Knowledge library

This notebook uses correlation-based principal component analysis on sector ETF returns to describe common risk factors and sector rotation. Standardizing returns gives each sector unit variance before decomposition, reducing the chance that a more volatile…

This notebook explains a principal-component baseline for forecasting CME futures returns. PCA is fitted to the training fold’s product-return panel, so its components represent co-movement directions across contracts rather than compressed carry, momentum,…

This notebook describes a supervised autoencoder that compresses ETF feature rows into a low-dimensional representation and predicts forward returns directly from that representation. Unlike a conditional autoencoder, it does not assume that returns arise…

This notebook fits ridge, lasso and elastic-net models to a broad set of established US stock characteristics, including value, profitability, investment, momentum, size, risk and liquidity measures. It evaluates raw monthly returns, a cross-sectionally…

This analysis applies correlation-based principal component analysis to returns from sector exchange-traded funds. Standardizing returns before decomposition gives each sector unit variance, so the leading components describe co-movement rather than being…

This document presents a supervised autoencoder as a control for conditional latent-factor models. It maps firm characteristics directly to forward returns while compressing 57 inputs through a narrow bottleneck. Comparing it with a conditional autoencoder…

This guide describes public factor return datasets from the Ken French Data Library and AQR, including market, size, value, profitability, investment, momentum, quality, and low-beta factors. It explains how to download and load monthly or daily…

This study tests whether the poor fit of a linear model to monthly US stock returns comes from its squared-error objective. It holds the unwinsorized forward-return target, features, and walk-forward folds fixed while comparing boosted tree models trained…

This document demonstrates Instrumented PCA, in which asset factor loadings are modeled as linear functions of characteristics observed before the return. It uses alternating least squares to estimate the characteristic-to-loading map and realized factors,…

This document describes a reproducible US equities benchmark drawn from the Chen, Pelger, and Zhu asset-pricing replication archive. It contains roughly 1.2 million monthly stock observations from 1967 through 2016, with 46 firm characteristics,…

This notebook adapts an adversarial stochastic discount factor design to daily U.S. equities. A portfolio-weight network forms a factor from next-day excess returns, while a competing moment network helps train the pricing kernel; a separate beta network…

This notebook compares a Random Forest with XGBoost, LightGBM, and CatBoost on a panel of anonymized firm characteristics and returns. The dataset supplies temporal training, validation, and test splits. Models are evaluated using Spearman rank information…

This notebook presents a conditional autoencoder that preserves the structure of an instrumented latent factor model while replacing the linear map from firm characteristics to factor exposures with a neural network. Monthly returns are represented as…

This notebook builds a monthly model matrix for a US firm-characteristics study from a released panel whose characteristics have already been ranked across firms. It adds composites and interactions using values in the same firm-month row, without fitting…

This notebook examines linear-model regularization for predicting returns across currency pairs. It distinguishes collinearity among input features from dependence among the assets: currency pairs share underlying currencies, so their returns and rankings…

This notebook studies a supervised autoencoder for predicting forward returns from firm characteristics. It retains a narrow neural-network bottleneck but removes the conditional-factor interpretation and pricing restriction used by related models. The…

This feasibility analysis checks whether a monthly long-short strategy based on US firm characteristics can be evaluated with the available panel. It verifies that the measures are encoded as ranks, describes how long firms remain observable, counts firms at…

This notebook presents a pipeline for turning financial headlines into stock-level signals. It cleans and standardizes a news corpus, removes exact and prefix-matched duplicates within ticker-date groups, and uses sentence embeddings to represent headline…

This notebook applies previously selected firm-characteristic predictions and a fixed portfolio configuration to a one-year holdout period. It makes no new choices about predictions, allocation, concentration, rebalancing, or transaction costs, preserving…

This notebook explains instrumented principal component analysis (IPCA) for a changing US equities panel. Traditional PCA assigns each stock a fitted loading, which cannot naturally cover names absent from the training sample and stays fixed as a company's…

This notebook synthesizes registered validation results for five latent-factor estimators—PCA, IPCA, CAE, SDF, and SAE—across nine case studies. It first reports which estimators have results in each panel, then identifies the highest mean daily Spearman…

This notebook applies principal component analysis to a panel of US stock returns to represent broad shared movements with a smaller set of factors. Each factor is a direction of common variation, and each stock’s loading describes its exposure to that…

The document describes a walk-forward study of TabM neural networks predicting monthly returns from firm characteristics. Because the shared training runner fixes the objective to squared error for regression and cross-entropy for classification, the…

This notebook presents a family-level view of five latent-factor approaches applied to an equity option analytics case study. PCA estimates common movements directly from the return panel; IPCA maps characteristics to exposures linearly; a conditional…