Knowledge that Transforms

We analyze the impact of stock market indexing on option market conditions using local linear regressions on Russell Index reconstitution. Our findings reveal that put-call parity deviations are significantly smaller for stocks at the top of the Russell 2000 Index, compared to those at the bottom of the Russell 1000 Index. Those top Russell 2000 stocks also exhibit higher trading option volume and narrower bid-ask spreads. Our results suggest that stock market indexing enhances option market conditions through increased liquidity, reducing hedging costs that benefit market makers.

Colocation services offered by stock exchanges enable market participants to achieve execution costs for large orders that are substantially lower and less sensitive to transacting against high-frequency traders. However, these benefits manifest only for orders executed on the colocated brokers' own behalf, whereas customers' order execution costs are substantially higher. Analyses of individual order executions indicate that customer orders originating from colocated brokers are less actively monitored and achieve inferior execution quality. This suggests that brokers do not make effective use of their technology, possibly due to agency frictions or poor algorithm selection and parameter choice by customers.

Almost half of US share trading volume occurs in dark markets, prompting regulatory concerns. We examine the effects of dark trading on issuers and show that, at moderate levels, dark trading improves the quality of corporate investment decisions by increasing the amount of information in prices that is new to managers. Consistent with this mechanism, higher dark trading is associated with greater investment–price sensitivity, improved managerial forecast accuracy, stronger M&A-price sensitivity, and superior future operating performance. These benefits diminish, and can reverse, at high levels of dark trading. We establish causality using exogenous changes in dark trading.

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.