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 stock-screening idea combines three conditions: at least five moving averages converge, company scale exceeds two hundred million in the source’s stated units, and the dividend payout ratio for 2019 is above 25 percent. The post interprets converging…
This document describes an A-share screening idea that combines a reported increase in holdings above 5%, technical crossover signals, valuation filters, and a historical dividend payout ratio above 25%. The discussion associates capital inflow and…
This Chinese-language conference excerpt introduces how artificial intelligence is being adopted by global asset managers. It frames technology as one response to falling margins per unit of managed assets, alongside efforts to grow assets under management.…
The document describes a Chinese equity strategy that first screens for companies with high dividend yields, PEG below one, and share prices between 3 and 15, then selects stocks with market capitalizations below 10 billion yuan. It frames these rules as a…
This equity factor study measures buying and selling pressure through the amount of time a stock spends at different positions within its recent price range. It first normalizes price between the interval’s high and low to obtain relative price position…
This Chinese equity research note develops industry-rotation signals from several types of institutional money flow. It treats a flow source as useful when its derived signals show a reasonably orderly relationship across ranked groups and a tolerable…
This Chinese-language post introduces a custom-coded stock selection strategy on the BigQuant platform. It says the strategy combines five selection rules, mainly seeking stocks that have fallen sharply and begun to stabilize, while also attempting to…
The report presents a framework for dynamically adjusting multi-factor portfolio weights by forecasting factor returns and their covariance. It replaces unconditional expectations with conditional expectations based on external market variables, allowing…
This short Chinese-language Q&A explains the delayed-entry setting in factor analysis. The setting shifts the point at which stocks are purchased based on factor values by a specified number of days. The question concerns a one-day rebalance cycle and asks…
The document outlines a Chinese equity screening approach that starts with price amplitude, a minimum market capitalization, and a comparison of 2021 revenue with 2018 revenue. It discusses limitations of relying on revenue growth alone, including omitted…
This proposed equity screen combines a price range condition, sustained profitability, and a snapshot of order book demand. It selects stocks whose high minus low is at least one unit, whose return on equity exceeds 15% in each of the prior five years, and…
This study tests whether machine learning can explain stock returns left unexplained by a conventional linear equity factor model. It uses 22 style factor exposures to predict standardized stock specific returns, then evaluates boosted trees, random forests,…
This stock selection proposal combines three filters: daily price range, five consecutive years of return on equity above 15%, and large-order net volume above 0.05 for at least three days. It then suggests smoothing that flow measure and requiring its…
This proposed equity screen combines three conditions: today's increase in holdings must exceed 5%, the company must have been listed before 2019, and its 2019 dividend payout ratio must exceed 25%. The article interprets the holdings increase as possible…
This stock-selection example combines three filters: RSI below 65, revenue in 2021 more than 1.1 times its 2018 level, and an opening price within 6% of the prior close. It proposes selecting the first N qualifying stocks and holding them for one year. The…
The proposed Chinese equity screen selects stocks classified in the metaverse industry when the open is near a ten-day moving average and the 2019 dividend ratio exceeds 25%. The post presents a formula-style version and a Python example. It frames…
The document describes a framework for evaluating equity factors and combining selected factors into a portfolio. It estimates factor returns with periodic cross-sectional robust regressions, measures the relationship between factor exposures and subsequent…
This post describes a Chinese-equity screening rule that combines membership in the metaverse theme with positive net buying attributed to major participants during the opening auction. It further limits candidates to Shenzhen main-board stocks using…
This proposed Chinese A-share screen starts with stocks in the metaverse theme, then selects for an opening gain below 6% and a circulating share count no greater than 5.5 billion. The article’s final version adds a price-to-earnings ratio below 60. It…
The document contrasts selecting stocks by relative rank with selecting them by an absolute model score. A ranking method can always choose the highest-ranked names in a universe, even when their scores are weak. The proposed alternative sets a minimum score…
The post outlines a stock screen combining three conditions: intraday high-low range relative to the open above 1%, a stated 2019 dividend-yield measure above 25%, and daily change from the open between -5% and 2.6%. It presents the conditions as a way to…
This summary describes an analysis of actively managed equity funds and funds with substantial equity exposure. Its stated selection process combines historical return data with portfolio holdings, sector exposure, and risk considerations to identify funds…
This example presents a monthly Chinese equity strategy using constituents of the CSI 300 as its starting universe. It excludes suspended and specially treated stocks, then calculates several cumulative return measures over different lookback windows. For…
This research review examines whether predictable stock returns in particular months reflect changing risk or temporary mispricing. Its central test is seasonal reversal: if a stock earns unusually high returns in one calendar period because demand…