Multi-region investment strategies, including developed and emerging markets.
An explainable multi-agent system using fine-tuned GPT-4o models for crypto portfolio management. Specialized agents analyze news, factors, and charts, collaborate on decisions, and execute trades—outperforming benchmarks in returns, accuracy, and interpretability.
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.
ChatGPT can classify crypto tweets into bullish or bearish and predict Bitcoin returns better than BERT or VADER. Its sentiment signals show strong predictive power for daily returns.
ChatGPT can predict future UK interest rate decisions by analyzing central bank speeches—turning policy tone into a reliable forecasting signal.
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.
When the euro gets stronger, European investors tend to sell government bonds from emerging countries if those bonds are in the local currencies of those countries. They do this because a stronger euro means those investments are now worth less to them, even if countries themselves aren’t riskier.
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.
Multinational firms' returns are predictably linked to foreign industry news from their sales regions. This paper shows that investors underreact to economically relevant information abroad.
Unusual negative news—news that combines rare language with negative sentiment—predicts sharp increases in market volatility. This paper shows that both firm-specific and market-wide volatility rise significantly after such news, with effects that persist for months.
This paper shows that the **entire equity premium since 1994** is earned in just four specific weeks of the FOMC cycle: weeks 0, 2, 4, and 6. Stock returns spike after informal Fed communications and Board of Governors meetings—revealing a hidden pattern driven by central bank behavior.
This paper shows that anomalies tend to lose profitability in the US after publication, likely due to arbitrage trading. However, the same does not hold for international markets, where anomalies remain strong and persistent—even post-publication.
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.