factor-researchlisted

# 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 value