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 article introduces factor investing as portfolio construction based on characteristics associated with differences in investment returns and risks. It distinguishes macroeconomic factors, such as growth, interest rates, and inflation, from style…
The document explains simple and multiple linear regression as ways to estimate a target from one or more predictors. It presents Ordinary Least Squares as the method for fitting coefficients by minimizing squared residuals, with coefficients describing the…
This article proposes an accessible self-study path for people interested in systematic trading. It groups suggested books and learning resources around Python and Pandas, market fundamentals across asset classes, machine learning, technical and fundamental…
This compiled overview summarizes webinars on machine learning, factor investing, algorithmic day trading, language models, options, volatility, medium-frequency trading, and alternative data. It outlines topics rather than teaching one complete strategy, so…
The document explains how quantitative value investing uses financial data and predefined rules to identify potentially undervalued stocks. It describes common measures including price-to-earnings, price-to-book, price-to-sales, price-to-cash-flow, return on…
This essay discusses how asset owners, asset managers, and companies can support sustainable investing by incorporating environmental, social, and governance considerations alongside financial analysis. It presents long-term ownership and broad market…
This article introduces eigenvalues and eigenvectors as tools for examining relationships among measured variables. It distinguishes principal component analysis, which forms orthogonal components that capture variance, from factor analysis, which models…
This research note outlines a machine-learning framework for timing equity factors. It discusses candidate signals such as valuation spreads between factors and correlations within factor pairs, then examines how those measures relate to subsequent factor…
This proposed Chinese equity screen selects stocks whose daily high-low range is greater than the open, whose dividend yield associated with 2019 exceeds 25%, and whose circulating share measure is at most 5.5 billion shares. The post describes the…
This research summary explains feature selection as a way to reduce a model’s input set before training. It outlines filter, wrapper, and embedded approaches, with filter criteria including variance or entropy, statistical measures such as F-scores and…
This research summary compares a basic reversal factor based on roughly one month of returns with volume-weighted high-frequency and structured reversal factors. The structured version incorporates both momentum and reversal behavior in high-frequency data.…
This brief BigQuant platform discussion asks how to implement complex methods for combining investment factors and whether suitable operators or code are available. The reply first asks what kind of combination the user has in mind, then says that modules…
This post describes a Chinese equity screen that selects stocks with daily amplitude above 1, codes beginning with 60, market capitalization below 10 billion yuan, and positive net profit. It also suggests sorting by a five-day moving average. The author…
The snippet demonstrates a fundamental stock screen built from valuation, income, growth, and profitability fields. It filters for a range of price-to-earnings values and minimum thresholds for earnings per share, net profit growth, and return on equity. The…
This stock screen selects Shenzhen main-board shares using daily price amplitude, a turnover-rate band, and limits on price-to-earnings and price-to-book ratios. The accompanying discussion frames the filters as a combination of volatility, market activity,…
This weekly research recap combines several China equity signals. A market-timing model evaluates broad indices through moving-average ordering and convergence, and describes the prevailing trend as weak with short-term signals leaning bearish. An industry…
This note compares DeepAlpha deep neural network and convolutional neural network stock selection experiments in a small-cap universe. The author describes a daily strategy that buys five stocks and sells them the next day, using 57 factors drawn from 19…
This Chinese A-share screening strategy combines liquidity, size, profitability, and valuation conditions. It selects Shenzhen main-board stocks with turnover between 3% and 12%, market capitalization below 10 billion yuan, no reported losses,…
The document describes a screen for companies in a metaverse-related sector. Its initial criteria are positive daily price performance and 2021 operating revenue more than 1.1 times the 2018 figure. A proposed expanded version adds positive net profit growth…
This factor-replication note divides stocks into two behavioral groups. “Team” stocks are described as low in both volatility and turnover: after rising, they are expected to attract continuation expectations, become overbought, and then retreat. “Coin”…
This post introduces a proposed study of how the strength of capital flows may relate to returns across equity styles, focusing on the size factor and the contrast between small- and large-cap stocks. Its motivating hypothesis is that investors may retain…
The document is a short discussion of difficulties encountered by a small-cap strategy in 2024. Its author suggests that adding a value factor may partly address those problems and emphasizes drawdown management or stop-loss actions as an important part of…
The proposed stock screen combines daily price amplitude above 1, return on equity above 15% for five consecutive years, and free-float market capitalization above 10 billion yuan. The article presents amplitude as a way to retain stocks with price movement,…
This short Chinese-language discussion addresses why total market capitalization may be classified as a valuation factor in one platform's documentation and as a size factor in research literature. Its answer is that valuation and size factors have some…