positive".
• No strategy construction on a weak/invalid verdict.
• Multiple factors tried = trials disclosed downstream to backtest-validation.
• Decay and turnover are always reported together — a horizon-1 edge with churny ranks is flagged, not celebrated.