Abnormal trading behavior of specific types of shareholders before US firm bankruptcy and its implications for firm bankruptcy prediction

Abstract

This paper examines the trading behavior of US corporate insiders and certain groups of institutional investors (short‐term, transient, top‐performing, and those with fiduciary responsibility) in the eight quarters leading up to a US firm bankruptcy filing. Using a matched sample based on year, industry, and a probability of future bankruptcy model, we find that US corporate insiders display abnormal reduced net trading activity in the quarters before bankruptcy, with corporate insiders ‘going quiet’ immediately preceding a US bankruptcy. In contrast, we find that our identified types of institutional shareholders display abnormal selling activity several quarters before a US bankruptcy. We then use this information to enhance the bankruptcy‐predictive capabilities of recent machine‐learning techniques such as gradient boosting, as well as the probability of future bankruptcy model. We find that the variables measuring the absolute value of net purchases by US corporate insiders in the two quarters prior to bankruptcy, along with the changes in ownership by specific types of institutional shareholders, improve the out‐of‐sample predictive capabilities of our two different bankruptcy prediction models. Overall, we find that specific types of shareholder display abnormal trading in the quarters preceding US firm bankruptcy, and such information improves the out‐of‐sample accuracy of firm bankruptcy prediction models.

CEO turnover and bankrupt firms’ emergence

“…Consequently, a large body of literature seeks to predict corporate bankruptcy (Barniv, Agarwal, & Leach, 2002; Cheng, Jones, & Moser, 2018; Cressy, 1996; Jones, Johnstone, & Wilson, 2017) including the outcomes of bankruptcy proceedings (e.g., Bryan, Tiras, & Wheatley, 2002; Gietzmann, Isidro, & Raonic, 2018). A firm may file for Chapter 11 bankruptcy for myriad reasons ranging from mere short‐term liquidity issues to more fundamental problems in their operating and financing policies.…”

Exploring the Impacts of Industry 4.0 from a Macroscopic Perspective

“…Of course, the optimization of machine learning and deep learning algorithms is still in progress, which is also a hot topic in the financial field of big data. For example, (Cheng et al, 2018) used matching samples based on year, industry, and probability of future bankruptcy model to enhance the solvency prediction capabilities of recent machine learning techniques. Creation of merchant intelligence has also been used by banks, investment companies and other companies.…”

Informed options trading prior to FDA announcements

“…Our findings contribute to the growing body of literature that reports the abnormal behavior of returns, volume, open interest and implied volatility spreads/skew in stock and options markets around major corporate announcements. For example, around earnings releases (e.g., Bohmann et al., 2019a; Lu & Ray, 2016; Tsai, 2014; Udpa, 1996), mergers and acquisitions (e.g., Bugeja et al., 2015; Cao et al., 2005; Chan et al., 2015), repurchases (e.g., Hao, 2016), stock splits (e.g., Chern et al., 2008), management forecast disclosures (e.g., Cairney & Swisher, 2004), bankruptcies (e.g., Cheng et al., 2018) and large price changes (e.g., Patel & Michayluk, 2016b; Savor, 2012). Holistically, the literature suggests informed trading in options markets is a pervasive issue.…”