We explore the use of technical indicators to forecast corporate bond returns with various machine learning models. We show that technical indicators yield statistically significant and economically meaningful results, consistently outperforming bond characteristics. Although bond characteristics possess predictive power for bond returns, they do not provide incremental value beyond technical indicators across all bonds. Additionally, machine learning models do not offer substantial improvements over the benchmark linear model. These results underscore the significance of technical indicators in the corporate bond market

While financial textual analysis increasingly relies on complex machine learning, we propose a simpler, data-driven alternative. Using elastic net regressions on a massive panel of 10-K n-grams, we construct a specialized dictionary that weights phrases by their marginal predictive power. This bottom-up methodology effectively forecasts expected stock returns, with a spread portfolio generating significant average returns. Our approach outperforms prominent financial dictionaries, off-the-shelf large language models, and machine learning algorithms. These results demonstrate the value of identifying financial meaning from the bottom up, highlighting the need for domain- specific models trained on relevant financial contexts

We examine the cross-sectional predictability of corporate bond returns using a novel international dataset and a set of machine learning techniques. We find strong predictability in both U.S. and non-U.S. markets, with differing predictive factors. Bonds in developed markets show greater integration with the U.S. market and stronger ties to equity markets. Predictive performance of machine learning models varies over time and is greater before the onset of the COVID-19 pandemic and during periods of deteriorating business conditions, reduced market liquidity, elevated investor sentiment, and heightened risk aversion. The results offer insights into bond pricing and global diversification opportunities

We apply machine learning methods to granular environmental variables and test if there is a strong positive relation between environmental sustainability and future stock returns. A long-short portfolio that longs stocks with high forecasted returns and sells stocks with low forecasted returns earns large abnormal returns, and it performs better when climate concerns in the media are more intense. Further diagnosis shows that various dimensions of environmental sustainability help return predictions. High forecasted returns are associated primarily with strong environmental operational performance. The return prediction based on a customized transformer model is similar

We assess the information content of order flow for the cross-section of cryptocurrency returns. Our analysis is based on a set of international order flows denominated in 11 major currencies that reflect world order flow. We find that world order flow has strong explanatory and predictive power for cryptocurrency returns. Order flow tends to dominate economic fundamentals for out-of-sample prediction, especially in the context of non-linear machine learning models, and its performance cannot be explained by limits to arbitrage. Overall, our findings indicate that order flow has a permanent effect on cryptocurrency returns