Project Layline is a research initiative that aims to leverage high performance and cloud computing to create publicly accessible datasets for research in financial economics. It lowers barriers to entry by democratizing access to data and also brings increased transparency to the field by facilitating replication studies.
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1 to 10 of 42 Results
May 7, 2025
Balogh, Attila, 2023, "Layline insider trading dataset", https://doi.org/10.7910/DVN/VH6GVH, Harvard Dataverse, V420
By using this dataset, you agree to cite the Related Publication shown below. This dataset captures insider trading activity at publicly traded companies. The Securities and Exchange Commission has made these insider trading reports available on its web site in a structured format since mid-2003. However, most academic papers use proprietary commer...
ZIP Archive - 254.4 MB - MD5: c0016021b8eede386600ed4ce1c243c9
Data
Derivative table
ZIP Archive - 756.3 MB - MD5: fcbc228b024326c271c241ccdb5295f6
Data
Footnote table
ZIP Archive - 761.7 MB - MD5: b817ce58fce5c006456fc47c2cf36c85
Data
Header metadata table
ZIP Archive - 452.8 MB - MD5: ed960fbe39b8abbd733c31fa7edfec78
Data
Non-derivative table
ZIP Archive - 418.1 MB - MD5: 1738ab32153cfb8b8cf47fb04478b851
Data
Final merged panel table
ZIP Archive - 386.5 MB - MD5: 98a09c7026b6afc15e9311c0392006ac
Data
Reporting owner table
ZIP Archive - 254.8 MB - MD5: fefb3016ab71a7f2f1eb424ee59070f8
Data
Signature table
ZIP Archive - 828.1 MB - MD5: 3d8f254c46fa8f4ab7e145ff0b0acf8c
Data
Submission table
May 7, 2025
Balogh, Attila, 2023, "Layline corporate filings dataset", https://doi.org/10.7910/DVN/WACGV5, Harvard Dataverse, V397
By using this dataset, you agree to cite the Related Publication shown below. Regulatory filing metadata obtained from the SEC's EDGAR system. Daily updates: https://dx.doi.org/10.34740/kaggle/ds/2992788
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