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 post describes a Chinese equity screening rule that combines a price movement condition, a low-price filter, and year-over-year growth in net profit attributable to parent-company shareholders. It specifies that the latest quarter’s profit growth should…
The document describes four ways to use the short-side information in high-frequency equity factors when building a CSI 300 index-enhancement strategy. It focuses on factors whose ability to identify weak stocks is stronger than their ability to rank…
This announcement outlines a two-day VeighNa training course on machine-learning cross-sectional multi-factor strategies, also described as alpha strategies. It presents their use in equity selection, index enhancement, absolute-return portfolios, and…
The document summarizes a fund-selection method that combines portfolio holdings and historical returns to estimate predictive alpha. It first screens for funds with enough data and positive performance persistence, then compares candidates pair by pair. A…
The document presents two strategies as possible low-cost defenses against sharp equity market declines. Time-series momentum is framed mainly for futures: measure each instrument’s return over a lookback period, take a long position after gains or a short…
This note presents a Chinese stock screen for companies with a daily high-to-low range of at least one percent, market value no greater than 10 billion yuan, positive earnings, and a positive price-to-earnings ratio. The accompanying formula further caps the…
This document outlines an equity screen for metaverse-related stocks with positive institutional activity and circulating market value above 10 billion yuan. It argues that the sector may offer growth exposure, institutional flows may provide a useful…
The document outlines a China A-share stock screen combining a daily amplitude threshold, membership in a robotics concept group, a circulating market-cap ceiling, and positive valuation ratios below specified upper bounds. It describes the screen as…
This weekly report summarizes several equity factors, including liquidity, momentum, price-volume, growth, and profitability. It says factor performance was generally subdued with a modest overall decline, while momentum was slightly positive and growth had…
The document outlines a Chinese A-share stock screen based mainly on turnover and share price. It selects stocks with turnover between 3% and 12%, excludes Beijing-listed shares, and describes a price near 18.5 yuan; its example formula instead uses a range…
The document summarizes research on 97 factors reported in finance, accounting, and economics journals. It compares long-short portfolio returns across the original sample, a later out-of-sample period, and the period after publication to examine whether…
This overview surveys approaches to combining strategies and allocating capital across assets. It traces the progression from fixed mixes and mean-variance optimization to Black-Litterman allocations that incorporate investor views, endowment-style exposure…
This research report proposes splitting a stock’s recent return history according to average trade size. For each stock, it ranks the past N trading days by daily turnover divided by trade count, sums returns separately for the higher and lower halves, and…
This proposed screen targets Chinese equities using price amplitude above a threshold, a nonempty indicator for outstanding convertible bonds, and a dividend ratio above a stated cutoff for 2019. The accompanying explanation interprets price movement as a…
The document describes a Chinese A-share screening rule that selects stocks associated with the metaverse theme, requires a positive institutional-flow indicator, and excludes securities with specified Beijing-related code prefixes. It provides both a…
This Chinese equity screening strategy combines trading activity with a simple revenue-growth condition. It selects stocks with turnover between 3% and 12%, an outside-to-inside trading volume ratio above 1.3, and 2021 revenue more than 1.1 times 2018…
This brief report summary proposes a two-stage equity-selection process. A fundamental model first creates a preliminary stock universe, and a deep learning model then screens that universe again. The stated aim is to improve excess returns while keeping…
This report describes a Chinese equity-selection process built from daily price and volume data. Genetic programming generates candidate factors: Rank IC is used to find factors with linear predictive relationships, while mutual information targets nonlinear…
This practitioner report reviews DeepAlpha, a neural model used to rank stocks from factor inputs, and compares it with other approaches. The author says a simple fully connected network appeared competitive with decision-tree and linear models, and…
This overview catalogs machine learning approaches that can be applied to quantitative investing, grouping them into tree models, linear models, unsupervised methods, deep learning, ensembles, and online learning. It links model families to tasks such as…
The document summarizes research on converting stock factors with strong negative-return associations into a long-only selection process. Because short selling is limited in the A-share market, it describes reversing the use of such signals: remove stocks…
This research summary examines how stock-selection factors with strong short-side performance might still be useful in A-shares, where short selling is constrained. For a single factor, it proposes excluding stocks with the undesirable factor characteristics…
This stock selection screen targets robot-themed A-shares with a floating market value below 10 billion yuan and turnover between 3% and 12%, while excluding Beijing-listed stocks. The accompanying example also removes names marked as special treatment,…
This report describes a monthly stock selection model that uses a random forest, an ensemble of decision trees trained with bagging, to estimate each stock’s probability of rising in the next period. Its workflow covers feature and label preparation,…