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
The document outlines the five factors used to explain differences in stock returns: market excess return, company size, book-to-market value, profitability, and investment. It describes each as a comparison between groups of stocks, such as small versus…
This BigQuant example builds a daily Chinese-stock portfolio by ranking eligible shares on 30-day turnover variability relative to their industry group. It filters out risk-warning stocks and applies price and listing-age conditions, then selects five names…
The report proposes benchmark indexes built from funds that seek to outperform the CSI 300 or CSI 500. It combines contractually designated index-enhanced funds with other funds identified as similar through their benchmarks, tracking error, and historical…
This Chinese equity screen selects stocks with turnover between 3% and 12%, excludes Beijing-listed shares, and limits circulating share count to 5.5 billion. The article also discusses combining these filters with company and industry information, valuation…
This stock-selection note combines a reported increase in holdings above 5% with parent-company net profit growth greater than 20% and no more than 100%, using 2021 data. The stated rationale is to find companies with signs of buying interest and growing…
This Chinese-language post describes an equity screen combining a relative strength index below 65, seven consecutive down sessions, and return on equity above 15% for each of five consecutive years. It presents the RSI and price streak as technical filters…
This brief educational note defines quantitative investing as expressing an investment strategy in code so that a computer can carry out trading in place of manual execution. It identifies reduced emotional interference as one potential benefit of rule-based…
This Chinese equity screening concept combines positive reported returns, a market capitalization below 10 billion yuan, a history without losses, and capital strength ranked from high to low. The article interprets capital strength as a measure of net money…
The document explains how factor mining outputs can be connected to strategy research. The mining module produces factor expressions as strings; researchers can then use those expressions to test factor effectiveness and run strategy backtests. It offers no…
The document presents a Chinese A-share stock screen combining market activity, a prior session’s 9:15 matching price at the limit-down level, and profitability history. It first describes conditions of amplitude above 1 and ROE above 15% for five…
The discussion clarifies how to read a BigQuant money-flow factor field whose name ends in a zero window. The question is whether “past zero trading days” means there are no observations to average, even though the description refers to an average net active…
This Chinese stock-selection strategy screens for shares with turnover between 3% and 12%, year-over-year growth in net profit attributable to the parent company above 20% and at most 100%, and a positive trailing price-to-earnings ratio. The article frames…
This stock-selection example screens Shenzhen main-board shares associated with the metaverse theme. Its stated criteria require price above the five-day moving average, positive price-to-earnings and price-to-book ratios, and upper bounds of 29.01 and 3.11…
The report describes stock-return prediction with machine-learning models built for short, medium, and long forecast horizons. It groups selection factors according to their information coefficients at different horizons, reflecting the idea that factor…
This report outlines a first step in building a multifactor model: choosing which factors to combine. It proposes screening combinations using three criteria: how strongly a factor differs from benchmark exposure, how correlated the factors are with each…
This research summary outlines three refinements to genetic programming for finding stock-selection factors: fitness measures based on mutual information and long-only excess return, ways to transform nonlinear factors, and validation procedures intended to…
This report studies the accuracy of analysts’ consensus earnings-per-share forecasts for China A-shares and develops forecast-bias signals for stock selection. Its full-sample statistics indicate substantial errors and an overall optimistic bias. An…
This study evaluates 19 factors derived from sell-side analyst consensus data, including forecasts of financial measures, analyst ratings, and attention. It tests the factors in several Chinese equity universes and across industries. For financial forecast…
The document presents a framework for deciding whether a candidate multifactor strategy adds value beyond a small set of investable reference factors, called elementary smart betas. It models each candidate through its exposures to those factors and its…
This stock selection rule combines membership in the metaverse theme with a morning price-change cutoff and a historical dividend ratio. The stated rationale is that limiting the early-session rise may help identify rebound candidates, while a dividend ratio…
This proposed A-share stock screen combines turnover between 3% and 12%, a newly formed KDJ bullish crossover, and return on equity above 15% in each of the past five years. The article presents turnover as a measure of trading activity, KDJ as a technical…
This article describes an equity screen combining price amplitude above a stated threshold, an upward relationship among recent moving averages, and a 2019 dividend payout ratio above a stated threshold. It frames the conditions as a way to find volatile…
This stock screen requires turnover between 3% and 12%, year-over-year growth in net profit attributable to parent-company shareholders between 20% and 100%, and a share price above its 250-day moving average. It combines a trading-activity range and a…
This post proposes a rules-based screen for Chinese A-share stocks in a designated metaverse sector. It combines a recent trading-activity condition with a profitability filter requiring return on equity above a stated threshold across five consecutive…