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 report introduction frames research into how factor and stock weights affect style exposure in multi-factor equity portfolios. It focuses on reducing unintended market-cap, industry, and other style biases, which the authors associate with past setbacks…
This strategy uses language metrics from company filings to form a monthly long-short equity portfolio. It selects a stock universe using liquidity and market-cap filters, retrieves filing metrics, and ranks eligible companies separately on lexical density…
This example describes a high-dividend stock selection model for the Chinese equity market. It first excludes special-treatment stocks, suspended stocks, and Beijing Stock Exchange listings. Within the remaining universe, it selects the 300 largest companies…
The document presents the Brinson framework for explaining portfolio returns through the benchmark, active asset allocation or timing, security selection, and their interaction. The benchmark reflects long-term asset-class weights and passive returns. Timing…
This A-share screening approach combines trading activity, recent price movement, and a sustained profitability condition. It selects stocks with turnover between 3% and 12%, a positive 10-day gain below 35%, and return on equity above 15% in each of the…
This report presents a fundamental quantitative stock-selection framework for China’s lithium battery industry. It first reconstructs the supply chain into battery makers, material suppliers, and upstream cobalt and lithium resources, arguing that…
This report summary presents a framework for converting intraday or other medium-to-high-frequency signals into factors intended to predict returns over a monthly horizon. It separates factor construction into signal generation, aggregation from intraday…
The document describes a strategy that ranks Chinese A-share market concepts by their recent equal-weighted stock returns, then buys stocks in winning concepts and shorts those in losing concepts. Portfolios are rebalanced monthly, with formation and holding…
This report summary describes a Chinese equity industry-rotation approach that scores sector conditions using financial data from regular reports, earnings forecasts, and earnings bulletins. It evaluates individual accounting measures, then combines…
This Chinese-language post outlines an equity screening rule that ranks stocks by capital strength, keeps the top 100, looks for simultaneous bullish crossovers among three technical indicators, and then restricts candidates to those with a positive 10-day…
This BigQuant example builds a long-only Chinese stock portfolio from a filtered universe of main-board shares. It excludes suspended stocks and, in its feature filters, keeps non-ST stocks listed for more than a year, with positive trailing earnings and…
This discussion explains how a quantitative platform interprets factor weights and describes its StockRanker as a learning-to-rank model based on a gradient-boosted decision tree structure. The response says the model does not require independent factors,…
This research compares ways to construct and transform analyst-consensus forecast factors for stock selection and industry rotation. It examines expected return on equity, net profit, net-profit growth, and a two-year compound-growth measure. The approaches…
This BigQuant forum exchange addresses two questions about accessing A-share financial statement data. The first asks why requesting a prior-year return-on-equity field by adding a suffix to the current field name causes an error. The reply explains that the…
The document proposes a stock screen combining daily price amplitude above a threshold, a market capitalization ceiling, positive net profit, and at least five converging moving averages. It argues that clustered averages may point to a relatively stable…
This research summary tests whether machine-generated stock-selection factors add value to established manually designed factor libraries. It uses genetic programming to evolve candidate formulas from market data, then applies random forests to turn factor…
The document proposes an A-share stock selection screen that combines daily price range, a specified closing price, revenue growth over several years, and additional filters for profit growth, market capitalization, and trading volume. It presents the…
This research summary proposes that trading activity can reveal information about informed traders. It reports a positive relationship between turnover and next-day share-price volatility: unusually active trading may precede either a sharp rise or a sharp…
This post describes a Chinese equity screen that selects stocks with turnover between 3% and 12%, codes beginning with 60, and a metaverse association. It presents both a database-style query and Python-oriented pseudocode for listing candidate securities,…
This Chinese equity-selection note combines three filters: daily amplitude above 1%, tradable shares no greater than 5.5 billion, and return on equity above 15% across a five-year period. It frames the filters as a way to find volatile stocks with relatively…
This research summary argues that low valuation alone can select weak businesses, so investors should assess company quality before deciding whether its price is reasonable. It evaluates A-share companies using indicators related to profitability, growth,…
This research summary compares five classifiers for predicting stock returns from historical equity factors: multinomial logistic regression, linear support vector machines, random forests, XGBoost, and deep neural networks. It distinguishes the linear…
This A-share stock screen combines a positive MACD reading with a favorable company-type filter and a historical dividend payout ratio above 25% for 2019. The stated rationale is to pair a technical condition with business and dividend characteristics,…
This Chinese A-share screening rule starts with price amplitude above 1 and a current control indicator above 21, while excluding Beijing A-shares. Its final proposed version adds return on equity above 15%, year-over-year revenue growth above 20%, and a…