Text Mining for U.S. Pension De-Risking Analysis

Limin Zhang, Ruilin Tian and Jun Chen
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Limin Zhang: Department of Accounting and Information Systems, College of Business, North Dakota State University, Fargo, ND 58108, USA
Ruilin Tian: Department of Transportation, Logistics, & Finance, College of Business, North Dakota State University, Fargo, ND 58108, USA
Jun Chen: Department of Transportation, Logistics, & Finance, College of Business, North Dakota State University, Fargo, ND 58108, USA

Risks, 2022, vol. 10, issue 2, 1-18

Abstract: In the past 30 years, as sponsors of defined benefit (DB) pension plans were facing more severe underfunding challenges, pension de-risking strategies have become prevalent for firms with DB plans to reduce pension-related risks. However, it remains unclear how pension de-risking activities affect firms’ performance, partially due to the lack of de-risking data. In this study, we develop a multi-phase methodology to build a de-risking database for the purpose of investigating impacts of firms’ pension risk transfer activities. We extract company filings between 1993 and 2018 from the SEC EDGAR database to identify different “de-risking” strategies that US-based companies have used. A combination of text mining, machine learning, and natural language processing methods is applied to the textual data for automated identification and classification of de-risking strategies. The contribution of this study is three-fold: (1) the design of a multi-phase methodology that identifies and extracts hidden information from a large amount of textual data; (2) the development of a comprehensive database for pension de-risking activities of US-based companies; and (3) valuable insights to companies with DB plans, pensioners, and practitioners in pension de-risking markets through empirical analysis.

Keywords: text mining; machine learning; natural language processing; classification; supervised learning; pension de-risking; SEC EDGAR (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2022
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