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 document explains how BigQuant’s factor research platform handles user-created factors and downstream dependencies. A researcher can combine platform factors A and B into a derived factor C, submit C to the platform, and let other users with access clone…
This research summary compares common stock-selection factors in the U.S. and Chinese markets, with a test of seven factor groups and 24 signals in the Russell 3000 universe. It reports that profitability, valuation, and growth measures were among the…
This Chinese-language post presents an equity screening rule for stocks associated with robotics. It combines a turnover-rate band of 3% to 12%, a stated circulating market-cap ceiling below 10 billion yuan, and a high ranking by large-order net flow. Its…
This entry introduces two overseas research topics: persistent bias in analyst expectations and holiday effects in stock markets. The first paper is described as examining habitual distortions in conventional analyst expectation factors and developing a…
The document distinguishes two broad uses of multi-factor models: regression models, which focus on explaining outcomes, and ranking models, which aim to identify stocks with attractive returns. Within the regression approach, it describes two ways to obtain…
The document describes a Chinese stock screen that combines three filters: membership in the metaverse sector, positive institutional activity, and a positive price-to-earnings ratio. It presents the screen as a way to identify companies in a chosen theme…
This weekly report summarizes factor returns across broad Chinese equity universes and several index or liquidity-based stock pools. It describes positive performance for profitability and book-to-price factors in the full market, while size, beta, and…
This literature summary describes a stock-market return forecasting method that decomposes returns into dividend yield, profit growth, and price-to-earnings multiple growth. It says the source study uses the different time-series properties of these…
This code sample implements parts of the Alpha101 factor set using historical equity fields such as open, high, low, close, volume, returns, and volume-weighted average price. The formulas combine rolling ranks, moving averages, correlations, price changes,…
This Chinese-language research summary describes equity industry-rotation models for cyclical sectors, non-cyclical sectors, and the full market. It ranks industries using composite scores built from different factor groups: historical and expected…
This note describes a Chinese equity screen combining a relative strength index below 65 with revenue in 2021 more than 1.1 times revenue in 2018. It further limits the universe to companies valued below 10 billion yuan and excludes loss-making firms. The…
This Chinese-language post outlines a stock screen for companies classified in the metaverse industry. It combines a maximum share-float threshold with a minimum market-value threshold, aiming to find larger companies whose tradable share supply remains…
The document explains how to decide whether to remove sector exposures from equity factor portfolios. It splits a stock characteristic into a within-sector component, which ranks companies against peers, and an across-sector component, which captures…
This article translates Steve Leuthold’s value-investing approach into a monthly stock-selection method for Chinese equities. It ranks candidates using low price-to-book, price-to-earnings, and price-to-cash-flow ratios relative to market averages, alongside…
This Chinese-language post describes an equity screen combining daily price range, five years of return on equity above 15%, and a daily price change between −5% and 2.6%. It also proposes adding a price-to-book ratio no higher than five and revenue growth…
This Chinese-language article introduces a workflow for building custom stock-selection strategies on the older BigQuant platform. It contrasts platform-generated AI strategies, which learn from selected factors but may be difficult to interpret or tune,…
This stock-screening idea selects companies whose codes begin with 60, whose amplitude exceeds one, and which were described as under major-player control on the previous day. The rationale is to narrow attention to volatile stocks with a potentially notable…
The document describes a Chinese A-share watchlist screen using three conditions: daily price amplitude above 1%, at least one limit-up event in a recent lookback period, and revenue in 2021 more than 1.1 times revenue in 2018. It provides example indicator…
The document describes a Chinese stock screen combining a daily price-range condition, prior-day turnover within a specified band, and return on equity above 15% for five consecutive years. It presents the screen as a way to pair recent trading activity and…
This article explains random forests and decision trees, then applies a random forest regressor to short-horizon Chinese stock selection. It describes bootstrap sampling, random feature selection, averaging tree predictions, tree pruning, regression split…
This Chinese A-share screening proposal selects stocks with RSI below 65, a circulating market value from 5 billion to 10 billion yuan, and positive institutional activity. The article frames the filters as a combination of a short-term technical measure,…
This tutorial explains how to use Alphalens to examine whether factor scores are associated with future asset returns. It distinguishes factor research from portfolio backtesting: factor analysis helps characterize predictive power, consistency across…
This document describes a Chinese equities screening rule combining price amplitude above 1, a nonempty name for outstanding convertible bonds, and a 2019 dividend ratio above 25%. It presents the screen as a way to find volatile stocks with stronger company…
This review surveys three directions in factor investing: refining established factor methods, incorporating new data and models, and using factors as a basis for asset allocation. It describes improving value signals with multiple valuation measures,…