We examine whether real-time business news predicts firm bankruptcy. Using full-text daily articles from the Dow Jones Newswires database, we generate firm-level predictors with ChatGPT and benchmark against FinBERT and dictionary-based models. ChatGPT-based variables outperform alternatives, with sentiment scores showing predictive power across horizons. Full-text news significantly enhance predictive accuracy over headlines. News-based measures add explanatory power beyond financial variables. Finally, we show that news captures timely information on macroeconomic conditions relevant to bankruptcy prediction, such as VIX, real GDP growth, and recession probability. • We find that variables generated by ChatGPT offer additional predictive power after controlling for traditional predictors. • We find that incorporating the full text of news articles is better than using headlines alone. • We find that news variables extracted by ChatGPT significantly outperform those generated by prior textual analysis methods. • We find that ChatGPT-based news variables serve as effective real-time approximations of market volatility, economic activity, and recession probability, all critical drivers of corporate bankruptcy.

We test the joint efficiency of the bitcoin and ether options and perpetual futures markets and identify the determinants of arbitrage opportunities. Our novel fiat-currency-free put–call parity relationship motivates new arbitrage tests for options-only and option–perpetual cross-markets. Bitcoin and ether derivatives markets are becoming more efficient, especially for options of maturity ≥ 15 days. Bitcoin derivative markets are generally more efficient than ether derivative markets, but arbitrage strategies can still be highly profitable even under conservative transaction cost scenarios, which include slippage for large orders, especially during periods of high trading volumes or when the blockchain traffic becomes more congested.