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 overview introduces three types of company data associated with FactSet: a six-level product and service based industry classification, company-to-company supply-chain relationships, and geographic revenue exposure. The classification is presented as a…
This weekly report describes two quantitative equity approaches. The first builds on holdings of successful active funds, then applies quantitative selection to create an enhanced portfolio benchmarked against active equity funds rather than broad market…
This note explains beta as a measure of an asset’s market risk exposure and introduces the Fama–French three-factor model as a way to capture size and value style exposures. It describes how to quantify style performance with long-short portfolios: small-cap…
This research proposes a stock selection factor that estimates similarity between companies using ten financial measures spanning profitability, solvency, operating efficiency, and growth. After cross-sectional normalization, it uses cosine similarity to…
This China equity strategy ranks stocks cross-sectionally using circulating market capitalization and earnings per share, combining the rankings into a total score and sorting from low to high. It excludes special-treatment and suspended stocks, requires…
This Chinese equity screen selects main board stocks whose MACD is above zero, whose price has risen by more than 1% for the day, and whose stated 2019 dividend ratio exceeds 25%. The post gives indicator and Python examples and describes the conditions as a…
This Chinese equity screening proposal combines three conditions: price amplitude above a threshold, return on equity above 15% in each of the prior five years, and a shortening green histogram on a 15-minute MACD. The intended logic pairs a history of…
This review of research on equity factor investing explains why low unconditional correlations between factors may not provide protection when macroeconomic conditions deteriorate. It describes selecting fast-moving state variables that reflect expectations…
This document summarizes a 2021 presentation on tools for classifying industry-themed funds and building quantitative industry allocation strategies. Its proposed framework brings together macroeconomic drivers that may affect sector conditions, comparisons…
This stock-screening note proposes combining moving-average confluence, a specific closing-price condition, and a high dividend ratio. It describes counting available observations in a short rolling window, checking whether the close equals the stated price,…
This note describes a daily liquidity signal built from intraday order book snapshots. It sums bid and ask volumes across the first five levels, then measures their normalized imbalance as the difference divided by the total. Separately, it calculates…
The document proposes a stock screen combining daily price range, positive price-to-earnings ratio, and revenue growth. It selects stocks whose high-to-low range exceeds one percent of the previous close, whose P/E is positive, and whose revenue in 2021 is…
The document briefly introduces a multi-factor investment platform, focusing on how to estimate stock and portfolio factor exposures. Stock exposures are derived from database values such as price-to-earnings and price-to-book ratios, then cleaned and…
This weekly competition report presents diagnostic summaries for the ten highest-ranked submitted factors and tracks a China 1000 index-enhancement portfolio. Factor diagnostics include average exposures to BARRA style factors and sector-level average Rank…
This introductory note describes quantitative investing as turning research, asset selection, timing, and position decisions into explicit rules that can be tested and executed by a computer. It distinguishes cross-sectional stock selection, which compares…
This stock-selection example combines a historical dividend payout filter with a liquidity screen. It selects shares whose 2019 dividend payout ratio exceeded 25% and whose prior-day turnover was above 60 million, then ranks candidates by a stated measure of…
This Chinese equity screening idea combines a daily price range filter, a history of limit-up moves, and year-over-year earnings-per-share growth. It seeks stocks with an amplitude above one percent, at least two qualifying limit-up events within the…
This document outlines an equity screen combining a daily high-low range threshold, five consecutive years of return on equity above 15%, and institutional ownership. Its refined rule translates the institutional buying idea into a minimum holding of 100,000…
This report reviews the relative performance of Chinese quantitative equity funds and considers what rising assets under management mean for strategy design. It attributes the period’s advantage for index-enhanced and quantitative long products largely to…
The proposed screen selects stocks with turnover between 3% and 12%, an external-to-internal trading volume ratio above 1.3, and return on equity above 15% across five consecutive years. It combines a liquidity and trading-activity filter with a measure of…
This short-term A-share screen combines three conditions: RSI below 65, parent-attributable net profit growth above 20% and no more than 100%, and a positive daily MACD reading. The document presents RSI as a way to find stocks that may have rebound…
This brief introduction contrasts linear regression of factor data with decision trees. Its central point is that market returns may not relate to factors in a simple linear way, so a tree-based method is worth considering when studying how factors…
This report presents an A-share stock-selection framework that applies StockRanker, a gradient-boosted learning-to-rank model, to fundamental, financial, and trading factors. It tests 282 individual factors across short, medium, and long holding horizons,…
The document summarizes two ways to classify crowded equity holdings. For passive crowding, it first divides stocks into size groups using market-capitalization thresholds, then ranks them within each group by the rolling change in passive-fund ownership as…