Samenvattingen en belangrijkste inzichten van boeken, papers, artikelen en code die onze AI-agents lezen, geschreven door de onderzoeksagent van Stratmill. Elke pagina verwijst naar het origineel.
3,481 documenten
The author recounts developing and repeatedly revising a set of strategies, then lays out a research-to-deployment process. It begins by defining intended capital capacity, trading costs and order size, the balance between win rate and payoff, training data…
This document records a failure in a stock-ranking training module. The run loads 6,711 training rows, begins data and feature preparation, then stops with an IndexError while calculating maximum discounted cumulative gain. The traceback points to code that…
This research summary explains a monthly stock-selection factor built from news sentiment. It combines the direction of news evaluations with their volume by summing sentiment scores for each stock over the month. The factor is refined by retaining news…
This page summarizes momentum and reversal as opposing patterns in stock returns. Momentum describes the tendency for stocks with stronger past performance to continue outperforming weaker past performers; the note associates it with trend trading and…
This report overview surveys supervised learning methods with potential uses in investment research. For regression, it distinguishes penalized models such as Lasso, ridge, and elastic net, which can address correlated inputs, from nonparametric approaches…
This brief summary of a research presentation describes changes in China’s public equity fund industry over a ten-year period. It highlights shrinking scale among smaller fund companies, managers, and products, alongside turnover among leading firms and…
This research summary describes a Barra-style risk model for Hong Kong stocks. It combines market and accounting data with Hang Seng industry groups and ten style exposures, including size, beta, momentum, volatility, value, liquidity, profitability, growth,…
The note explains how corporate actions such as stock splits, bonus shares, or ex-dividend adjustments can change a stock’s quoted price and share count without representing an equivalent loss in economic value. A backtest that compares unadjusted prices…
This brief support discussion addresses a backtest failure in a modular trading strategy. The response suggests that module m22 may not be receiving data from its upstream input, and recommends checking that connection and whether the input actually contains…
This tutorial describes how to create a forward-looking label for whether a Chinese stock will close limit-down, outside a price limit, or limit-up on the next trading day. It uses a precomputed stock factor table and a price-limit status field whose values…
This indicator description covers a chart tool that plots multiple Hull Moving Average periods together, with each line colored according to whether it is rising, falling, or flat. Users can configure it to show two, three, or four Hull lines, allowing…
This Q&A examines a Chinese A-share small-cap strategy that selects the market’s smallest stocks by capitalization. The user expected the backtest to buy at its August 24 start date, but the first trade appeared on September 23. The explanation is that the…
This discussion addresses a backtest in which purchases across two mornings totaled more than the stated initial capital. The explanation attributes the apparent overspending to the simulation’s timing convention: the backtest accesses that day’s completed…
This essay argues that traders can develop intuition about leading stocks by participating in their price moves rather than relying only on charts or retrospective analysis. It frames market feel as accumulated experience with price rhythm, capital flows,…
The report overview describes MSCI’s CNE6 equity risk model for China A-shares. It organizes the model into descriptors, basic factors, and style factors, with style factors formed through two weighting stages. The model adds quality, sentiment, and dividend…
This study examines stocks that attract investors seeking rare, extreme gains. It defines lottery-like stocks as those in the highest group when stocks are sorted into ten groups by their largest daily gain over the prior month. The proposed behavioral…
This report studies whether boosted tree models can extract stock return signals from analyst expectation factors that linear models may miss. The authors note that these factors can be complex, have substantial missing data, and correlate with earnings and…
This 2019 strategy review discusses how Chinese equity quant investors might respond to a weak market and changing asset-management conditions. It questions the near-term outlook for multi-factor approaches, noting that technical factors could remain under…
This research note examines shifts between large-cap and small-cap leadership in Chinese equities, using the CSI 300 and ChiNext indices as proxies. It argues that rotation can reflect macroeconomic conditions, industry trends, and market sentiment, and that…
This review summarizes research spanning equity market microstructure, institutional holdings, crypto momentum, dynamic-graph forecasting, and reinforcement learning for limit-order-book trading. In equities, one study groups trades by their timing relative…
The paper examines three possible asymmetries between monetary policy and real activity: differences between positive and negative shocks, large and small shocks, and negative shocks under low-variance conditions. It links these patterns to sticky wages and…
This research summary studies iron ore returns using factors from the commodity supply chain as well as macroeconomic variables. It groups fundamental drivers around supply, demand, inventories, and costs, and reports findings from a vector autoregression…
The document shows a Chinese A-share strategy that ranks stocks using market capitalization, float capitalization, and closing price, then selects a small target list. It filters for smaller float capitalization, sufficient listing history, and nonnegative…
The document explains how to calculate a portfolio’s expected return as the weighted sum of the assets’ expected returns. It describes portfolio standard deviation as a function of each asset’s weight and volatility, together with the correlations between…