Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.
4,194 documents
This notebook explains how a conditional autoencoder extends instrumented PCA for ETF return modeling. Both methods map fund features to factor exposures and combine those exposures with factor returns; the autoencoder replaces IPCA’s linear…
This US equities panel study uses a penalized linear model as a baseline for forecasting forward returns from features observed at the close. Its feature set contains overlapping momentum and volatility measurements, creating multicollinearity that can make…
This notebook explains instrumented principal component analysis (IPCA), which estimates common factors while making each stock’s factor exposures a shared linear function of its same-day option-surface features. Those features include implied volatility,…
This notebook tests whether four managed-portfolio strategies explain SPY returns after controlling for broad ETF co-movement. The strategies rank ETFs using lagged momentum, volatility, or recent returns. Ten principal components from a balanced set of…
This document introduces the Chen, Pelger, and Zhu monthly US equity panel used to benchmark asset-pricing models. It describes 46 firm characteristics grouped by economic theme, next-month excess returns, and the panel’s time-ordered train, validation, and…
This analysis reads a registered backtest pipeline for a monthly US equity strategy based on firm characteristics. It reports Sharpe uncertainty, probability and deflated Sharpe measures, paired strategy comparisons, holdout decay, and a cost analysis…
This notebook explains risk-premium PCA, which modifies the return covariance matrix by adding a weighted outer product of the training-period mean returns. Its leading eigenvectors define static factor portfolios; raising the weight tilts the factors toward…
This notebook presents instrumented principal component analysis (IPCA), a latent-factor approach to forecasting returns from firm characteristics. Instead of assigning each characteristic a direct return weight, IPCA models each firm’s factor exposures as a…
This utility describes a method for explaining strategy returns with Fama–French three-factor or five-factor models, optionally including momentum. It aligns daily factor data with strategy returns, detects the return frequency, and compounds factor returns…
This notebook evaluates published factor returns as potential portfolio building blocks. It separates three questions: whether a factor’s average return clears a demanding statistical bar, whether factors diversify one another for reasons that may persist,…
This notebook assesses whether a monthly long–short strategy based on firm characteristics can be studied with a released US firm panel. It checks the data encoding, how the available universe changes over time, whether each rebalance has enough firms to…
The document introduces latent factors as shared return drivers inferred from co-movement across futures rather than supplied as named predictors. It compares two approaches fitted within each training fold. Principal component analysis (PCA) finds linear…
This notebook fits ridge, lasso, and elastic net models to published firm characteristics for individual US stocks. The features span value, profitability, investment, size, momentum, reversal, risk, and liquidity. Walk-forward folds keep configurations…
This document introduces public Fama-French and AQR factor return datasets as inputs for benchmarking, risk attribution, and factor research. It describes market, size, value, profitability, investment, and momentum factors from the Fama-French library,…
This notebook clusters monthly returns from nine equity, bond, currency, and commodity factor and market series using Gaussian mixture models. After dropping months with incomplete data, it standardizes the series so differences in scale do not dominate the…
This notebook evaluates 57 accounting and price-based characteristics as individual predictors of next-month US stock returns. It calculates monthly cross-sectional rank correlations, averages them over time, and adjusts uncertainty for serial dependence and…
This chapter presents a framework for turning a trading idea into a documented feature specification. It asks researchers to align feature horizons with the intended decision, state a driver hypothesis, and distinguish predictive signals from contextual…
This notebook aggregates linear-model results, including OLS, Ridge, Lasso, and ElasticNet, across nine case studies. It compares each study’s strongest complete configuration on its primary label, then examines regularization, fold-level information…
This chapter presents a framework for evaluating causal claims in quantitative trading research. It starts with clear definitions of treatment, outcome, estimand, counterfactual, and adjustment set, then matches research questions to methods: Double Machine…
This notebook uses principal component analysis on a liquid subset of US stocks to build gross-normalized eigenportfolios and study their role as latent risk factors. It examines explained variance, interprets components through sector ETF correlations,…
This configuration describes monthly long-short US equity portfolios ranked on firm characteristics. It specifies a complete-case universe with 46 characteristics, month-end decisions, next-open execution, equal weighting within the long and short legs, and…
This notebook introduces a stochastic discount factor model for pricing a cross-section of ETFs. Instead of separately estimating fund exposures and factor returns, it trains networks to find a common discount-factor series whose covariation with fund…
This notebook screens financial and model-derived features for their ability to rank ETFs by subsequent returns. It calculates a date-by-date information coefficient by comparing each feature’s cross-sectional ranks with forward-return ranks, then examines…
This data utility supports work with the Chen, Pelger, and Zhu asset-pricing dataset, which includes stock returns, firm characteristics, macroeconomic series, and pre-split characteristic tensors. Its conversion method builds Parquet files from the…