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 describes a Chinese stock selection screen combining three conditions: at least five moving averages overlap, the 20-day moving average is above the 120-day average, and the 2019 dividend payout ratio exceeds 25%. It presents the overlap…
The document describes a stock screen for main-board shares using current turnover, circulating market value, and the previous day's actual turnover. The specified filters are turnover between 3% and 12%, circulating market value between 5 billion and 10…
This strategy note describes a China A-share approach that blends model-based stock ranking with technical timing. It begins with a manually defined pool of roughly 100–300 large-cap names associated with the CSI 150 universe, then ranks candidates using…
This research summary outlines a framework for evaluating private index-enhanced funds through their active exposures to risk factors. Because benchmark exposure is inherent to these products, the analysis treats deviations in style and industry exposures as…
This research digest summarizes two papers relevant to investment research and portfolio decisions. The first examines analyst skill and influence across products at fourteen mutual fund firms. It reports that buy-side analysts contribute positively to…
This research summary explains that style rotation depends on defining each style accurately before building a timing model. It distinguishes styles represented by current stock characteristics, such as size, value, reversal, and momentum, from styles…
The post describes a Chinese stock screen that ranks shares by a capital-strength measure, keeps those associated with a non-empty outstanding convertible-bond name, and restricts the data to 2021. It presents the conditions as a way to identify stocks with…
This support discussion explains a factor-analysis failure caused by insufficient coverage of a derived price feature. The feature compares the close with the high-low range and then takes a one-period change. Although feature extraction returned data, the…
This strategy studies a daily selection of ten Chinese main-board stocks ranked by free-float market capitalization, from smallest to largest. It excludes stocks marked ST, suspended stocks, companies listed for less than a year, and listings on the STAR…
This A-share stock selection method combines three filters: at least five moving averages must converge, the stock must have a positive price-to-earnings ratio, and its 20-day moving average must be above its 120-day moving average. The convergence condition…
This study builds a text-derived earnings surprise factor, SUE.txt, to capture post-earnings announcement drift in Chinese equities. It uses analyst report titles and summaries related to earnings forecasts, converts them into word-frequency features, and…
This research summary describes a quantitative factor intended to measure listed companies’ competitive advantages using financial statement data. It evaluates the factor across China’s A-share universe, compares its relationship with established quality and…
This Chinese equity screen combines a metaverse industry classification with a rising 30-day moving average and a revenue growth condition. It requires the current 30-day average to exceed its prior value and the stated 2021-to-2018 revenue ratio to be…
This research framework measures how crowded equity industries are using price and trading activity. It groups candidate measures into trend-oriented indicators, supplementary volume and order-flow measures, and relative comparisons across industries.…
This stock selection method filters for turnover between 3% and 12%, an opening price within roughly 5% of the 10-day moving average, and money-flow volume greater than its prior reading. The post frames the last condition as ranking or selecting for…
This report summary outlines an investigation of how high-frequency factors behave across different periods and domains, along with an analysis of what may influence their performance. The authors say earlier work built factors from trading logic using…
This Chinese equity screen selects shares with turnover between 3% and 12%, year-over-year growth in net profit attributable to parent-company shareholders above 20% and no more than 100%, and first-level bid volume greater than first-level ask volume. The…
The document describes an A-share stock screen combining daily price amplitude above 1%, robotics-concept membership, floating market capitalization below 10 billion yuan, and a 2019 dividend payout ratio above 25%. It sketches how to express those…
This overview presents active quantitative management as a process of forecasting factor returns and managing portfolio exposures. It describes a linear model in which each stock’s return is associated with its factor exposures, factor returns, and a…
This stock-selection proposal screens mainland Chinese main-board shares using an RSI below 65, a daily gain above 1%, and a dividend ratio above 25% for 2019. Its rationale combines a technical condition intended to identify room for a rebound, a positive…
This stock-screening proposal combines three filters: today’s increase in institutional holdings must exceed five percent, the share price must have fallen for seven consecutive sessions, and the company’s 2019 dividend payout ratio must be above twenty-five…
This review of Piotroski and So’s study examines whether value-growth return differences arise partly from investors’ mistaken expectations about future company performance. It describes FSCORE, a composite of nine binary signals covering profitability,…
This report evaluates 56 equity factors built from Chinese A-share trading and financial statement data. It sorts stocks into ten groups by each factor, measures the next month’s value-weighted portfolio returns, and tests the return spread between the…
The document presents an outline for a VeighNa training course on machine-learning-based cross-sectional multi-factor strategies, also described as alpha strategies. It frames these methods as a form of statistical arbitrage applicable to equity selection,…