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 Chinese equity screening idea combines reported net profit growth, an estimate of current buying interest, and a history of limit-up moves. Its stated criteria require buying activity above 5%, year-over-year net profit growth above 20% and no more than…
The document describes a stock screen requiring price amplitude above 1%, turnover greater than 2% and below 9%, and a 2019 dividend payout ratio above 25%. It presents amplitude and turnover as measures of market activity and uses the historical payout…
This market review describes a year of changing styles in Chinese equities: cyclical and growth themes led during the recovery, followed by a shift toward value later in the year. It argues that a high earnings base and flattening growth expectations could…
This industry-selection approach adapts William O’Neil’s CAN SLIM framework by calculating relative strength at the sector level. The report proposes a high relative-strength reading as an early screening signal for industries that may lead the market during…
This study develops an equity factor for company competitiveness across four dimensions: technology, products, internal controls, and capacity to sustain and improve existing advantages. It combines measurable indicators from these areas, then adjusts the…
This equity-factor study compares several ways to measure illiquidity and examines whether less liquid stocks earn higher subsequent returns. Its microstructure measures are quoted spread, effective spread, and price-impact lambda; it also tests Amihud’s…
This A-share screening recipe combines three conditions: daily price amplitude above 1%, a nonempty name for an outstanding convertible bond, and return on equity above 15% for five consecutive years. It is intended to pair a price-movement filter with a…
This report examines whether price momentum signals behave differently according to which types of traders account for trading volume. It divides A-share participants into retail, medium, large, and institutional groups based on order size, then compares…
This post proposes screening Chinese equities using a historical dividend measure alongside price activity: a stated 2019 dividend ratio above 25%, intraday amplitude above 1, and a 9:25 price rise below 6%. It frames the combination as a way to find active…
The document describes a Chinese equity screen that combines at least five overlapping moving averages, a circulating market value above 200 million, and revenue in 2021 exceeding 1.1 times revenue in 2018. The moving-average condition is presented as a way…
This post proposes a Chinese equity screening rule that combines daily price activity, the stock’s indicated gain at 9:25, and year-over-year net profit growth attributable to shareholders of the parent company. It selects stocks with an amplitude above 1,…
This research summary examines the low idiosyncratic volatility effect in China’s A-share market. It estimates volatility from the annualized standard deviation of residuals in CAPM, Fama–French three-factor, and Carhart four-factor regressions fitted to the…
The discussion explains how to repeatedly reduce a model’s factor list using feature importance. The example starts with 15 factors, removes the three with the lowest importance after a training and backtest run, and repeats the process until five factors…
This document summarizes research on two equity-market questions. The first uses China’s IPO approval process as a natural experiment to examine how a newly approved offering affects existing, substitutable stocks. It reports negative price effects around…
The report compares linear, polynomial, Gaussian, and sigmoid support vector machine classifiers, along with support vector regression, for multi-factor stock selection. Its workflow extracts features and labels, preprocesses inputs, trains and tunes models…
This document introduces factor research as a quantitative way to study characteristics that may help explain asset returns and risk. It names market capitalization, quality, momentum, low volatility, and yield as examples, and describes using statistical or…
This stock screen combines four filters: price amplitude above a stated threshold, market capitalization below a stated ceiling, positive net profit, and exclusion of the STAR Market. The accompanying discussion presents the amplitude filter as a way to find…
This report explains how the MSCI China A50 Connect Index selects and weights large-cap Chinese equities. It chooses stocks across GICS industries, first taking leading free-float market-cap names by industry and then filling to 50 constituents; weights are…
This study examines quantitative stock selection within China’s electronics and electronic-components sectors. It reviews conventional fundamental and price-volume factors, reporting promising selection results for valuation, quarterly profit growth, return…
Return on equity (ROE) measures net income relative to shareholders’ equity, expressing how much profit a company generates from its equity base. The document gives the formula and a simple numerical illustration, then describes how investors may interpret…
This stock screening approach combines a Metaverse concept classification with an upward-sloping 30-day moving average and fundamental filters. It first selects companies associated with the theme, then looks for a rising average and firms below the stated…
This tutorial develops a stock factor from the sum of trading volume during the first 15 minutes of a session. It explains why opening activity may be informative: overnight news is reflected in early trading, the opening period contributes to price…
This post proposes screening Chinese equities using three conditions: buying-pressure share above a threshold, prior-day turnover value above a threshold, and a high dividend ratio in a specified historical year. It frames buying pressure as a sign of…
The document addresses how to combine multiple stock-selection factors when they have different preferred directions. Its example asks how to score one factor from low to high and another from high to low, then rank securities by the average of their scores.…