Artikel
Text mining for U.S. pension de-risking analysis
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.
- Language
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Englisch
- Bibliographic citation
-
Journal: Risks ; ISSN: 2227-9091 ; Volume: 10 ; Year: 2022 ; Issue: 2 ; Pages: 1-18 ; Basel: MDPI
- Classification
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Wirtschaft
- Subject
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classification
machine learning
natural language processing
pension de-risking
SEC EDGAR
supervised learning
text mining
- Event
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Geistige Schöpfung
- (who)
-
Zhang, Limin
Tian, Ruilin
Chen, Jun
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
-
Basel
- (when)
-
2022
- DOI
-
doi:10.3390/risks10020041
- Handle
- Last update
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10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Zhang, Limin
- Tian, Ruilin
- Chen, Jun
- MDPI
Time of origin
- 2022
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