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 report proposes attributing bond fund returns from net asset value histories, addressing the limited holdings data available for many fixed income funds, especially private funds. Its Campisi-inspired regression model uses five risk factors representing…
This review examines whether Amihud’s ILLIQ measure captures information about stock illiquidity beyond IDVOL, the average inverse of trading volume. It describes a decomposition that isolates a residual component tied to the relationship between absolute…
The document presents a framework for assessing broad trends in China’s A-share market using three factors: corporate earnings, liquidity, and valuation. Strong or weak earnings and liquidity together are associated with bullish or bearish conditions, while…
This page collects questions for a quantitative trading discussion rather than supplying answers. Topics include the Sharpe ratio, overfitting and the gap between backtests and live results; outlier treatment and avoiding look-ahead bias when building…
The proposed screen selects stocks associated with the metaverse theme, requires a positive institutional-flow measure, and compares reported revenue across two years. The stated revenue test is that the later year’s revenue exceeds the earlier year’s by a…
The document presents a BigQuant stock-selection workflow and a ParserException raised while compiling its factor expression. The intended score adds ranked profitability, leverage, revenue growth, and price-to-book factors, along with a rolling turnover…
This document summarizes a factor-investing handbook that evaluates profitability and earnings-quality factors. For profitability, it reports examining fourteen factors, plus two additional measures based on EBIT: return on assets and return on invested…
This BigQuant example builds a monthly Chinese equity portfolio from a screened stock universe. It ranks eligible stocks using dividend yield and year-over-year net profit growth, then selects 50 holdings and assigns equal weights. The workflow includes…
This introduction presents dividend yield as a stock selection measure for investors seeking income and long-term value. It defines the measure as dividends relative to the share purchase price and argues that a consistently high yield can make a stock more…
This note outlines an A-share stock screen using a turnover range of 3% to 12%, a market capitalization below 10 billion yuan, and a condition that the company is not loss-making. Eligible stocks are then meant to be ranked by individual-stock heat, with the…
This Chinese-language post outlines a stock screen for companies in the metaverse theme whose prior-day price is above the 250-day moving average and whose price-to-earnings ratio is positive. The stated rationale combines a thematic category, a long-term…
This note describes a Chinese equity screen that combines membership in a metaverse-related theme, positive net buying by major participants during the auction, and a dividend payout ratio above 25% for 2019. The proposed logic mixes a market-theme filter…
This Chinese equity screen combines a daily price range greater than 1%, turnover between 2% and 9%, and year-over-year growth in net profit attributable to parent-company shareholders above 20% and no more than 100%. The intended logic is to combine trading…
This report compares six ways to combine equity factors: equal weighting, historical factor returns, historical Rank IC, maximum IC, maximum IC_IR, and principal component analysis. It applies the methods within six factor groups in a Chinese A-share…
This stock-selection screen combines three conditions: daily amplitude greater than one, positive institutional flow, and a circulating share count no higher than 5.5 billion. The article gives example implementations in indicator-formula and Python styles.…
The note defines quantitative investing as turning data and trading ideas into factors that guide investment decisions. It presents this as a systematic way to apply investment thinking, with a wider range of methods than traditional discretionary trading.…
The stock screen combines membership in the metaverse theme, a price condition relative to a long-term moving average, and a 2019 dividend ratio above 25%. The article presents the theme as a way to target a popular sector, the moving-average comparison as a…
This Chinese-language post describes a stock screen that combines metaverse-sector membership, a positive institutional-flow measure, and circulating market value between 5 and 10 billion yuan. It presents the criteria as a way to find moderately sized…
This meetup Q&A covers several practical quantitative trading topics. It explains rolling training as repeatedly updating a model using a moving or expanding historical window, and distinguishes incremental updates from retraining on the full dataset. It…
This research summary tests valuation factors for equity industry allocation. It examines five conventional measures, including earnings, book, sales, and operating cash flow multiples, and reports that standalone valuation rankings struggled to beat an…
This Chinese-language seminar summary frames AI adoption as a response to falling asset-management margins despite growing assets under management. It sketches a progression from adjusting active portfolios using factor analysis, to building long-short…
This screening idea selects Chinese A-share stocks associated with the metaverse, limits their circulating share count to no more than 5.5 billion, and ranks the remaining names by recent fund inflows. The suggested flow measure is net capital inflow over…
This report proposes diversifying portfolios across market, style, and strategy risk premia rather than allocating only among broad asset classes. It uses representative global equity, bond, and commodity indices to construct long-only strategies reflecting…
The document describes a Chinese equity screen that selects stocks with turnover rates from 3% to 12%, positive ten-day gains below 35%, and a positive price-to-earnings ratio. Its accompanying Python example outlines additional universe filters, including…