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.
5,922 documents
This research summary examines whether intraday data can improve sector-rotation signals, focusing on realized skewness and the share of volatility attributable to downside moves. It describes constructing these measures from high-frequency observations and…
This guide explains how to configure OctoBot order sizes using fixed quantities, portfolio-scaled percentages, or amounts that change with available holdings. Fixed settings specify a base-asset quantity or a quote-currency value. Scaling settings allocate a…
This document explains how time-series momentum (TSMOM) and moving-average crossover (MACROSS) strategies can represent closely related ways to measure price trends. It describes expressing trend signals through past prices or returns, and using “signature”…
The article summarizes research that uses active share, or portfolio deviation from a benchmark, as a proxy for a fund manager’s conviction. It reports that strong past performance is associated with subsequent increases in active share, particularly for…
This strategy ranks commodity futures by roll return each month, buys the highest-return group, and shorts the lowest-return group. The groups are equally weighted, and positions are held for one month. The implementation calculates roll return from the…
This article outlines a discretionary framework for managing a cryptocurrency portfolio. It recommends setting a trading plan, defining risk and reward with stop-loss and profit targets, diversifying across tokens, researching each project, planning entries…
This Qlib example shows an online manager coordinating rolling model training and strategy updates. Its workflow begins by training an initial strategy set, then runs a routine that updates online predictions, prepares new tasks and models, and generates…
This personal-finance article uses addition, subtraction, multiplication, and division as metaphors for managing money. Addition means steadily building savings and financial knowledge. Subtraction means limiting debt and high-risk investments.…
The article presents regular investing in broad market index funds as a low-effort, long-term approach, arguing that it can reduce the need for stock picking and market timing. It attributes active managers’ difficulty beating indexes to fees, short-term…
The document summarizes research on combining market anomalies and on refining multi-factor models. For anomaly strategies, it reports that returns to many well-known anomalies declined after academic publication, while diversified long-short portfolios…
This page summarizes several quantitative investing topics: timing equity factors with cross-sectional and time-series indicators; adjusting portfolios to target volatility; modeling changing stock-bond correlations; assessing tactical asset-allocation…
This note summarizes when to use several portfolio allocation approaches. A naïve allocation is presented as a practical choice when the universe is broad and return forecasts are difficult. Risk parity and minimum variance are associated with settings where…
This Chinese-language summary presents a 2021 survey of reinforcement learning in financial decision-making. It introduces Markov decision processes as a foundation, then identifies value-based and policy-based learning approaches, including extensions that…
This guide surveys how machine learning can support quantitative investing, from data preparation and model evaluation to portfolio selection. It explains supervised, unsupervised, and reinforcement learning, and reviews methods including regularized…
This example describes a watchlist strategy that uses public SEC Form 4 filings to adjust portfolio weights. A research agent retrieves recent filings and keeps open-market purchases and discretionary open-market sales, while excluding grants, gifts, option…
This research summary describes a dynamic rebalancing overlay for multi-factor equity portfolios. The portfolio keeps its regular monthly rebalance, while market-cap factor exposure is checked daily mid-month. If exposure breaches a set threshold, the…
This April 2020 brokerage-report summary argues that overseas pandemic concerns had weakened prices without materially lowering broad-market valuations or company earnings forecasts at that point. It frames semiconductor shares against a late-2018 price…
The article outlines ways institutional investors can seek yield from DeFi lending, borrowing, and liquidity provision. It recommends comparing protocols for liquidity and expected returns, accounting for interest, fees, and token incentives, and using…
This analysis proposes heterogeneous volatility as an alternative to the standard volatility factor in multi-factor equity selection. Standard volatility is the dispersion of a stock’s returns. The proposed measure instead captures the volatility of returns…
This Chinese-language report outlines a framework for evaluating single factors used in quantitative stock selection. It organizes the assessment around information coefficients (IC) and portfolio grouping tests. The IC section includes rank IC, the…
The document examines how information coefficients (ICs) are used to assess stock-selection factors and assign weights in multifactor models. It distinguishes Pearson correlation from rank correlation and points out a gap in the usual inference: a larger IC…
This research note examines why stocks with high correlation to their benchmark may perform better than low-correlation stocks in the China A-share market, despite findings cited for overseas markets. It explains benchmark correlation in terms of constituent…
The document explains performance attribution as a way to identify where a portfolio’s returns and risks come from, assess whether excess returns may persist, investigate large fluctuations, compare strategies, and monitor the investment process. It outlines…
The document summarizes research that forecasts stock industry returns using lagged returns from a broad set of industries. Because including all industry predictors creates a high-dimensional regression with overfitting risk, the method uses LASSO to select…