Knowledge library

This notebook presents instrumented principal component analysis as a model of cross-sectional returns. Instead of assigning each firm’s characteristics direct return weights, IPCA estimates a shared linear map from characteristics to factor exposures,…

This algorithm example demonstrates a two-stage method for narrowing a stock universe. Its coarse filter ranks available stocks by daily dollar volume and passes the five highest-volume names onward. A fine filter then ranks those candidates by…

The document defines price-to-book as closing price divided by book value per share, with book value per share calculated from total assets less total liabilities, divided by common shares outstanding. It implements this measure as a pipeline factor using…

This example builds a daily stock universe from currently tradable Shanghai and Shenzhen listings, then intersects it with the CSI 300 constituents. It ranks the remaining stocks by market value and keeps the five smallest. The selection is a small…

This document describes an AI-assisted workflow that turns natural-language crypto factor ideas into computed signals and validation reports. A language model interprets the idea, generates a factor function, and assigns signal direction; the system then…

This US equity strategy builds an equal-weighted portfolio through successive screens. It starts with liquid NYSE stocks while excluding financial companies, ADRs, and REITs; selects firms with low enterprise-value-to-EBIT ratios; then favors stronger…

This US equity strategy builds a machine learning feature set from fundamentals, quality measures, price and volume behavior, technical indicators, securities data, and market signals. Its target is the following week's return. Fundamental inputs are…

This notebook explains instrumented principal component analysis as a way to make latent-factor loadings depend on observable stock characteristics rather than assigning each stock a fixed loading. A shared mapping from characteristics to factor exposures…

This document introduces a family of firm-return models that assumes a small set of unobserved common factors explains much of the cross-section. Rather than predicting returns directly from characteristics, these models infer each firm's factor exposures…

The notebook introduces a supervised autoencoder for equity factor modeling. Unlike unsupervised autoencoders that learn a bottleneck by reconstructing the cross-section, this model also includes forward returns in its training objective. The target…

This notebook applies a neural stochastic discount-factor model to CME futures. In asset-pricing terms, a stochastic discount factor is a random variable whose product with each asset's return has the same expected value under no arbitrage. The model…

This notebook presents a cross-case-study inventory of engineered financial features across nine asset classes. It counts features and name-based families from each available feature panel, compares feature-space sizes, and summarizes the strongest…

This case study evaluates monthly long-short decile strategies built from US firm characteristics, comparing linear models, gradient boosting, and latent-factor approaches under point-in-time accounting lags, walk-forward validation, and era-dependent…

This module coordinates walk-forward fitting and evaluation for several latent factor models, including principal components, instrumented principal components, and neural autoencoders. It prepares panel data and folds, invokes model-specific runners, and…

This implementation describes a conditional autoencoder for asset pricing. A beta network maps each stock’s characteristics to latent factor loadings, while a linear factor network maps managed portfolio returns to factor returns. The model predicts a stock…

This notebook explains how to build and inspect a quarterly equity fundamentals panel from SEC XBRL data. Each observation carries both the fiscal period it describes and the filing date when the information became public. That distinction is essential in…

This notebook evaluates accounting and price-based characteristics as standalone predictors of next-month US stock returns. It computes monthly cross-sectional rank correlations between each characteristic and the return label, then summarizes them across…

This notebook explains how a conditional autoencoder extends an instrumented principal component model: a neural network maps stock characteristics to factor exposures, while the model retains the same exposure-times-factor-return structure. It fits the…

The document explains instrumented principal component analysis for modeling ETF returns. Unlike ordinary PCA, which assigns each fund fixed factor exposures, IPCA makes each fund’s exposures a shared linear function of its observable features. It estimates…

This notebook explains instrumented principal component analysis (IPCA) for an equity panel with option-surface features. Unlike ordinary PCA, which estimates each stock's exposures from its return history, IPCA models exposures as a shared linear function…

This notebook uses principal component analysis on a liquid subset of US equities to extract latent factors and form long-short eigenportfolios. It explains how standardized PCA vectors are converted to raw-return weights and normalized by gross exposure,…

This notebook presents principal component analysis as an unconditioned latent-factor baseline for forecasting stock returns in an option analytics study. PCA uses the historical return panel to estimate common movements and each stock’s exposure to them,…

This notebook evaluates a cross-sectional momentum factor using information coefficients, quantile returns, top-to-bottom spreads, and classification diagnostics. It introduces Spearman IC as the date-by-date rank correlation between a signal and subsequent…

This notebook uses principal component analysis to represent the common movements in a broad US equities panel. PCA extracts directions that explain shared variation in returns; each stock’s loading measures its exposure to each direction. A prediction…