pflab_panel_describe | Coverage report of a characteristics panel: per field its type, share missing overall and inside the universe, assets with no data, categorical levels and their sizes, optionally broken down by a text field; with optional forward/backward filling of gaps (e.g. quarterly fundamentals) and shrinking the universe to rows with data. | |
pflab_synthetic_panel | A simulated characteristics panel (returns from a factor structure, market cap, prices and volumes, fundamentals, analyst estimates, short interest, industries, listings and delistings, missing values) in the long layout the factor-lab tools take. | |
pflab_descriptors | Computes descriptors (raw characteristics) for every date and asset of a panel: value ratios, momentum and reversal, volatility, beta and residual risk, size, liquidity, short interest, profitability, growth, leverage and yield, or growth/change/passthrough of any field. | |
pflab_cs_transform | Transforms a field cross-sectionally at every date: winsorizing at quantiles, tanh outlier shrinkage, weighted z-scoring, Gaussian-rank or percentile-rank scaling, optionally within groups (industry-neutral) and weighted (e.g. by market cap). | |
pflab_factor_exposures | Factor exposures at every date: the market (global) factor, one-hot categories (industry, country), and style factors as weighted composites of descriptors, winsorized and z-scored cross-sectionally (optionally within groups). | |
pflab_alpha_model | Alpha research on a characteristics panel: a factor risk model isolates idiosyncratic returns, each alpha model forecasts them from descriptors (fixed weights, exponentially weighted Sharpe-optimal weights, or a ridge/forest/boosting predictor), and the forecasts are evaluated out of sample: information coefficients and their t-statistics, rank- and z-weighted long-short portfolios, quantile spreads, decay over horizons, calibration, coverage and factor correlations, compared across alphas (predictor alphas keep no forecast history and are not evaluated). | |
pflab_characteristics_factor_model | Barra-style characteristics factor model: exposures from descriptors and categories at every date, factor returns by cross-sectional regression of returns on lagged exposures (cap-weighted, robust or GLS), factor covariance and premia, idiosyncratic risk, and asset expected returns and covariance. | |
pflab_cs_regression | Cross-sectional regression at every date of a panel field on other fields (lagged by default), weighted by a field or equally, by least squares, Huber, ridge or lasso: the Fama-MacBeth estimate of each regressor’s return. | |
