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.
22,592 documents
This research summary examines additional ways to monitor crowding in equity investment factors. Factor crowding refers to a situation in which heavy investment in a factor may weaken its returns or make them less stable. The analysis considers asset…
This summary of a 2020 study examines equity factor strategies in China’s A-share market, where market structure and regulation can affect how familiar investment signals behave. It reports that carefully designed traditional factor strategies delivered…
The proposed Chinese stock screen combines three filters: convergence among the 5-, 10-, 20-, 60-, and 120-day moving averages; turnover between 2% and 9%; and revenue in 2021 more than 1.1 times revenue in 2018. The document frames moving average…
This proposed Chinese equity screen selects stocks classified in the metaverse industry, with the opening price near the 10-day moving average, and with a nonempty field for an outstanding convertible bond name. The technical condition is presented as a…
This Chinese stock-selection proposal combines a relative strength index below 65 with a condition involving price change multiplied by net large-order volume, then filters for prior-day turnover above 60 million. It frames the RSI and order-flow measure as…
This fund analysis describes a Chinese equity strategy that combines quantitative signals with qualitative judgment. Its inputs include mean reversion in returns and valuations, sector rotation, fundamental reversals, event effects, investor behavior and…
The document describes a Chinese equity screen that selects stocks with daily amplitude above 1%, a robotics theme, and circulating market capitalization below 10 billion yuan. It also discusses broadening the original 2021 time filter to include data from…
This stock screen looks for shares with amplitude above one and ranks eligible stocks by the day’s opening auction amount, selecting the top five while excluding the STAR Market. The article frames auction activity as a way to find stocks attracting market…
This stock screen combines three technical conditions: amplitude above one, the stock’s average price above its five-day moving average, and a positive daily MACD value. The document presents the conditions as a way to focus on short-term price action, using…
This platform support discussion reports sparse cross-sectional coverage for two company growth fields: year-over-year growth in free cash flow and year-over-year growth in adjusted net profit. The author says the free cash flow field has only about 800…
This proposed Chinese equity screen looks for stocks with RSI below 65, a positive interaction between percentage price change and net inflow attributed to very large orders, and a share price or average price above its five-day moving average. The document…
This Chinese stock selection proposal combines three filters: at least five moving averages converge, KDJ has just formed an upward crossover, and the stated 2019 dividend ratio exceeds 25%. The explanation interprets moving average convergence as possible…
The article outlines an A-share stock screen centered on companies classified in a metaverse-related category. Its initial conditions are a positive recent return and a six-period RSI below 65. The proposed refined screen also keeps stocks in the top half by…
This proposed stock screen combines a prior-day price range greater than one percent, appearance on the previous day’s trading leaderboard, and a company or exchange classification. The examples operationalize the classification as the Science and Technology…
This stock-selection idea combines three filters: rank shares by volume ratio and retain the top 100, require a positive prior-day price change without a limit-up close, and require the 30-day moving average to be rising while price remains above it. The…
This screen focuses on stocks classified in the metaverse theme. It pairs an upward-sloping 30-day moving average with a price above a long-term moving-average threshold. The prose refers to the previous day's price and a 250-day average, while the formula…
This technical stock screen selects shares with a daily high-low range above one percent of the previous close, a high equal to the greater of the current and prior day's highs, and a weekly five-day moving average crossing above the ten-day average. The…
This stock screen targets companies in the metaverse theme that have a positive return and a bullish moving-average signal. Its formula reference uses a crossover of the five-day average above the ten-day average. The accompanying Python example instead…
The document proposes screening Chinese stocks for daily price range above a threshold, market capitalization below a limit, positive net profit, and a minimum period since listing. It explains these filters as a way to focus on more volatile, smaller…
The document summarizes research on a listwise learning-to-rank loss designed for constructing long-short stock portfolios. The proposed loss builds on ListMLE but gives extra emphasis to the top and bottom of the ranking, where long and short selections are…
The document describes an A-share stock screen that combines a 14-period RSI ceiling with the sign of price change multiplied by a five-day proxy for large-order flow. It also filters for positive auction-period price movement and buying activity, minimum…
This document describes an equity factor based on intraday volume moving through a low-to-high-to-low cycle. It smooths volume with a nine-minute neighborhood, identifies a peak and the lowest-volume points before and after it, then measures price-change…
The article presents a workflow for translating a written trading specification into a QMT strategy. Its example applies ADX and directional indicators to one Chinese-listed equity: buy when ADX exceeds a threshold and positive directional movement is…
This short strategy summary describes a quantitative stock-selection approach built with TabNet, a machine-learning model. It says the approach extracts 98 price and volume factors, trains the model on data from 2010 through 2018, and applies predictions to…