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 introductory article presents ten practical points about machine learning for non-specialists. It emphasizes that machine learning finds patterns in data, so data quality, accurate labels, representative samples, and careful transformations often matter…
The article explains how a long-only smart beta portfolio can combine equity factors to seek steadier returns than relying on one factor. It highlights value, low beta, profitability, investment, momentum, and size, arguing that their different return…
This report evaluates whether changes in listed companies’ employee compensation can help distinguish future stock returns. It compares growth in compensation payable, which represents amounts owed, with growth in compensation already paid, and also…
The post asks how to define groups for a learning-to-rank model in a stock selection setting. In ranking algorithms, each group contains observations that should be ranked against one another; the author proposes grouping stock samples by trading date so…
This convertible-bond research note describes a relative-value approach based on the embedded option. It first compares implied volatility with the underlying stock’s historical volatility: relatively low implied volatility is treated as evidence that the…
FactorVAE is described as a probabilistic dynamic factor model that applies a variational autoencoder to noisy market data to forecast cross-sectional stock returns. In training, an encoder-decoder uses future returns to identify posterior factors that…
This introductory article explains Matplotlib’s role in Python data visualization and outlines the Figure and Axes objects, along with its state-based pyplot and object-oriented interfaces. It notes that the library supports chart types such as lines,…
The report develops stock selection factors from abnormal operating cash flow as proxies for real earnings management. Its premise is that firms can alter real business activity to make reported performance look stronger, potentially harming future…
This research summary discusses how asset prices can reflect economic expectations before official data is released. It proposes comparing year-over-year changes in a basket of asset prices with economic indicators, and comparing asset-price changes with…
This trading exercise translates a discretionary method into explicit stock-selection rules. A candidate must close above its five-day moving average for three consecutive days and remain within ten percent above its lowest price over the previous twenty…
The report develops stock-selection factors from one-minute Chinese equity data, adding transaction counts to price and volume inputs. It examines average trade value and the direction of large-trade activity, then evaluates these signals both alone and…
This platform guide distinguishes raw financial statement records from processed point-in-time (PIT) data and explains why the distinction matters in quantitative investing. Raw records are organized by instrument, report date, publication date, and change…
This brief Chinese-language forum exchange concerns whether a user can retrieve feature importance from a random forest in a visual quantitative-strategy platform. The reply identifies the random forest module and suggests creating a visual strategy template…
This monthly review compares equity factors using information coefficients (IC) and long-short returns. It reports that growth and turnover factors were relatively consistent over the preceding three months, while profitability and reversal results varied;…
This research summary studies whether lagged returns from related industries can help predict industry returns for sector rotation. It motivates the question by noting that such predictability would not be expected in an ideal frictionless market, while real…
This research summary examines low-volatility investing in overseas markets and mainland Chinese equities. It presents the low-volatility anomaly as a source of long-run risk-adjusted performance, with a behavioral explanation: speculative demand and…
This report summary describes a Barra-style risk model for Hong Kong stocks, built to study factor returns and portfolio exposures. Its data foundation combines security details, prices, trading activity, Hang Seng industry classifications, valuation…
The article outlines common aims of algorithmic execution: splitting a parent order to follow market volume, reducing market impact, and concealing trading intent. It also presents automation as a way to improve order-entry efficiency and reduce manual…
This article presents a revised dividend-factor stock selection strategy for Chinese equities and a simpler equal-weight alternative using dividend-themed ETFs. The V2 stock strategy expands the portfolio from 8 to 20 holdings, rebalances monthly instead of…
This note proposes screening Chinese equities associated with the metaverse theme, requiring a positive institutional-flow measure and a weekly crossover involving a 30-week reference. The intended rationale is to combine thematic exposure, evidence of…
This overview explains Chinese depositary receipts (CDRs) as securities traded domestically that represent rights in foreign shares through a depositary institution. It summarizes the historical pilot eligibility criteria described in the document, says…
This forum post describes a reproducibility question in a quantitative modeling workflow. The author sets a learning-rate parameter to the same value in the model and in a hyperparameter-search module, and configures the search grid to contain only that…
The document explains sliced inverse regression (SIR) as a supervised dimension-reduction method for financial prediction. Unlike principal component analysis, which summarizes variation in predictors without using the target, SIR uses the forecast variable…
This overview surveys models used to relate asset values or expected returns to risk and other inputs. It describes CAPM as linking expected return to the risk-free rate and market exposure, then introduces multifactor models such as Fama–French, which add…