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
This translated research review examines seven U.S. equity sentiment signals built from analyst revisions and surprises, short-selling data, news, options, consumer web content, corporate filings, and insider trades. It describes how the signals are…
This brief Chinese-language research note concerns a replication of a study on the micro-level sources of reversal effects in China’s A-share market. It says the replication lacked tick-by-tick data and therefore used minute-level data as an approximation.…
This research review describes a link between employee movement and subsequent stock performance. It summarizes a study that estimates company hiring and departures from employee résumé histories, then tests whether net outflows predict returns. In event…
This research summary describes a unified framework for explaining and testing generalized linear models in multi-factor stock selection. It compares how these models can combine factors into a composite signal, then evaluates the signal through grouped…
The tutorial explains how Brinson performance attribution separates a portfolio’s actual return from its benchmark return and decomposes active performance into timing, security selection, and an interaction component. The benchmark reflects planned asset…
The study summary describes a dynamic contextual alpha model designed to address a weakness in conventional multi-factor scoring: applying the same factor evaluation across the entire market. It groups A-share stocks by characteristics such as size,…
The document reports a BigQuant user’s problem: the visualization module was set to a latest strategy date of November 1, 2022, while the generated data appeared to begin on July 15, 2018. The response suggests checking the beta factor and points to a…
This article distinguishes overfitting from overtraining and explains why cross-validation does not, by itself, prevent a model from overfitting. Validation estimates predictive performance, including through out-of-sample data and averaged errors across…
This research summary examines how to estimate factor premia for return prediction when market conditions change. It notes that a fixed estimation window creates a trade-off: a long window can lag shifts in the market, while a short one can be dominated by…
This example combines three cross-sectional stock measures into a composite score: market capitalization rank, turnover rank, and the rank of the latest close-to-prior-close ratio. It filters out risk-warning stocks, recently listed stocks, certain listing…
This short platform discussion addresses whether a strategy initialization routine runs once in backtests but each day in simulated live trading. The response explains that live trading parameters are reloaded each trading day, so simulation follows that…
The document explains a basic result in convex optimization: for a differentiable convex function, any point where the gradient is zero is a global minimizer. It argues this by applying the first-order inequality for convex functions, which bounds the…
This weekly competition report profiles the ten highest-ranked ModelScore factors and tracks a CSI 1000 index-enhancement portfolio. It summarizes average style exposures from daily multivariate regressions and reports average within-industry Rank ICs, the…
This discussion examines how growth in assets can affect a quantitative strategy’s returns and stability. It describes a trade-off: as a strategy takes more capital, returns may decline and volatility may rise. It distinguishes broad-capacity equity…
The document asks how to define an earnings stability factor as the mean of four years of trailing-twelve-month gross profit divided by its standard deviation. It considers calculating this from daily financial-indicator data using a rolling window, and asks…
This study examines how neural network hidden-layer configurations affect reinforcement-learning performance in stock portfolio optimization. Using the FinRL framework, agents receive open, high, low, close, and volume data and seek to improve portfolio…
This paper summary presents a method for evaluating active mutual funds when their stated benchmark may not match their actual style. It argues that omitted risk factors can make factor-model alpha estimates too high, while benchmark mismatch can also…
This study examines whether differences in buy and sell limit order strength contain information about short-term price moves and longer-horizon stock returns in Chinese equities. Its Spread measure aggregates order book strength at each tick. Across…
This overview organizes machine learning algorithms in two ways: by learning setup and by functional family. It distinguishes supervised learning with labeled examples, unsupervised learning that finds structure in unlabeled data, and semi-supervised…
This report describes a regularized fund-evaluation model used to rank Chinese equity funds and form fund-of-funds portfolios. It compares funds selected for overall ability, stock-selection ability, and market-timing ability, then reviews their holdings and…
This explanation clarifies why a stock strategy may send orders for shares it already owns. It separates current holdings into positions with sell signals, new buy candidates, and existing positions that still qualify to be held. The strategy sells the first…
This article explains how to allocate weights in a multi-factor equity portfolio using a structured risk model. It decomposes stock returns into factor exposures, factor returns, and stock-specific returns, then describes industry and style neutrality…
This report summary outlines a risk-budgeting approach to domestic multi-asset allocation conditioned on economic growth and inflation regimes. It describes statistical comparisons of asset behavior across those states and reports rotation patterns: bonds…
This research note describes a China A-share fund designed to enhance returns over the CSI 500 while managing benchmark-relative risk. It attributes the strategy’s excess returns to quantitative multifactor stock selection, sector allocation, trading…