Aggregate insider trading and the prediction of corporate credit spread changes
Patrick Hable and
Patrick Launhardt ()
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Patrick Hable: 2iQ Research GmbH
Patrick Launhardt: University of Ulm
Financial Markets and Portfolio Management, 2020, vol. 34, issue 1, No 1, 31 pages
Abstract This paper shows that equity-based aggregate insider trading predicts future changes in US corporate credit spreads. Results suggest that the closer insiders are involved in daily business activities, the greater the predictive power of those insiders’ transactions is. In line with the literature, we reason and find that closely involved insiders are better at gauging future changes in cash flow realizations eventually affecting a firm’s default risk, because these insiders have greater access to in-firm information. The predictive power of aggregate insider trading doubles each time we increase the forecast horizon and each time when gradually increasing the level of default risk from BBB to CCC spreads. For the standard BBB–AAA spread, a univariate model explains up to 52% in annual credit spread change variation and is economically meaningful. An increase in one standard deviation in aggregate insider trading translates into a decrease of up to 72% of the standard deviation of annual credit spread changes. The predictive power of aggregate insider trading is neither just driven by the 2007/08 financial crisis, nor only by information conveyed from net purchasing or net selling insiders. Our results recommend portfolio and risk managers to take aggregate inside information and the heterogeneity among insiders into account when assessing future aggregate default risk.
Keywords: Aggregate insider trading; Predictive regressions; Future credit spread changes; Insider heterogeneity; Cash flow channel (search for similar items in EconPapers)
JEL-codes: G11 G12 G14 (search for similar items in EconPapers)
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