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.
3,481 documents
This post proposes a stock-selection screen using daily price amplitude, a rounded price shape, MACD above its zero line, and volume above its recent average. The stated rationale is that amplitude and the rounded pattern identify active stocks, while…
The article presents four warning signs that a rising equity market may be approaching a peak: an index making new highs while volume contracts, momentum indicators failing to confirm price highs, heavy volume accompanying a falling or sharply rejected…
The article compares three consumer activity measures for publicly traded companies: estimated in-store visits, traffic to company and related websites, and interest in brands measured through search and social activity. It forms growth proxies by comparing…
The document outlines six assumptions used to simplify asset pricing theory, especially the Capital Asset Pricing Model: competitive and efficient markets, no taxes or transaction costs, risk-averse investors, shared expectations, normally distributed…
The research note describes factor-mimicking portfolios (FMPs) as an alternative representation of alpha factors, with conversion between the two under a given stock covariance structure. It argues that linearly combining alpha signals corresponds to…
This short note introduces data preprocessing as a step that can help machine-learning and deep-learning estimators work with raw feature vectors. It points readers to scikit-learn’s preprocessing module, which provides transformation classes and functions…
This research summary examines whether Chinese listed companies’ earnings previews can add information to conventional equity factor models. Because previews arrive before earnings announcements and periodic financial reports, the report treats them as an…
This short BigQuant support exchange answers how to obtain an algorithm’s daily returns from backtest performance data. Its method reads the raw performance table, takes the cumulative algorithm period return, converts it to a growth index by adding one, and…
This research summary examines two responses to a prolonged value-factor drawdown: changing how the factor is constructed and forecasting when its returns may be stronger. It compares conventional cross-sectional valuation with time-series measures of a…
This assignment asks learners to build a trading strategy using the standard deviation of turnover rate as a stock selection factor. It also specifies two exit rules: liquidate positions when the broad market falls by 4% consecutively, and close an…
This Chinese-language research roundup summarizes three studies from overseas literature. The first presents a numerical simulation approach for approximating American option values, with potential application to pricing domestic convertible bonds. The…
The article contrasts discretionary stock trading with quantitative approaches that analyze large datasets for statistical relationships. It describes using price and volume alongside less conventional inputs, such as online search activity and social media,…
The document presents a discretionary chart-reading idea for stocks that rally sharply and then drift lower on shrinking volume and narrow price ranges. It interprets this pattern as possible position cleaning by a large market participant, arguing that slow…
This research applies macroeconomic momentum signals to active bond trading, aiming to forecast government bond yield direction as well as changes in term and credit spreads. It contrasts active strategies focused on capital gains with passive approaches…
The document introduces microstructure segmentation: using high-frequency price and volume data to divide trading into intervals with different trading characteristics. It argues that activity levels can correspond to different price-trend behavior and…
This 2020 investment analysis discusses the ChiNext index and Bosera's ETF tracking it. It argues that refinancing policy changes and the stock registration system could improve financing access for technology and growth companies. The index is described as…
This research summary introduces APM, a stock-selection factor built from differences between morning and afternoon price behavior in Chinese A-shares. Its premise is that informed traders are more active in the morning, making morning price moves a stronger…
This short forum exchange raises a practical concern about transparency in visual quantitative research tools. A user asks for fuller descriptions of how drag-and-drop modules calculate outputs, especially whether feature-generation steps first group data by…
This research note describes a daily equity alpha approach that uses supervised neural networks to generate many candidate factors from designed factor units. It then transforms the original inputs into weak, mutually orthogonal factors and weights them…
This study adapts SinGAN, a multiscale generative adversarial network trained on one sample, to generate financial return sequences. Its pyramid trains progressively across scales: coarse layers represent broad, low-frequency structure, while finer layers…
This report reviews equity factor performance, emphasizing a February 2018 snapshot and trends over a longer historical period. It compares factor information coefficients, which measure association with subsequent returns, and market neutral long-short…
This summary describes a single-stock strategy for Changchun High-Tech, a Chinese pharmaceutical company. It argues that steady fundamentals and stable analyst expectations for earnings growth create a valuation reference point. Using a price-to-earnings…
The document summarizes research that derives two liquidity factors from high-frequency order-book data, one associated with buy orders and one with sell orders. The factors are then aggregated to monthly frequency for use in stock selection. It reports that…
This edited seminar transcript surveys a Chinese quantitative research program on deep learning, then focuses on AlphaNet, a neural approach to stock factor discovery. It frames a multi-factor process as factor generation, factor combination, and portfolio…