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 explains how to read the balance sheet, income statement, and cash flow statement as connected views of a company’s resources, operating results, and cash movements. It distinguishes operating assets from investment assets, and describes how…
This research summary compares factor-based Smart Beta products with active quantitative and index-enhancement strategies. It reports that applying commonly used Chinese equity alpha factors to S&P 500 constituents produced little excess return before fees,…
The document raises a portfolio allocation question: when a strategy’s performance weakens and later recovers, allocations based on its current Sharpe ratio may react too slowly. It asks whether changes in Sharpe ratio or the slope of a strategy’s cumulative…
This report describes using Campisi and Brinson models to analyze the performance and investment styles of bond funds, with fund selection for fund-of-funds managers as the motivation. It notes that bond fund valuation and disclosure challenges make…
This market-timing weekly report combines a broad-market regime filter, short-term risk assessment, industry views, valuation, and suggested equity exposure. It classifies the market using the distance between 20-day and 120-day moving averages of a broad…
The document describes a crypto portfolio mode that allocates a reference-market balance across a configured set of coins. At startup or during scheduled checks, the system values holdings, compares their proportions with the index targets, and rebalances…
This broker report describes the Beijing Stock Exchange's role in financing smaller innovative companies and its relationship with China's other equity venues. It outlines the exchange's development from the New Third Board, its focus on advanced…
This document outlines a daily strategy for trading a set of assets using a cointegration vector estimated with the Johansen method on training data. It applies the vector to log prices to form a combined process, then sums its recent changes to determine…
The post raises a research question about obtaining daily exchange-rate data for several countries, including China, the United States, and Japan. Its author argues that broad equity-market timing should consider capital flows, distinguishing domestic flows…
This module describes an interactive dashboard for examining two-asset pairs through cointegration tests and mean-reversion models. Its analytics include Engle–Granger and Johansen portfolio construction, augmented Dickey–Fuller results, cointegration…
The discussion asks how to combine a traditional stock-selection strategy with a machine-learning ranker. The user has a screening method that produces too many candidates and wants to rank those stocks afterward. The post contrasts traditional strategies,…
This overview discusses how machine learning can address high-dimensional and nonlinear return-prediction problems that strain conventional econometric methods. It frames the challenge through empirical asset pricing, including factor models, a proliferation…
The document outlines a two-stage quantitative investing workflow: combine several signals into a single factor score, then optimize portfolio weights against a chosen objective. It names linear combination as one approach to factor synthesis and mentions an…
The document outlines ways to set the changing asset allocation, or glide path, of a target-date fund. One approach uses an investor’s human capital and forecasts of future asset returns to choose a path that maximizes investor utility. Another bases the…
This competition update reports diagnostics for the ten highest-ranked anonymous factors and tracks a CSI 1000 enhancement portfolio. Factor profiles include average Barra style exposures and sector-level average Rank ICs, calculated from cross-sectional…
The code describes a portfolio-wide risk overlay that scales all positions by a shared multiplier between zero and one. It computes separate multipliers from normal risk, volatility-shock risk, aggregate absolute risk, and leverage, then applies the lowest…
This study proposes adding daily risk monitoring to a traditional monthly rebalancing schedule for multi-factor equity portfolios. It tracks the portfolio’s exposure to the size factor during the month and rebalances on the next trading day when that…
The report describes an expanded Black-Litterman asset allocation model that combines subjective views with quantitative portfolio construction. It uses behavioral finance and historical trend information, including momentum and reversal effects, to form…
This Chinese market note proposes that China’s credit impulse may be turning upward and argues that it tends to lead A-share earnings growth. On that assumption, it recommends overweighting Chinese equities, favoring growth over value, and ranking…
This configuration describes a futures trading system that estimates forecasts from several exponentially weighted moving average crossover rules and a carry rule. The EWMAC rules pair faster and slower lookback periods, while the carry forecast uses…
This study compares conventional return labels with alternative labels based on information ratio and Calmar ratio for AI stock selection models. It argues that labels incorporating returns, volatility, and drawdowns across a period may convey more…
This research summary reviews portfolio optimization choices, including transaction-cost penalties, turnover and tracking-error limits, risk aversion, position bounds, style-factor exposure, and limits on the number of holdings. It explains that stricter…
This study reviews Chinese equity-oriented mixed funds over 2010–2020 and compares their performance with several domestic equity benchmarks across value- and growth-led market periods. It reports that the fund group’s relative results varied with market…
The document answers a platform question about restricting an A-share strategy’s eligible stocks to listings on the Shanghai and Shenzhen exchanges. It recommends adding an A-share stock-filter module to the prediction set, then configuring the listing-board…