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 post presents an A-share stock screen that combines a technical indicator, reported earnings growth, and institutional ownership. It selects shares with RSI below 65, year-over-year growth in net profit attributable to the parent company above 20% and…
The post describes a stock-selection screen that ranks companies by capital-flow strength and considers share price and revenue growth. Its core growth condition compares 2021 revenue with 2018 revenue, requiring the ratio to exceed 1.1. The final proposed…
This proposed stock screen targets companies classified in the metaverse sector during 2021. It selects stocks that appeared on the previous day’s trading leaderboard, have market capitalization below the stated limit, and report positive net income. The…
This essay defines quantitative investing as using mathematical methods, data, and factors to guide security selection and trading. It groups approaches by time-series versus cross-sectional decisions, by signal source such as price and volume factors or…
This A-share screen combines daily amplitude above 1 with a positive weekly MACD reading and a listing history longer than one year. The article then adds valuation and profitability constraints: price-to-earnings below 20, price-to-book below 3, and return…
This post describes a Chinese equity screening idea that combines membership in the metaverse concept with positive net buying by large participants during the opening auction and a prior-day measure of main-fund control. Its stated interpretation requires…
This stock screen combines three criteria: rank equities by capital strength, require yesterday’s turnover rate to exceed 8%, and select companies whose 2019 dividend payout ratio exceeded 25%. The accompanying explanation treats capital strength as an…
This event-driven equity strategy buys stocks at the open after financial results are announced, selecting companies whose year-over-year net profit attributable to parent shareholders has grown by more than 30%. Eligible stocks are ranked by growth, and the…
The document summarizes a study of Alpha101 factors in China’s A-share market and a method for generating additional price-volume signals with genetic programming. It says the authors assessed existing factors using Rank IC, portfolio stratification, and…
This Chinese equity screening strategy selects stocks with turnover between 3% and 12%, three consecutive declining sessions, and return on equity above 15% for five consecutive years. The article presents the ROE filter as a way to favor firms with…
This report examines whether removing stocks selected by high-frequency multi-factor short portfolios can improve a CSI 300 index-enhancement strategy. It compares three approaches: combine standardized factor scores, use a regression model to forecast…
The document presents a Chinese stock selection rule combining price behavior, a market control measure, a weekly moving average crossover, and fundamental filters. The initial screen looks for amplitude above one, a daily control reading above 21, and a…
This article presents an equity screen combining three conditions: price range greater than one, return on equity above 15% in each of the prior five years, and turnover between 3% and 12%. It frames the filters as a way to find companies with sustained…
The document describes a Chinese stock screen that combines three conditions: MACD above its zero axis, a 20-day moving average above the 120-day average, and revenue growth between 2018 and 2021 exceeding a stated ratio threshold. It frames MACD and the…
The article compares two ways to build equity strategies: train a predictive model on chosen factors and a defined stock universe, or specify screening conditions directly. The AI approach can be quicker to assemble, but its behavior may be hard to diagnose…
The report reassesses valuation, growth, technical, and risk factors in Chinese A-shares after removing industry and style influences. Its core method is to adjust factor data for exposures such as industry, market capitalization, or beta, then test whether…
Fundamental quantitative investing applies statistical methods and algorithms to company accounts, industry information, and macroeconomic data to rank stocks by estimated value or quality. The document describes analyzing profitability, financial strength,…
This competition entry modifies a Deep Alpha convolutional neural network by supplying five time slices for each of 98 factors, rather than using a single slice. The network structure and most parameters remain close to the original model because training…
This report examines changes in concentrated holdings among Chinese active equity funds using quarterly disclosures and a daily estimate of fund exposure to a core group of crowded stocks. It compares the core group’s holdings over time, separates changes in…
This document describes a stock screen that combines market activity, reported earnings growth, and recent attention. It selects stocks with turnover between 3% and 12%, parent-company net profit growth above 20% and no more than 100%, and a listing on the…
The article describes an integrated order flow imbalance factor built from changes in bid and ask quantities across five limit order book levels. It explains how each level’s imbalance can be normalized by typical depth, then combined with principal…
The article proposes screening Chinese A-share stocks using three conditions: a dividend payout ratio above 25% in 2019, RSI below 65, and MACD above its zero line. It describes the dividend measure as a fundamental filter and RSI and MACD as technical…
This document describes a Chinese equity screening rule that selects Shenzhen main-board stocks with daily amplitude above 1%, a year filter for 2021, price-to-earnings ratios from 0 to 29.01, and price-to-book ratios from 0 to 3.11. It provides both an…
This short BigQuant assignment describes a daily stock-ranking approach built from a combination of total equity and trailing-twelve-month average return on equity. Each day, the strategy holds stocks with comparatively high values of the combined factor. It…