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 research tests whether machine learning can estimate and reduce the optimism bias in analyst earnings forecasts. It uses 27 variables drawn from research reports, analyst characteristics, company fundamentals, and market information. LASSO provides a…
The report estimates stock-specific volatility from residuals in CAPM, Fama-French three- and five-factor, and Carhart four-factor models, then studies its role in China’s A-share market. It describes a low-volatility portfolio that equally weights stocks in…
The post proposes a simple equity-selection strategy based on revenue relative to market capitalization. It first requires positive year-over-year growth in the latest reported total revenue. Eligible stocks are then ranked by the previous year’s total…
The document proposes a stock screen combining three conditions: price amplitude above a threshold, progressively higher price lows, and circulating market capitalization above a stated floor. Its rationale is to find stocks with enough movement to avoid…
This research note examines Chinese property developers through their business mix, financial statements, operating efficiency, leverage, and return on equity. It groups developers by scale and argues that inventory turnover and debt repayment capacity…
This study examines the shift from reversal toward momentum in Chinese A-shares, especially among stocks favored by institutions. It compares several ways to measure price strength: conventional lookback returns, returns excluding limit-up days, daily-rank…
This report summary distinguishes active quantitative mutual funds from index enhancement and quantitative hedging products. Active quant funds generally face less stringent tracking error limits than index enhancement funds and typically use long-only…
This guide explains how to configure a genetic programming module to search for trading factors from supplied market data and input features. Users choose a market and data frequency, return field and horizon, time splits, fitness measure and thresholds,…
The document describes a Chinese equity screening rule that selects stocks associated with the metaverse theme, trading above a 250-day moving average, and priced below a stated threshold. The accompanying discussion frames the long-term average as a trend…
This research summary describes a short-horizon A-share selection factor built from intraday data. It calculates realized volatility, realized skewness, and realized kurtosis, then uses a regression model and the residual standard deviation from each period…
This Chinese equity screen combines a 14-period RSI below 65 with year-over-year growth in net profit attributable to shareholders of the parent company above 20% and no more than 100%. It also excludes STAR Market stocks. The article presents the…
This FAQ explains three data-processing tasks in BigQuant’s DAI SQL environment. It outlines how to define rolling aggregate macros and date-partitioned cross-sectional ranking or averaging macros, including a workaround for choosing sort direction when SQL…
This Chinese A-share screen ranks stocks by today’s auction amount, retains the top five, requires amplitude above one, and looks for overlap among five moving-average periods: 5, 10, 20, 30, and 60 sessions. The article’s final version adds a…
This example documents a manual, language-model-assisted factor-building workflow for equities over 2022–2024. It combines a stock’s price relative to its 20-day average with cross-sectional standardized price-to-earnings data, scales the composite by its…
This post presents an A-share stock screen focused on companies described as connected to the metaverse. Its stated selection logic uses a turnover-rate range of 3% to 12% and excludes Beijing-listed A-shares. The discussion recommends examining companies'…
This document describes a China A-share screening strategy that excludes Beijing-listed and certain other regional stocks, requires daily price amplitude above 1%, and selects companies with positive net profit and year-on-year net profit growth from above…
This report summary describes an industry-rotation strategy for Chinese A-shares based on medium-term momentum, measured by returns over the previous 250 days. It adds two crowding controls to address momentum reversals: a sector-level concentration measure…
The document proposes a Chinese equity screen that selects companies classified in the metaverse industry, with a volume ratio above 1.5 and below 6, and a dividend payout ratio above 25% for 2019. It frames dividend payout as a valuation consideration and…
This document describes a portfolio analysis workflow that attributes a return series to selected risk factors. It combines daily returns, holdings, factor returns, and security-level factor loadings, converting dollar positions to portfolio weights and…
The document proposes a daily equity factor that combines three market measures: the gap between a 10-day average of amount per volume and the latest close, current volume relative to its 20-day average, and the latest close relative to its 20-day average.…
The document outlines an equity screen combining three criteria: membership in the metaverse theme, a prior-day price above its 250-day moving average, and circulating market value between 5 billion and 10 billion yuan. The long-term average is used as a…
This document presents an equity screen combining three conditions: MACD above zero, short-term moving averages arranged to indicate an upward trend, and return on equity above 15% for five consecutive years. It interprets the MACD and moving-average…
The document describes a tool for evaluating many equity factors in batches and saving overall and yearly long-side results. Users configure the factor source and names, stock universe, benchmark, optional smoothing, and output settings. For high-frequency…
This post describes a China A-share stock screen that combines four filters: association with the metaverse, positive institutional activity, a robotics concept label, and circulating market capitalization below the stated threshold. It frames the screen as…