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.
3,481 documents
This report summary introduces entropy pooling as a way to incorporate qualitative or quantitative views into a risk model by updating a probability distribution. It contrasts the approach with the Black–Litterman model, describing broader choices for risk…
The document summarizes research on weighting equity alpha factors through factor-mimicking portfolios (FMPs). It presents FMPs as an alternative representation of alpha signals under a stock covariance model, with linear combinations of factors…
The research note explains how alpha factors can be represented through factor-mimicking portfolios (FMPs) under a stock covariance model. It describes translating linear combinations of alpha factors into corresponding combinations of FMPs, then choosing…
This article outlines a directional strategy for Chinese equity index futures based on a schedule of daily long, short, or no-trade signals prepared in advance for the week. It describes opening an initial position around 10 a.m., adding to it around 2 p.m.…
This report summary describes fundamental implied returns, or FIR, as a measure of fundamental momentum intended to use more information than conventional measures. FIR accounts for lagged fundamental variables and their time trends, and can be constructed…
This overview presents fixed-income-plus investing as a way to seek steadier returns by combining bonds and equities. Its central portfolio argument is that historically low, often negative, correlations between Chinese stocks and bonds can let an…
The document summarizes research on using corporate earnings-call transcripts to predict subsequent stock-price movements. Earnings calls contain management discussion of financial performance and information that investors and analysts may use when making…
This research framework measures crowding in equity industries using price and trading activity. It groups candidate indicators into trend-oriented measures, including price deviation and moving-average dispersion; supplementary measures such as turnover,…
This study examines why publicly listed Asian companies disclose environmental, social, and governance information. Using panel data and regression models, it tests whether economic sustainability performance, profitability, leverage, and company size are…
The document proposes adding a simple API to retrieve selected strategy performance metrics over a specified date range. Examples include the Sharpe ratio, annualized return, benchmark return, and alpha. The request responds to a gap between an existing…
This stock-selection rule screens companies classified in the metaverse industry, requires the prior day’s actual turnover rate to fall between 3% and 28%, and looks for a weekly close crossing above its 30-week moving average. The rationale is that the…
The discussion explains that a supervised model learns from factors paired with labels, where a label defines the target. For stock selection, the target might be future returns over a chosen horizon. The model and label together determine what predictions…
This study examines how exposure to international trade relates to US stock returns. It uses industry transportation costs as a measure of globalization exposure, sorting stocks into portfolios and comparing their excess returns. The reported evidence…
The document answers whether a factor can be neutralized for both market capitalization and industry exposure. It recommends including market capitalization as a continuous explanatory variable and industry membership as dummy variables in the same…
This event study examines how Chinese A-share prices behave around formal earnings reports, especially when companies previously issued earnings forecasts. It compares reported results with forecast ranges and groups companies by forecast category or by the…
This report applies behavioral finance to equity factor research, using the disposition effect and prospect theory to explain how investors' risk preferences may differ depending on whether they hold gains or losses. It constructs a capital gains overhang…
This report compares groups of Chinese equity factors across industries, focusing on cash flow, valuation, size, momentum, liquidity, volatility, beta, and alpha. Its stated findings come from portfolio net value behavior, industry-level results, ranking…
This BigQuant forum post discusses how to define a factor for stocks closing above their historical maximum high. The example query groups earlier price records by instrument and name to calculate a prior high, then compares a later close with that value.…
This study describes a grouped-factor approach to combining quality, growth, and value signals for A-share stock selection. A quality composite based on enterprise value and return on equity ranks stocks and defines a filtered universe for further selection.…
This Chinese-language post outlines an equity screen for companies classified in the metaverse theme. It selects stocks whose actual turnover rate from two sessions earlier falls between 3% and 28%, and whose current control-related measure exceeds 21. The…
This research summary asks whether financial data vendors act as information intermediaries in equity markets. It focuses on FirstCall, a Thomson Reuters service, and whether the timing of its distribution of earnings information affects how investors…
This research note summarizes a study of downside volatility as a measure of portfolio risk and compares it with conventional volatility. It reports that the measures are generally closely related, but their relationship weakens during historically turbulent…
This 2020 research summary argues that the Shenzhen 100 could suit long-term allocation, describing it as a liquid large- and mid-cap index with both growth and blue-chip characteristics. It cites fundamentals reported at the time, including return on equity…
This Chinese-language knowledge post explains a mismatch in a sample trailing-stop calculation. The code obtains a holding duration by subtracting the position’s last sale date from the current date, which yields calendar days. It then passes that duration…