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 research framework measures crowding in equity industries using price and trading activity. It groups candidate indicators into trend-oriented measures, including price deviation and moving-average dispersion; supplementary measures such as turnover,…
The strategy sorts U.S. stocks by price-to-book ratio to form a value portfolio. Within a universe limited in the implementation to the largest 3,000 stocks by market capitalization, it buys the lowest price-to-book quintile and shorts the highest. The…
The document presents a Chinese stock screen using turnover, circulating share capital, and a historical dividend payout ratio. Its initial criteria specify turnover between 3% and 12%, circulating shares no greater than 5.5 billion, and a 2019 payout ratio…
This 2020 investment analysis argues that Hong Kong equities looked attractive after the Hang Seng Index’s price-to-book ratio fell below one during the pandemic selloff. It compares prior episodes of the index falling below or nearing book value and reports…
The discussion explains that a supervised model learns from factors paired with labels, where a label defines the target. For stock selection, the target might be future returns over a chosen horizon. The model and label together determine what predictions…
This study examines how exposure to international trade relates to US stock returns. It uses industry transportation costs as a measure of globalization exposure, sorting stocks into portfolios and comparing their excess returns. The reported evidence…
This proposed equity screen selects stocks with turnover between 3% and 12%, a listing year of 2021, and a dividend ratio above 25% for 2019. The article presents these as activity, company-age, and historical payout filters. It cautions that relying on a…
The document answers whether a factor can be neutralized for both market capitalization and industry exposure. It recommends including market capitalization as a continuous explanatory variable and industry membership as dummy variables in the same…
This report applies behavioral finance to equity factor research, using the disposition effect and prospect theory to explain how investors' risk preferences may differ depending on whether they hold gains or losses. It constructs a capital gains overhang…
This report compares groups of Chinese equity factors across industries, focusing on cash flow, valuation, size, momentum, liquidity, volatility, beta, and alpha. Its stated findings come from portfolio net value behavior, industry-level results, ranking…
This study describes a grouped-factor approach to combining quality, growth, and value signals for A-share stock selection. A quality composite based on enterprise value and return on equity ranks stocks and defines a filtered universe for further selection.…
This report summary describes a stock-duration factor inspired by bond duration and constructed using company financial data and analyst consensus estimates. It reports that the factor’s cross-sectional behavior varies with market conditions and across…
This A-share screening proposal combines three conditions: market capitalization below 10 billion yuan, no reported losses, prior-day actual turnover between 3 and 28, and today’s position-addition share above 5%. The article interprets these as filters for…
This research note summarizes a study of downside volatility as a measure of portfolio risk and compares it with conventional volatility. It reports that the measures are generally closely related, but their relationship weakens during historically turbulent…
This 2020 research summary argues that the Shenzhen 100 could suit long-term allocation, describing it as a liquid large- and mid-cap index with both growth and blue-chip characteristics. It cites fundamentals reported at the time, including return on equity…
This Chinese A-share screening proposal starts with stocks in the robot concept whose circulating market capitalization is below 10 billion yuan and whose daily high-low range exceeds 1%. Its initial momentum condition is that at least one session in the…
This stock screen combines three filters: membership in the metaverse industry, a positive one-day price return, and return on equity above 15% in each of the previous five years. The document explains that sustained ROE is intended to identify firms with a…
The proposed stock screen selects Shenzhen main-board shares with daily high-to-low amplitude above 1%, at least one limit-up event in the prior 25 days, positive price-to-earnings ratios below 29.01, and positive price-to-book ratios below 3.11. Selected…
These release notes describe additions to Zipline’s Pipeline API in version 0.9.0. New datasets expose buyback authorizations and dividend information organized by ex-date, payment date, or announcement date. Related built-in factors measure business days…
The report evaluates ten equity valuation measures, including earnings, book, sales, cash flow, dividend, enterprise value, and growth based ratios. It first describes how factor levels vary across industries and company sizes, and how those relationships…
This post describes a Chinese equity screening rule that combines a technical indicator with company size and historical revenue growth. It selects stocks with RSI below 65, a circulating market capitalization between 5 billion and 10 billion yuan, and 2021…
This article proposes a Chinese stock screen for companies associated with the metaverse, limited to non-STAR Market listings and a circulating share count at or below the stated cap. The accompanying rationale characterizes the screen as a way to focus on…
This post outlines a Chinese equity screen using three conditions: a price-range measure above a threshold, a dividend yield threshold tied to the stated 2019 dividend criterion, and a closing price above the prior day’s low. It presents these filters as a…
This forum exchange addresses two practical questions about factor analysis: how to submit a factor that needs Python processing beyond SQL, and whether factor values must exist for every stock on every trading day. The reply says the SQL field may be left…