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 brief note lists four candidate low-frequency factors based on daily price behavior over a rolling window: the close’s relative position or spread within the period, the relative index of the period high, the relative index of the period low, and the…
This proposed equity screen combines capital-flow strength with a robotics theme, a small free-float market capitalization, and a historical dividend payout ratio above a stated threshold. Its rationale is that strong flows may indicate investor interest,…
The post describes a Chinese equity screen that ranks Shenzhen Main Board stocks by capital-flow strength and filters for price-to-earnings ratios from 0 to 29.01 and price-to-book ratios from 0 to 3.11. It presents the valuation bands as a way to find…
This A-share screening proposal combines a technical condition with two company filters: RSI below 65, year-over-year growth in net profit attributable to the parent company above 20% and no more than 100%, and more than one year since listing. It frames the…
This strategy forms a quality score from four accounting measures: accruals, cash flow relative to earnings, debt relative to assets, and return on equity. It favors firms with lower accruals and leverage and higher cash flow and profitability. Stocks are…
This stock screening post selects companies with daily turnover between 3% and 12%, circulating share float at or below 5.5 billion shares, and a positive price-to-earnings ratio. The stated rationale is to find actively traded stocks with a relatively…
The proposed stock screen combines a technical condition with valuation and dividend criteria: select shares with RSI below 65, positive price-to-earnings ratios, and a dividend payout ratio above 25% for 2019. The accompanying rationale treats the RSI…
The article describes a risk-model framework for evaluating fund managers beyond headline returns, Sharpe ratio, or drawdown. It decomposes active returns into factor and stock-specific contributions, and examines risk contributions from a fund’s style…
This stock-selection proposal combines a price-amplitude threshold, a KDJ condition with K below 20, a market-value ceiling of 10 billion yuan, and a requirement for positive profits. The rationale is to find smaller companies with substantial price movement…
This stock screen combines a daily price-range condition, a weekly MACD condition, and a profitability filter. It seeks shares with an amplitude above 1, weekly MACD above zero, and return on equity greater than 15% in each of the previous five years. The…
This article collects practical lessons from building supervised machine learning strategies for stock selection. It recommends keeping training and test data separate and splitting financial time series chronologically, since random splits can mix market…
This overview proposes using machine learning to support value investing by combining financial statements, market data, and macroeconomic information. It lists a broad set of candidate measures, spanning performance and risk statistics, valuation and…
This report examines whether updating fundamental signals more frequently can improve stock selection during China’s annual report season. It argues that factor signals decay over time, but more frequent rebalancing offers limited benefit for fundamental…
This Chinese-language article outlines a stock screen combining a 14-period RSI below 65, three consecutive bearish candles, and return on equity above 15% across five years. The stated aim is to find stocks with recent technical weakness alongside a record…
This article describes a Chinese equity screening rule that combines a daily high–low range of at least one unit, return on equity above 15% in each of five years, and market capitalization of at least 200 million. It presents the criteria as a way to…
This Chinese-language research report outlines an equity index evaluation framework based on Barra-style risk factors. It proposes decomposing index returns into market, industry, and style contributions, including size, valuation, and volatility, then using…
This study compares three futures signals: cumulative return over a lookback window, an average based on cumulative daily steps, and a trend measure incorporating Garman–Klass volatility. It reports information-coefficient statistics, groups contracts into…
This research summary describes a multi-factor tracking framework built around the pricing logic of factors. It examines historical returns for technical, expectation-based, and financial factors using a modified cross-sectional regression approach. To…
This Chinese stock screen combines a daily price-range condition, a short-term trend filter, and valuation limits. It seeks Shenzhen Main Board stocks with amplitude above 1, a price above the five-day moving average, a price-to-earnings ratio between 0 and…
This Chinese-language post proposes screening stocks using three conditions: intraday amplitude above a threshold, a high dividend measure for 2019, and a product involving the day's price change and large-order net activity above a threshold. It frames the…
The document surveys a platform’s patent database and describes how patent characteristics are turned into equity-selection factors. It groups patent information by patent type, application or grant status, and measures such as counts, claims, citations,…
This research summary examines ways to improve equity momentum signals in China’s A-share market, where the authors report that reversal has historically been stronger than momentum. It compares conventional momentum with four adaptations: using moving…
This note describes an A-share stock screen combining a 14-period RSI below 65, a rising MACD signal line (DEA), market capitalization below 10 billion yuan, and no reported loss in the prior fiscal year. Its rationale is to pair modest momentum conditions…
The article compares a StockRanker stock-selection strategy with a Deepalpha deep neural network using the same 17 short-term factors, training data, and backtest setup. The strategy selects the top-ranked A-share stock each trading day and sells it the…