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 stock-selection post describes a screen for metaverse-related shares on the Shenzhen main board. It combines a limit-up event within the prior 25 days with positive price-to-earnings and price-to-book ratios capped at 29.01 and 3.11,…
This report reviews quality-focused Smart Beta investing in overseas and Chinese markets. It describes the approach as an index strategy built from company financial data to capture underlying business quality, then compares product development and index…
The document outlines an equity screen for stocks in the metaverse theme. It selects companies whose closing price is above the five-day moving average and that meet a chosen company-characteristic filter. It also proposes considering whether valuations are…
This stock screen selects Chinese equities with daily price amplitude above 1%, market capitalization below 10 billion yuan, positive net profit, and previous-day turnover above 60 million yuan. It also excludes names containing the special-treatment marker…
The post proposes a daily, after-close stock screen combining three filters: five-period amplitude above 1, institutional participation above 30, and total shares above 200 million. It presents the combination as a way to find stocks with notable price…
This Chinese-language post describes an equity screen that ranks stocks by capital strength and today’s auction trading value, selecting the top five on each measure. It frames the measures as proxies for market attention and activity, and presents the…
This article explains random forests through CART decision trees, Gini impurity, bootstrap sampling, and aggregation. Trees are trained on resampled data, while their predictions are combined by voting for classification or averaging for regression. It also…
This post describes a stock-selection screen combining a 2019 dividend ratio above 25%, today’s increase in holdings above 5%, and a recent price change between minus 5% and 2.6%. It interprets the holdings measure as buying interest, the bounded price move…
This Chinese-language post describes an equity screen combining a 2019 dividend ratio above 25%, daily amplitude greater than 1, and exclusion of stocks that closed at the upper price limit the previous day. It presents the amplitude condition as a way to…
This Chinese stock-selection proposal combines clustered moving averages, market attention, and historical revenue growth. It initially describes selecting stocks with at least five overlapping moving averages, ranking by popularity, and requiring 2021…
This note describes a screen for stocks with daily amplitude above 1%, at least five overlapping moving averages, market capitalization below 10 billion yuan, and no losses. It presents the combination as a way to mix a technical condition with a basic…
This note proposes an A-share screen combining a reported buying or position-increase share above 5%, company scale above 200 million, and a 2019 dividend payout ratio above 25%. Its later refined logic adds price-to-earnings below 20, price-to-book below 2,…
This note outlines a China stock screen for robot-themed companies using a turnover range of 3% to 12%, a circulating share count or capitalization constraint, and a circulating market value below 10 billion yuan. The described logic also refers to a maximum…
This incomplete strategy sketch combines three proposed stock filters: a 2019 dividend payout ratio above 25%, net inflows from large orders during the afternoon, and convergence among at least five moving averages. It explains convergence as possible…
This proposed Chinese equity screen combines robotics concept membership, floating market capitalization below 10 billion yuan, turnover between 3% and 12%, and return on equity above 15% over five consecutive years. The financial filter is intended to favor…
This post sketches a screen for Shenzhen main-board stocks using price-to-earnings and price-to-book ranges, a market capitalization ceiling, and a condition described as having no losses. It also proposes selecting shares with several closely grouped moving…
This post outlines a China A-share stock screen combining capital strength ranking, a minimum tradable market value, and a historical dividend payout ratio threshold. It interprets strong capital flows as a sign of market attention, larger capitalization as…
This research notebook outlines a simple test of whether stocks with stronger prior returns tend to earn higher returns next day. It measures momentum from prices over a twelve-month window while omitting the latest month, then computes next-day returns. The…
This post describes a Chinese equity screen combining positive institutional activity, a threshold for the day's increase in holdings, and a 2021 date filter. It interprets the holding-change measure as a sign of buying interest and positive institutional…
This note describes a Chinese equity screen that combines a high dividend ratio in 2019, net inflows from large orders in the afternoon, and a daily increase in holdings above a stated threshold. It interprets the filters as capturing dividend distributions,…
This document presents a Chinese equity screen based on reported earnings growth, a historical dividend payout ratio, and a measure of current buying activity. Its final criteria also include a market-capitalization floor and a price-to-earnings ceiling. The…
This Chinese equity screening idea combines a daily price-range threshold, a dividend-yield condition based on 2019 data, and a candlestick pattern described as a morning star. The article supplies indicator formulas and sample Python logic for applying…
This stock screen combines three conditions: daily trading range above a threshold, return on equity above 15% in each of five years, and a weekly crossover of the five-period moving average above the ten-period average. The rationale pairs a profitability…
The document introduces factor research as a way to study characteristics that may help explain the returns and risks of financial assets. It names market capitalization, quality, momentum, low volatility, and yield as examples of factors that researchers…