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 fund analysis describes a Chinese equity strategy that combines quantitative signals with qualitative judgment. Its inputs include mean reversion in returns and valuations, sector rotation, fundamental reversals, event effects, investor behavior and…
The document summarizes research on a listwise learning-to-rank loss designed for constructing long-short stock portfolios. The proposed loss builds on ListMLE but gives extra emphasis to the top and bottom of the ranking, where long and short selections are…
The document surveys the deep reinforcement learning agents available through FinRL, which integrates implementations from ElegantRL, Stable Baselines 3, and RLlib. The listed algorithms include value-based, policy-gradient, actor-critic, and multi-agent…
The Pearson approach forms equity pairs by ranking stocks on the correlation of their monthly returns during a formation period. For each stock, it selects the most highly correlated peers and combines their returns into a benchmark portfolio, using either…
This overview surveys private fund investment approaches, outlining how managers combine asset selection, portfolio construction, and risk management. It covers equity long-only, long-short, and market-neutral strategies; managed futures trend and arbitrage…
This report summary introduces entropy pooling as a way to incorporate qualitative or quantitative views into a risk model by updating a probability distribution. It contrasts the approach with the Black–Litterman model, describing broader choices for risk…
The document summarizes research on weighting equity alpha factors through factor-mimicking portfolios (FMPs). It presents FMPs as an alternative representation of alpha signals under a stock covariance model, with linear combinations of factors…
The research note explains how alpha factors can be represented through factor-mimicking portfolios (FMPs) under a stock covariance model. It describes translating linear combinations of alpha factors into corresponding combinations of FMPs, then choosing…
This code describes position buffers used in a trading system’s position sizing and portfolio processes. It supports three configured methods: forecast-based buffers, position-based buffers, and a nominal small buffer when buffering is disabled or an…
This overview presents fixed-income-plus investing as a way to seek steadier returns by combining bonds and equities. Its central portfolio argument is that historically low, often negative, correlations between Chinese stocks and bonds can let an…
This research framework measures crowding in equity industries using price and trading activity. It groups candidate indicators into trend-oriented measures, including price deviation and moving-average dispersion; supplementary measures such as turnover,…
The strategy sorts U.S. stocks by price-to-book ratio to form a value portfolio. Within a universe limited in the implementation to the largest 3,000 stocks by market capitalization, it buys the lowest price-to-book quintile and shorts the highest. The…
This 2020 investment analysis argues that Hong Kong equities looked attractive after the Hang Seng Index’s price-to-book ratio fell below one during the pandemic selloff. It compares prior episodes of the index falling below or nearing book value and reports…
This study examines how exposure to international trade relates to US stock returns. It uses industry transportation costs as a measure of globalization exposure, sorting stocks into portfolios and comparing their excess returns. The reported evidence…
This study describes a grouped-factor approach to combining quality, growth, and value signals for A-share stock selection. A quality composite based on enterprise value and return on equity ranks stocks and defines a filtered universe for further selection.…
This report summary describes a stock-duration factor inspired by bond duration and constructed using company financial data and analyst consensus estimates. It reports that the factor’s cross-sectional behavior varies with market conditions and across…
This research note summarizes a study of downside volatility as a measure of portfolio risk and compares it with conventional volatility. It reports that the measures are generally closely related, but their relationship weakens during historically turbulent…
This 2020 research summary argues that the Shenzhen 100 could suit long-term allocation, describing it as a liquid large- and mid-cap index with both growth and blue-chip characteristics. It cites fundamentals reported at the time, including return on equity…
This strategy forms a dollar-neutral stock portfolio by identifying securities that rank among the strongest or weakest performers over two overlapping six-month return windows. It buys stocks in the top decile in both windows and shorts those in the bottom…
This reference explains how a futures platform derives performance and activity measures at four levels: individual accounts, weighted account portfolios, individual strategies, and weighted strategy portfolios. It maps daily balance, fees, margin, risk,…
The document outlines a portfolio-allocation framework built around five macro risk factors: economic growth, consumer inflation, producer inflation, interest rates, and exchange rates. It describes constructing tradable factor proxies, reducing overlap…
The document introduces the standard form of an equity multi-factor model: stock returns are represented as a linear combination of factor returns, plus a residual component that the factors do not explain. It also notes that a portfolio’s return can be…
This note describes ways to reduce memory use and speed up parameter tuning for an ETF rotation strategy. It recommends moving suitable calculations into SQL, loading only needed data, reusing cached data, releasing large objects, precomputing values during…
This event outline introduces advanced FinRL development topics, including the FinRL-Tutorials project, reinforcement-learning portfolio allocation, stock trading in China’s A-share market, and ensemble strategies. It distinguishes portfolio allocation from…