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 stock screen selects Chinese A-shares whose daily high-low range exceeds a threshold, excludes Beijing-listed shares, and requires the 20-day moving average to be above the 120-day average. The longer-term trend filter is paired with a short-term…
This Chinese-language post outlines a Shenzhen main-board stock screen that combines price activity, trading measures, and valuation. It selects stocks with amplitude above 1 and a ratio formed from the prior day’s turnover rate and the current auction…
This study applies TabNet, a neural network architecture for tabular data, to A-share stock selection. It outlines TabNet's encoder structure, in which attentive masks select features at successive steps, and explains the motivation for combining neural…
The article describes a Chinese-equity screen for metaverse-related stocks. It selects companies whose prior-day price is above the 250-day moving average, whose market capitalization is below the stated limit, and that are not loss-making. It includes…
This Chinese equity research summary reports tests of more than two hundred money-flow factors, built from a set of underlying measures and derived variants. It highlights small-order inflow amount, defined by buy executions from orders below a stated value…
This document describes a Chinese A-share stock screen combining a 14-period RSI below 65, at least two limit-up days within 500 days, and 2021 revenue more than 1.1 times 2018 revenue. Its Python example adds further filters for positive earnings per share…
This research summary proposes measuring stock valuation through time as well as across stocks. It separates a valuation series into a longer-run trend and a short-term deviation, then uses an error-correction model to define deviation factors from the…
This article outlines a machine-learning stock-selection experiment using TabNet and 98 price-and-volume factors. It trains the model on data from 2010 through 2018, then applies its predictions in a backtest covering 2018 through September 2021. The…
This research summary explains a method for separating a mutual fund’s alpha into stock-selection alpha and weighting alpha. It estimates selection skill by reweighting a fund’s holdings by market capitalization, then defines weighting alpha as the…
This Chinese-language summary explains factor investing as constructing a basket of equities with chosen factor exposures. It outlines several implementation routes, including active exposure to rewarded styles, index-based exposure, alternative weighting…
The document proposes an intraday factor intended to capture the distance between volume-weighted average traded price and reference prices. It identifies the daily VWAP, an average midpoint derived from the high and low, an average quote midpoint from the…
The post reports two errors encountered while analyzing the high-frequency factor hf_volume_diff_large_order: the software formed only four groups when five were requested, and it reported factor-value coverage below the minimum threshold. The author says…
This post presents a Chinese equity screening rule that combines a recent increase in reported fund allocation with a bounded daily price change and a rising 30-day average. It interprets the fund measure as evidence of buying interest, the price-change band…
This note describes a stock risk factor based on the Sharpe ratio over a 20-day period. It defines the measure as annualized stock return minus a risk-free rate, divided by the stock return standard deviation. The stated risk-free rate is 0.04. The factor is…
This research explores whether intraday price and volume relationships can improve stock selection. It builds three signals from price–volume correlation: its average level, its stability over time, and its trend. The average signal adjusts conventional…
This article turns a value investing approach associated with Charles Brandes into a rules-based stock selection strategy adapted for China. It screens for companies with moderate debt and valuation measures below specified market-relative limits, also…
This report examines institutional investors’ holdings and style preferences, focusing on Chinese equities and summarizing US institutional ownership for context. It outlines four possible data sources for monitoring institutions: macro indicators, trading…
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 review summarizes research on whether anchoring to a stock’s 52-week high helps explain why prices respond slowly to news about economically connected firms. Its central hypothesis is that investors underreact to good news about a customer when a…
This Chinese-language strategy note surveys ways to connect macro conditions, company fundamentals, valuation, and sector performance. It describes a credit and earnings framework for market regimes, a rolling stock-bond yield spread as a relative valuation…
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 proposed stock screen combines three filters: MACD above its zero line, a ratio of external to internal trading volume above a stated threshold, and a dividend payout measure above a stated level for 2019. The accompanying discussion treats MACD as a…
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…