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
This report describes ways to study institutional investors’ market footprint and preferences in Chinese equities. Because many institutions do not disclose complete holdings, it proposes classifying shareholder accounts from listed companies’ public…
This report explains how to isolate a desired equity style factor while controlling exposure to other factors. It contrasts full replication, which reproduces modeled pure-factor returns but does not control ex ante risk, with constrained mean-variance…
This research summary presents factor timing as a risk-control problem rather than solely as a return-forecasting problem. It proposes adjusting the upper and lower bounds on factor exposures in a portfolio, with those limits informed by factor risk,…
This overview introduces the Fama-French five-factor asset pricing model as an extension of the three-factor model. It describes the market risk premium, size, value, profitability, and investment-style factors, and explains each as a return spread between…
This page serves as a source-code index for a developing multicurrency Expert Advisor series. The series explores combining multiple trading strategies inside one program so they can operate on the same account. Its planned design moves market order handling…
The document outlines a portfolio construction method for fund-of-funds portfolios targeting absolute returns. It combines Black-Litterman expected-return estimates, which blend market equilibrium returns with investors’ absolute or relative views according…
The document explains the Sharpe ratio as portfolio excess return over a risk-free rate divided by portfolio return volatility. It identifies the portfolio return, risk-free rate, and standard deviation as the inputs, with short-term government debt offered…
This research summary compares 17 machine learning approaches with conventional linear methods for predicting stock excess returns from 51 alpha factors. It describes splitting the prediction into two parts: an AR(1) forecast for return dispersion and a…
The document describes a monthly equity strategy based on market capitalization. At each month end, it ranks eligible stocks from smallest to largest by market value and buys the ten smallest. The portfolio holds those selections for the following month,…
This review summarizes research applying machine learning to monthly and annual returns for Chinese A-shares. The study combines stock characteristics, macroeconomic variables, and industry indicators, then compares linear models, tree methods, and neural…
This document is a brief synopsis of a research report on strategic asset allocation and fund-of-funds investing. It says the report covers the research context, an asset-allocation framework, calculation models, and empirical strategy analysis. The subject…
This document is a brief synopsis of a research report on strategic asset allocation and fund-of-funds investing. It says the report covers the research context, an asset-allocation framework, calculation models, and empirical strategy analysis. The subject…
This assignment analyzes how a stock selection strategy’s exposure to market styles changes over time, then outlines a framework for rotating among factor strategies. The initial analysis uses a small-capitalization selection factor and compares strategy…
This document describes a cross-sectional momentum strategy for equity mutual funds. It first limits the universe to no-load funds, then ranks eligible funds by their trailing six-month returns. The portfolio holds the top decile, equally weighted, and…
This Chinese stock selection post proposes combining a positive MACD reading with three consecutive down days and a 2021 time filter. Its explanation frames the setup as pairing a longer-term upward indication with recent weakness, but the supplied code does…
This code reference presents several ways to measure dependence or distance between financial data vectors and matrices. It defines angular distance from Pearson correlation, plus absolute and squared variants that alter how negative or strong correlations…
This research summary considers how the CSI 300 might change if Chinese companies listed overseas returned to mainland China. It notes that trade tensions and tighter US oversight were associated with weaker performance of US-listed Chinese stocks relative…
The document introduces graph representations for modeling relationships among financial assets, arguing that stocks cannot be understood solely as independent observations. Graphs can encode different kinds of links, including sector membership, return…
This research summary examines an equal-weighted index drawn from the China Securities Index 500. It combines low valuation with expected earnings improvement, using analyst consensus expectations and a GARP-style approach to select mid-cap growth stocks.…
This research overview describes two broad approaches to absolute-return investing: combining equities and bonds, and hedging or taking exposures through derivatives. It lists stock-bond rebalancing, target-volatility, risk-parity, and risk-budgeting…
This overview presents QLib as an open-source quantitative research platform that supports data preparation, model training, portfolio construction, risk analysis, backtesting, and execution research. It notes support for supervised learning, market-dynamics…
This strategy ranks a fixed universe of ten large U.S. stocks by price momentum over roughly one trading year. On the first trading day of each month, it selects the three strongest performers, sells positions outside that group, and adjusts each selected…
This BigQuant forum post presents a SQL query intended to calculate sector-level and market-level returns, compound each series across a rolling window, and derive an information-ratio-like value. The author reports that the underlying daily inputs appear…
The document summarizes research on whether seasonal shifts in investor mood help explain recurring cross-sectional patterns in stock returns. It treats January, March, and Fridays as relatively optimistic periods, and September, October, and Mondays as…