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 forum exchange addresses an error encountered when adding a code-list module to a factor analysis workflow. The reply explains that the code-list module returns a dictionary rather than a DataSource object, which is the type expected by the factor…
This post outlines an equity screen that combines a stated price-range condition, five consecutive years of return on equity above 15%, and a three-session limit-up pattern on the prior day. It presents the technical conditions as a way to find stocks with…
This article discusses the difficulties faced by quantitative index-enhancement funds after a period of rapid product launches and asset growth. It reports that some funds failed to generate positive alpha, lagged their benchmarks, or experienced declines in…
This brief BigQuant post reports a test combining rolling model training with LightGBM and an experimental factor described as “暗流涌动” (roughly, hidden currents). The author says the factor's standalone effect is not prominent, but it may contribute something…
This note proposes an equity factor that adds cross-sectional percentile ranks for year-over-year growth in recent net profit, the negative of a ten-period turnover sum, and the negative of total market capitalization. The intended interpretation is that…
This document describes an equity screen combining three conditions: a daily high-low range above one percent, a high ranking on a large-order net-flow measure, and revenue growth of more than ten percent between the referenced periods. It presents…
The note examines an attempted A-share stock-selection strategy that combined dividend yield and price-to-sales signals with equal scoring weights while retaining both screens. The author reports that the combined strategy backtested substantially worse than…
The document describes a Chinese stock screen combining beverage and alcohol industry classification with turnover between 3% and 12% and more than two limit-up sessions in a 10-day window. Its rationale is to pair moderate trading activity with recent price…
The document presents a Chinese stock selection screen combining trading activity, earnings growth, and a short-term price move. It selects stocks with turnover between 3% and 12%, year-over-year growth in net profit attributable to parent-company…
This research note organizes convertible-bond factors into three groups: factors from the underlying stock, factors describing the bond-stock relationship, and the convertible bond’s own price and volume behavior. It reports that earnings yield and…
This research overview presents a framework for studying high-frequency equity factors, emphasizing both incremental information and the economic logic behind observed returns. It uses linear models, factor statistics, and portfolio backtests to assess a…
This report extends a factor attribution framework for equity funds from quantitative strategies to actively managed funds. It argues that the factor set should reflect the fund’s likely investment approach and risk exposures. Active managers may share…
The article describes training BigQuant’s StockRanker machine-learning model using an overnight-return factor, following earlier factor research that links this signal to informed trading. Training uses all A-share stocks from 2013 through the start of 2021.…
This research summary examines how market-cap style relates to trader behavior and trading activity in Chinese equities. It argues that shifts between large- and small-cap stocks involve changes in capital flows, and that market capitalization is closely…
The report summary describes a strategy that varies fundamental factor exposures according to recurring seasons around company financial reports. It groups the calendar into four windows: annual-report disclosure, post-report performance trading,…
The document introduces an index-enhancement research report about building longer-horizon price and volume factors from mid- and high-frequency data. Its central motivation is a mismatch between data frequency and forecast horizon: as sampling frequency…
This Chinese A-share selection rule combines a technical condition with a company earnings filter. It selects stocks with RSI below 65, year-over-year growth in net profit attributable to parent-company shareholders above 20% and at most 100%, and a rising…
This overview explains how environmental, social, and governance considerations can enter investment decisions alongside conventional financial analysis. It surveys approaches including positive and negative screening, thematic allocation, impact investing,…
This research summary tests stock selection factors built from return and trading-volume data, using moments from the first through fourth orders. It places these measures in a broader taxonomy: factors can use one data dimension, combine dimensions at one…
This Chinese stock-screening note describes selecting equities with RSI below 65, a circulating market value between 5 billion and 10 billion yuan, and a closing price above the Bollinger middle band but below the upper band. The article presents the range…
The discussion addresses how to combine daily observations with data from a longer period, such as the previous month, when constructing factors. Its suggested workflow is to convert daily market data into weekly or monthly bars with Python resampling, then…
This brief discussion shows how to retrieve financial factor data through a data-access module using an SQL query. Its example selects dates, instrument identifiers, and a trailing-twelve-month operating-profit growth field from a Chinese stock…
This short strategy note describes an iterative experiment in which initial parameter adjustments appeared unpromising. The author then added a weight based on closing-price ranking and reports that the resulting Sharpe ratio exceeded 1.5. The takeaway is…
The document outlines a dynamic scenario alpha model (DCAM), which layers scenario classification over a conventional static alpha model. It argues that scenarios are useful when their subdivisions correspond to meaningfully different drivers or importance…