Knowledge that Transforms

We empirically identify a group of stock-exchange accounts that profit from 11 years of earnings surprises. Their trading behavior is consistent with privately informed trading, yet they have negative and temporary price impacts. We then empirically identify a second group of accounts that have positive and permanent price impacts. The trading behavior of the second group is more consistent with trading on public information, and they trade the wrong way before earnings surprises. The behavior of both account groups contrasts with models that associate permanent price impact with privately informed trading.

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.

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 investigate the role of market microstructure metrics in predicting price dynamics for five cryptocurrencies. We show that measures of liquidity and price discovery have predictive power for price dynamics measures used in electronic market making, dynamic hedging strategies, and volatility estimation. We identify own market and cross-market effects for Roll measures and VPINs in BTC and ETH. Our results change little during crypto winter or the 2022 change in interest regimes. Market dynamics of cryptocurrencies are similar to those of futures but exhibit greater toxicity. Our findings are relevant for proposals regarding the appropriate regulatory structure for digital currencies.