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 compares predictive, structural, and causal model evidence for a cross-sectional S&P 500 study combining equity features with options-derived measures such as implied volatility, skew, and term structure. It evaluates weekly forward-return…
This notebook explains PCA as a feature-free latent-factor baseline for forecasting ETF returns. It decomposes each label’s training-window return matrix, using a loading for each fund and estimated factor premia to assemble forecasts. Because the same fund…
This notebook presents a neural stochastic discount factor model for a panel of CME futures. The model uses both product returns and observable characteristics, such as carry, momentum, and volatility, to construct latent factors and generate predictions for…
This experiment examines how much a selected monthly US equity long-short signal depends on small-cap stocks. It applies a lagged market-equity filter that removes the smallest quartile from validation predictions, then reruns the same portfolio construction…
This notebook explains a conditional autoencoder that models stock returns as characteristic-dependent factor loadings multiplied by latent factors. It first estimates characteristic-managed portfolio returns with a joint cross-sectional least-squares fit,…
This analysis compares feature-triage ledgers across nine trading case studies. It distinguishes Benjamini–Hochberg false-discovery-rate significance from a second route to a PROCEED decision, which combines a case-specific effect-size floor with fold sign…
This notebook builds principal-component factors from a panel of CME futures returns, not from engineered characteristics such as carry, momentum, or volatility. PCA finds directions that explain variation across products; the first may resemble a common…
This notebook describes slow-moving contextual features for daily trading decisions: accounting ratios, macroeconomic conditions, and calendar encodings. It outlines value and quality measures such as earnings yield, profitability, accruals, leverage, and…
This document describes ways to turn dated entity records into arrays for latent-factor case studies. One method keeps only entities that meet a coverage threshold learned from an eligibility dataset, producing a consistent entity axis with missing…
This notebook examines a keyword-based ESG headline pipeline and contrasts its structured classifier output with the requirements a retrieval-augmented generation assistant would need to meet. A first keyword list selects headlines, and a second assigns…
This notebook explains how searching across many factors or strategies inflates the apparent performance of the selected winner, even when individual estimates are computed correctly. A simulated set of noise factors illustrates that the largest information…
Risk-Premium PCA modifies the covariance matrix by adding a weighted outer product of the training mean-return vector. Its leading eigenvectors define static factor portfolios: ordinary PCA is recovered when the weight is zero, while positive weights tilt…
This notebook uses a Gaussian mixture model to group months by the joint behavior of nine factor and asset-class return series. It standardizes monthly observations, drops months with missing values, and compares candidate cluster counts using BIC, AIC, and…
This feature-engineering notebook explains how slower information can condition faster trading signals through fundamentals, macroeconomic data, and calendar context. It outlines value ratios such as book-to-market and earnings yield, quality measures such…
This notebook synthesizes registered validation results for five latent-factor estimators across a set of case studies. It first maps which estimators have results for each panel, then identifies the leading estimator by mean daily Spearman rank information…
This chapter presents retrieval-augmented generation as a way to make language models more useful for open-ended financial research, where unsupported claims and hallucinations can be costly. It walks through the system from document ingestion and…
The paper proposes a single-directional Transformer model, SERT, for pricing large-cap US stocks and applies pre-trained Transformers to stock pricing and factor investment. It compares these approaches with standard and encoder-only Transformers across…
The document describes an asset-pricing anomaly associated with employee remuneration: firms that spend more on salaries and benefits per employee are reported to have stronger average stock performance. It also proposes that firms with similar pay policies…
This study examines whether price momentum performance in Korean equities changes when the eligible universe is narrowed to subgroups of the KOSPI 200. It compares momentum returns across submarkets and checks the pattern against portfolios sorted by company…
The study separates factor discovery from factor approval. An agent may propose candidates and create diagnostic probes, while a frozen statistical referee evaluates each proposal using market outcomes observed only after submission. The referee uses…
The paper compares portfolios optimized to minimize shortfall with portfolios optimized to minimize variance. The empirical study uses Barra Extreme Risk and style factors such as Value, Growth, and Momentum across US, UK, and Japanese equity markets. It…
The paper studies which characteristics of published stock anomalies predict a decline in risk-adjusted performance outside the original sample. It tests explanations associated with arbitrage capital entering a strategy after publication and with in-sample…
EverMine is a framework for testing whether research agents improve as they accumulate reusable skills, tools, and rules during long-horizon alpha discovery. It represents research state through the research history, current factor portfolio, and accumulated…
The paper combines statistically constructed factor models with stochastic control to study high-dimensional statistical arbitrage. Its model assumes mean-reverting residuals and shows how to build analytically market-neutral portfolios. It then derives…