Insider trading.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
26 04 2023
26 04 2023
Historique:
received:
12
01
2023
accepted:
11
04
2023
medline:
17
5
2023
pubmed:
15
5
2023
entrez:
15
5
2023
Statut:
epublish
Résumé
This paper describes a dataset capturing insider trading activity at publicly traded companies. Investors and investment analysts demand this information because executives, directors and large shareholders are expected to have more intimate knowledge of their company's prospects than outsiders. Insider stock sales and purchases may reveal information about the firm's business not disclosed in financial statements. They may also convey new information predictive of stock price movements if insiders can better interpret public information about the firm. Since mid-2003, the Securities and Exchange Commission has made these insider trading reports available to the public in a structured format; however, most academic papers use proprietary commercial databases instead of regulatory filings directly. This makes replication challenging as the data manipulation and aggregation processes are opaque and historical records could be altered by the database provider over time. To overcome these limitations, the presented dataset is created from original regulatory filings; it is updated daily and includes all information reported by insiders without alteration.
Identifiants
pubmed: 37185601
doi: 10.1038/s41597-023-02147-6
pii: 10.1038/s41597-023-02147-6
pmc: PMC10130014
doi:
Types de publication
Dataset
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
237Informations de copyright
© 2023. Crown.
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