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Do Proxies for Informed Trading Measure Informed Trading? Evidence from Illegal Insider Trades

The Review of Asset Pricing Studies 2020 10(3), 397-440 open access
Abstract This paper exploits hand-collected data on illegal insider trades to provide new evidence on the ability of a host of standard measures of illiquidity to detect informed trading. Controlling for unobserved cross-sectional and time-series variation, sampling bias, and strategic timing of insider trades, I find that when information is short-lived, only absolute order imbalance and effective spread are statistically and economically robust predictors of illegal insider trading. However, when information is long-lasting, insiders strategically time their trades to avoid illiquidity, and none of the standard measures considered are reliable predictors, including bid-ask spreads, order imbalance, Kyle’s λ, and Amihud illiquidity. (JEL D53D82G12G14K42) Received: March 14, 2019; Editorial decision: February 18, 2020 by Editor Thierry Foucault. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Who Writes the News? Corporate Press Releases during Merger Negotiations

Journal of Finance 2014 69(1), 241-291 open access
ABSTRACT Firms have an incentive to manage media coverage to influence their stock prices during important corporate events. Using comprehensive data on media coverage and merger negotiations, we find that bidders in stock mergers originate substantially more news stories after the start of merger negotiations, but before the public announcement. This strategy generates a short‐lived run‐up in bidders' stock prices during the period when the stock exchange ratio is determined, which substantially impacts the takeover price. Our results demonstrate that the timing and content of financial media coverage may be biased by firms seeking to manipulate their stock price.

Sectoral comovement and conglomerate networks

Review of Finance 2026 open access
Abstract We study the influence of multi-sector conglomerate firms on sectoral comovement. Using an innovative network model of firms and industries, we derive a novel measure of the co-concentration of industries in which two industries are more co-concentrated if they share greater exposure to the same conglomerate firms. Using time-series, cross-sectional, and longitudinal tests on establishment-level data from nearly all US firms over 1991 to 2019, we find that industries with higher co-concentration exhibit stronger comovement in employment, sales, and asset growth. Controlling for alternative explanations, a one-standard deviation increase in co-concentration corresponds to a 0.32-standard deviation increase in the comovement of employment growth. In variance-covariance decompositions, we find that firm-specific shocks explain nearly half of aggregate volatility and industry comovement and that conglomerates play a significant role in sectoral comovement. Our framework helps explain how idiosyncratic, firm-level shocks contribute to aggregate fluctuations and influence business cycles.