Commodities (gold, oil, etc.) and foreign exchange markets.
This paper introduces a multi-step prompt strategy called Classify-and-Rethink (CAR) to help ChatGPT overcome behavioral biases—especially the framing effect—in financial decision-making. Applied to gold news, CAR improves score rationality and generates higher Sharpe ratios.
This paper introduces a ChatGPT-based Commodity News Ratio Index (CNRI) to forecast excess returns on commodity futures. Using over 2.5 million articles across 18 commodities and 80 years, the CNRI significantly outperforms traditional predictors both in- and out-of-sample.
This paper shows that classic market microstructure measures like VPIN, Amihud, and Roll still have predictive power—even in modern, high-frequency, machine-traded markets.
Stocks tend to perform better in the same calendar months they did well in the past. This paper shows that historical same-month returns are strong predictors of future performance—across stocks, anomalies, industries, countries, and even commodities.
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.
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.
Adding a seasonality factor to a portfolio of market, size, value, and momentum increases the Sharpe ratio from 1.04 to 1.67.