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 example filters shares using three conditions: price amplitude above one percent, a positive ten-day return below 35%, and year-over-year net profit growth attributable to parent-company shareholders above 20% and no more than…
This research summary explains how stock exposure to macroeconomic indicators can inform equity decisions. The proposed logic is conditional: when an indicator is expected to rise, favor stocks with positive or statistically strong sensitivity; when it is…
This Chinese-language strategy note tests a simple stock screen for mainland China A-shares. It selects companies using three annual-report criteria: net profit growth above a threshold, return on equity above a threshold for three consecutive years, and a…
This introductory tutorial explains machine learning concepts and linear regression with finance examples. It distinguishes features from labels, outlines training, validation, and test data, and contrasts supervised with unsupervised learning,…
This Chinese equity-selection proposal combines three filters: rank companies by trading volume, retain firms with market capitalization below 10 billion yuan that are not loss-making, and require at least two limit-up sessions during the prior 500 days. It…
The document presents a Chinese equity screening rule for companies classified in the metaverse industry. It selects stocks whose previous-day actual turnover rate is between 3% and 28% and whose price-to-earnings ratio is positive. It identifies the concept…
This stock-selection rule combines a minimum daily price range, positive weekly MACD, and a historical dividend condition. It selects stocks whose high-to-low range exceeds 1% of the prior close, whose weekly MACD is above zero with the MACD line above its…
This report evaluates momentum based sector allocation in Chinese equities. It compares ordinary lookback return ranking with risk-adjusted alternatives, including Sharpe ratio, information ratio, and a path-adjusted measure that divides return by the sum of…
This Chinese equity screening example combines a technical indicator, earnings growth, and trading activity. Its stated criteria are RSI below 65, year-over-year growth in net profit attributable to parent-company shareholders above 20% and no more than…
This report summarizes quantitative approaches to enhancing returns over Chinese equity benchmarks while managing tracking error. It frames enhancement as a blend of active and passive investing and describes a top-down range of choices: market exposure,…
This document outlines a monthly long-only stock selection strategy for the Chinese A-share market. It ranks the universe using a market-cap rank and a 20-day average of price changes, combines the two into a score, then holds 50 equally weighted stocks with…
This note describes a Chinese A-share screen combining daily price range, profitability, and recent trading attention. It seeks stocks with an amplitude above 1, return on equity above 15% for five consecutive years, and an appearance on the previous day’s…
This research note explores additional technical factors for stock selection in the CSI 500 universe. It groups candidate measures into turnover behavior, illiquidity, and combined volume-price information. Turnover candidates include stability, coefficient…
This Python class assembles a subset of Alpha101-style equity signals from price, volume, VWAP, returns, and precomputed factor series. Its methods apply operations such as cross-sectional ranking, rolling correlation, covariance, time-series ranking, decay,…
This report describes an initial step in building a multi-factor equity model: choosing which factors to combine. It proposes reducing the factor search space by including exposure relative to a benchmark as one screening criterion, alongside the strength of…
This A-share screen selects stocks whose daily amplitude exceeds 1, whose daily change in controlling-shareholder holdings has an absolute value above 21, and whose 2019 dividend ratio exceeds 25%. The article describes these inputs as proxies for price…
This stock-selection screen targets listed companies with turnover between 3% and 12%, positive earnings, and market capitalization below 10 billion yuan, while excluding Beijing-listed shares and stocks marked as special treatment. The article frames the…
This document describes a Chinese equity screening rule for Shenzhen main-board stocks. It selects shares with turnover between 3% and 12%, an opening price within 5% of the 10-day simple moving average of closing prices, a price-to-earnings ratio from 0 to…
This stock-screening proposal selects companies classified in the metaverse industry that also carry a robotics concept label. It further requires the previous day's actual turnover to fall between 3% and 28%, and limits circulating market value to below 10…
This review examines the low-risk effect: securities with lower measured risk have historically delivered stronger risk-adjusted returns than the standard CAPM relationship would predict. It explains betting against beta (BAB), which leverages a low-beta…
This research summary compares two ways to incorporate analyst expectations into equity selection. A parallel approach blends forecasts with other factors or uses them jointly to predict returns. The proposed serial approach first builds a conventional…
The post describes a Chinese equity screen seeking companies with no losses, scale above a stated minimum, market capitalization below a stated ceiling, and a recent position increase greater than 5%. It frames the approach as a short-term filter based on…
This brief BigQuant support exchange explains two ways to control which stocks enter a factor analysis. One approach is to select a predefined universe, such as the CSI 300 or CSI 500, which fixes the eligible stock set through an index-based pool. The other…
This report tracks equity factors using information coefficient (IC) summaries and the relative returns of factor-ranked long portfolios. It describes ranking stocks by factor values, selecting the top decile for each factor, and weighting holdings by the…