☼ Spotlight on Faculty Research ☼
At some point in their organizational lives, many, if not most companies will experience decline and serious performance problems, which threaten their ability to continue to operate. When such problems are severe, the odds are against the leaders and managers who are tasked with turning around the organization. Our turnaround research program makes comparisons between successful and unsuccessful turnaround cases to identify patterns of strategic actions and leadership changes that separate the successful turnaround cases from the unsuccessful ones. To date, this program has offered a number of practical insights for turnaround leaders and managers to improve their chances of successfully returning the organization to a healthy performance trajectory.
First, early actions to stabilize the condition of the organization are critical to successful turnaround attempts. If these actions are late, the organization may pass a point of no return. Second, balancing between stability and change is vital to companies’ performing capacity in turnaround situations. Without such balance, the declining companies tend to be in either stagnation or disarray. Third, turnaround situations often involve the replacement of CEO or top leader. However, new leadership does not guarantee the turnaround success. During their first year, new CEOs of turnaround companies should focus on stabilizing and simplifying the operations of the organization, and should resist the urge to reorient the company into a new direction too early. Fourth, vacancies in management team before the new CEO arrival can give the new CEO some room for change without the need to remove/replace the existing management personnel. Fifth, after the new CEO has arrived, removing/replacing the existing management personnel can intensify frictions and tensions, reducing the likelihood of turnaround success. Finally, maintaining stability of support functions, such as accounting and human resources, after the new CEO arrival can enable the new CEO to focus on improving primary functions such as sales, marketing, and operations, which have a particularly large impact on the potential for a successful turnaround.
Papers from this research have received recognitions at the Academy of Management meeting and appear in leading scholarly journals.
In recent years, firms reporting revisions of prior financial statements outnumber those reporting restatements. Misstatements that are material to prior periods are required to be reported as restatements, whereas immaterial errors can be reported as revisions. Based on SEC guidance and widely used materiality benchmarks, I find a significant percentage, 29%, of revisions are suspect in that they meet at least one materiality criterion. These suspect revisions are 15% to 29% more likely to be reported when managers have a strong incentive to avoid restatements—when they face the threat of a compensation clawback for reporting a restatement. This result is especially salient when the clawback policy does not require misconduct for recoupment and when the error correction significantly reduces prior period net income. Overall, this evidence suggests that some managers use materiality discretion opportunistically to report misstatements as revisions instead of restatements.
Defined Benefit (DB Pension De-risking Project
Defined Benefit (DB) pension plans are confronted with macroeconomic swings, volatile funding levels, increased longevity, and new regulatory requirements, necessitating effective risk management strategies. With the support of the Society of Actuary (SOA), Dr. Ruilin Tian and Dr. Jeffrey (Jun) Chen spearheaded an innovative research collaboration exploring the intricate landscape of DB pension plans and their risk transfer strategies (i.e., pension de-risking), with a focus on the U.S. context over the past three decades.
The project navigates challenges posed by limited publicly available data on U.S. companies' pension de-risking activities. Collaborating with Dr. Limin Zhang, we conducted exhaustive web crawling of 30 million filings in the SEC Edgar database, employing text mining, machine learning, and manual validation to construct a unique pension de-risking database that encompasses a whole spectrum of distinctive risk transfer activities.
The outcomes culminate in multiple papers and technical report, which explored the determinants of pension de-risking, dissected the dynamics of pension assets and de-risking-adjusted liabilities, analyzed the interplay between business environment and pension de-risking decisions, and evaluated the economic impacts of pension de-risking. The related pension research can be found in the SOA report, “De-risking Strategies of Defined Benefit Plans: Empirical Evidence from the United States”, and “Applications of Machine Learning in Text Identification for DB Pension Analysis” published at Risks. More working papers targeting Insurance: Mathematics and Economics, Journal of Risk and Insurance, andJournal of Corporate Finance are under development.
The study bridges a significant gap in the pension risk management literature by furnishing empirical evidence from the U.S. market. With relevance to academics and practitioners alike, the findings extend beyond borders, offering valuable insights to corporations, pension plan sponsors and trustees, investors, and regulators for informed de-risking decisions.
- Ruilin Tian and Jeffrey (Jun) Chen, De-risking Strategies of Defined Benefit Plans: Empirical Evidence from the United States, Society of Actuarial (SOA), 2020.
- Limin Zhang, Ruilin Tian, and Jeffrey (Jun) Chen, Applications of Machine Learning in Text Identification for DB Pension Analysis, Risks, Volume 10, Issue 2, 41, 2022.