Strategies designed for hedge funds and proprietary trading firms.
Crypto returns aren’t driven by mining costs or fiat correlations. Instead, they respond to network adoption, momentum, and investor attention. This paper identifies the key factors that predict crypto returns and provides a foundation for data-driven crypto investing.
During financial crises, hedge funds and commodity index traders (CITs) offload positions as volatility rises, forcing commercial hedgers to absorb risk. This “risk convection” disrupts markets, creating trading opportunities based on liquidity shifts.
Performance * Basis-momentum is the strongest predictor of commodity returns, outperforming traditional factors like basis and momentum. * A long-short strategy based on basis-momentum earns an 18.38% annualized return with a Sharpe ratio of 0.9. * The strategy captures both spot
Gold’s returns are predictable using volatility and risk factors, but it doesn’t always act as a safe haven or inflation hedge. Instead, gold moves with stocks and bonds, though its actual returns can diverge in crises.
The study finds that inflation and industrial growth predict commodity prices, while stocks lead commodities by a month in downturns. Futures trading makes markets more efficient but increases volatility after introduction and during crises.
Equity market factors like size, profitability, and past returns can predict corporate bond returns,
Want better bond return forecasts? This paper finds a new factor that improves risk premium estimates, making Treasury bond investing more precise.
Less than 43% of stocks beat Treasury bills, and just 4% account for all market gains. 🚀📊
Most stock market gains happen during a four-hour window before European markets open. This paper finds that almost all returns come from this short time period, likely because investors are reacting to overnight news. A simple strategy that trades during this window beats buy-and-hold.
Sharpe ratio up to 7.2. A powerful AI-driven approach to price trends, demonstrating that machine learning can outperform traditional technical indicators.
Adding a seasonality factor to a portfolio of market, size, value, and momentum increases the Sharpe ratio from 1.04 to 1.67.
The study highlights greater returns in markets with high trading frictions, such as non-US developed and emerging markets.