Systematic strategies based on factors like value, momentum, quality, etc.
This paper shows that timing factor exposures over time can improve performance significantly. By forecasting returns on key equity anomalies using valuation signals, the authors build a dynamic portfolio that earns higher Sharpe ratios than static factor investing or market timing alone.
This paper finds that stocks more sensitive to investor mood swings—called "high mood beta" stocks—earn higher returns in good-mood periods (like Fridays and January) and lower returns in bad-mood periods (like Mondays and October). These effects are systematic, persistent, and tradable.
This paper shows that investors with limited ability to borrow tend to chase high-risk assets. As a result, low-risk (low-beta) assets offer better risk-adjusted returns. A strategy that goes long low-beta assets and shorts high-beta ones—called “Betting Against Beta”—consistently beats the market.
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.
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,
Less than 43% of stocks beat Treasury bills, and just 4% account for all market gains. 🚀📊
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.