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.
5,922 documents
The document summarizes a study that proposes a public-information reliance (RPI) measure as a way to assess active equity fund managers. It estimates how much changes in a fund’s stock holdings can be explained by changes in sell-side analyst…
This strategy uses a four-agent workflow to select among a fixed universe of large US stocks. A research agent ranks the stocks using recent prices, trends, and news. Bull and bear agents then independently argue for and against the candidates, and a trading…
This article argues that asset prices and expected returns can be understood by modeling who demands assets, rather than focusing only on how company characteristics predict returns. With supply often relatively stable, changes in investor demand can move…
This article argues that asset prices and expected returns can be understood by modeling who demands assets, rather than focusing only on how company characteristics predict returns. With supply often relatively stable, changes in investor demand can move…
This research note describes a way to monitor changes in fund holdings by first classifying sample funds by industry or investment style, then estimating position changes within those groups. Industry classification draws on both fund mandates and reported…
The article summarizes research on a mismatch between active equity funds’ management capacity and the assets they attract. It argues that investors often mistake returns associated with common factor exposures for evidence of manager skill. Funds with…
This document summarizes an analysis of China Europe Fund manager Zhou Yingbo and the fund China Europe Times Pioneer A. It reports the fund’s historical returns and drawdown in relation to its benchmark and peer group, and cites a Treynor-Mazuy model…
The report presents a way to speed up portfolio optimization while retaining a covariance estimate built with statistical shrinkage. It starts with a linearly shrunk covariance matrix, decomposes it spectrally, keeps the components associated with the…
This overview presents a quantitative trading system as a chain of research, historical testing, execution, and risk management. It distinguishes mean-reversion strategies from trend or momentum strategies, and notes that trading frequency affects the…
This report compares methods for forecasting asset return covariance, a key input to minimum-variance and risk-budget portfolios. It evaluates historical and shrinkage covariance estimates, exponentially weighted moving averages, and multivariate GARCH…
This brief introduces two research papers on asset allocation and mutual fund performance prediction. The first examines statistical problems in forecasting long-term returns, including estimation errors and misinterpretation of model parameters. It reports…
This research summary examines stock and industry characteristics associated with long term performance across Chinese A-share bull and bear markets. It argues that revenue growth is especially relevant in rising markets, while profitability and valuation…
This document explains how a futures trading system uses several instrument sets: the full catalog, instruments sampled for price data, instruments with adjusted prices, and the smaller sets used in simulation or production backtests. It describes…
The document describes a qualitative and quantitative approach to sector rotation based on economic cycles. It treats commodity prices, represented by the CRB spot index, as a timely gauge of the real economy, and links long, medium, and short cycles with…
This report surveys quantitative enhancement of Chinese equity indices, aiming to outperform a benchmark while controlling tracking error. It places multi-factor models at the center of the approach: estimate returns and risks through factor exposures, then…
The document proposes a holdings-based labeling system for domestic active equity funds, addressing the risk that fund names and stated mandates can misrepresent actual exposures and that theme funds may drift across industries. It assigns each stock one…
This tutorial walks through assembling a quantitative equity research workflow with Qlib. It covers retrieving and inspecting market data, interpreting adjusted prices, working with dynamic universes and point-in-time fundamentals, and constructing features.…
This report illustrates the Black–Litterman portfolio framework with a hypothetical industry-allocation exercise. It assumes an investor forming a 2020 allocation at the end of 2019, then combines four subjective views with market-implied equilibrium returns…
This strategy selects China A-shares by circulating market capitalization, removes suspended and specially treated stocks, and holds the smallest-ranked names in equal weights. The described implementation targets 50 stocks and rebalances on the first…
This BigQuant forum fragment shows an initialization routine for a stock strategy. It sets the trading universe to the constituents of the CSI 300 and assigns an initial capital value through the strategy context. The snippet describes the default as one…
This overview explains a supervised learning workflow for ranking stocks by their expected future returns. It introduces features, a fitted function, model parameters, and a loss function, then describes labeling return targets, splitting observations into…
The post asks whether a VeighNa backtest can run several instruments against one shared pool of capital instead of assigning starting capital separately to each instrument. The question reflects a practical concern: separate allocations may not represent how…
This BigQuant assignment submission describes a stock strategy with two adjustable parameters and a target of holding 50 stocks. The author says the implementation adds filters for non-ST stocks, non-suspended shares, and excludes Beijing Stock Exchange…
The article describes a framework for identifying crowded sectors and estimating whether crowding may be building or unwinding. It measures asset clustering from principal components of sector return covariances, using the concentration of explained variance…