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.
8 documents
The document describes a monthly cross-sectional strategy across 22 commodity futures. It calculates each contract’s skewness over the prior 12 months, buys three commodities with the lowest skewness, and shorts three with the highest, using equal weights…
The document explains time series momentum as a strategy that uses each instrument’s own past return, rather than ranking assets against one another. Its central signal is the sign of the prior 12-month excess return: go long when positive and short when…
The document describes a cross-sectional commodity futures strategy based on return asymmetry. It defines an IE measure as the difference between the counts of unusually large positive and negative daily returns, using a rolling 260-day window. At each month…
The document describes a monthly long-short strategy that ranks commodity futures by their past 12-month performance, buys the strongest quintile, and sells the weakest. The cited research finds profitable continuation strategies and reports an average…
The document describes a futures spread strategy based on the price difference between WTI and Brent crude oil. It explains that the oils differ in composition and production and transport characteristics, while temporary shocks may cause their price spread…
The document explains a strategy that trades the VIX futures basis and hedges broad equity exposure with E-mini S&P 500 futures. It interprets the basis as a volatility risk premium: the cited research finds it forecasts futures returns, even though it does…
This document explains a cross-sectional commodity carry strategy that ranks futures by roll returns, buys the strongest contracts, and shorts the weakest. Its simple monthly example equally weights the top and bottom quintiles and holds the positions for…
This strategy applies short-horizon mean reversion to a universe of 24 US futures markets. It uses weekly Wednesday-to-Wednesday returns and ranks contracts within groups defined by recent changes in trading volume and open interest. Volume is normalized…