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 study measures a fund’s risk shifting by comparing the volatility implied by its latest disclosed holdings with the fund’s realized volatility over the same rolling period. Using quarterly holdings and return data for actively managed US domestic equity…
This brief Chinese-language support note addresses how to use factors produced by a genetic factor-mining process. It says the discovered factor has an expression, but that a user must convert the expression manually before sending it to a factor analysis…
The document describes an equity screen requiring price amplitude above 1, return on equity above 15% for five consecutive years, and more than three years since listing. It presents these conditions as a way to combine active trading with a record of…
This research summary examines Shanghai–Hong Kong and Shenzhen–Hong Kong Stock Connect, comparing northbound and southbound trading and describing the traits associated with northbound holdings. It reports that flows did not reliably anticipate market…
This A-share stock screen combines a range expansion condition, a Bollinger Band location filter, and a historical dividend ratio threshold. It selects stocks whose daily high-low range exceeds its 20-day average, whose close lies between the middle and…
This stock-selection rule combines a 14-period RSI below 65, positive daily return, and year-over-year growth in net profit attributable to parent shareholders above 20% and at most 100%. The article presents the combination as a way to find companies with…
This screening idea combines a daily high-low range threshold, consistently strong return on equity over five years, and a filter related to the previous day’s limit-up status. The stated rationale is to pair a measure of price movement with a longer-term…
The document describes a commodity futures strategy that ranks 28 markets by changes in Twitter-derived sentiment. It calculates daily sentiment from keyword-matched posts using a financial sentiment dictionary, then forms equal-weighted long and short…
This Chinese A-share stock screen selects shares with a daily high-low range above a stated threshold, while excluding Beijing-listed stocks and specified board categories. The article also describes a refinement that keeps prices close to a 60-period moving…
The document presents a stock-screening idea that combines MACD above its zero line, upward-diverging daily moving averages, and an external-to-internal trading volume ratio above a stated threshold. It then extends the screen with fundamental filters:…
The document describes a U.S. equity strategy based on balance-sheet accruals, the noncash component of reported earnings. It estimates accruals from annual changes in current assets, cash, current liabilities, short-term debt, income taxes payable, and…
This note proposes selecting stocks with MACD above zero, a favorable but undefined company-quality characteristic, and a positive price-to-earnings ratio. The rationale combines a technical trend signal with a basic profitability screen: positive MACD is…
This stock screen combines a daily price-range condition, a dividend-yield threshold tied to 2019, and a weekly MACD condition above zero. The stated logic seeks shares with some recent price movement, a high historical dividend yield, and positive…
This proposed stock screen combines amplitude above 1, institutional participation, and year-over-year growth in net profit attributable to parent-company shareholders above 20% and at most 100%. The final criteria specify institutional participation above…
This post proposes screening A-share stocks for turnover between 3% and 12%, market value below 10 billion yuan, scale above 200 million yuan, and no losses. It presents the screen as a way to combine trading activity, company size, and profitability, then…
The document describes a Chinese stock screen that combines a daily increase in reported holdings, a daily price gain, and a company size and profitability filter. Its initial description focuses on stocks with market capitalization below a stated ceiling…
This research summary explains how to build a machine-learning stock-selection process using historical factor values to predict subsequent returns. In the training stage, a supervised model learns the relationship between inputs and returns; in the testing…
This example turns a CAPM regression into a monthly stock-selection process. It takes a recent window of daily returns for eligible constituents, adjusts stock and benchmark returns by a stated daily risk-free rate, and regresses each stock’s returns against…
The document proposes a Chinese equity screening approach that selects robot concept stocks with daily amplitude above 1%, float capitalization below 10 billion, and no ST designation. It specifies screening before 10 a.m. and says a five-step limit-up…
This sample describes a high-dividend stock-selection model for Chinese equities. The process excludes special-treatment stocks, suspended securities, and Beijing Stock Exchange listings. It then screens for larger companies by market-capitalization rank,…
This sample strategy selects Chinese equities using a dividend yield ranking alongside size and valuation filters. It first removes special-treatment stocks, suspended shares, and Beijing Stock Exchange listings. From the remaining universe, it favors…
This stock screen combines a trading-activity filter with a size constraint and a profitability-quality condition. It selects shares with turnover between 3% and 12%, circulating market capitalization between 1 and 55 hundred million yuan, and return on…
This tutorial shows how to implement a collection of Chinese stock features and screening rules in BigQuant AIStudio 3.0. It divides them into expression features and expression filters, then explains that the same calculations can be entered as a SQL query.…
This report describes a Chinese equity index-enhancement strategy built from a composite stock-selection signal and portfolio constraints. It combines factors spanning company size, valuation, growth, profitability, technical behavior, liquidity, and…