EDGAR-CORPUS: Billions of Tokens Make The World Go Round

• Published in: arXiv.org
• Main Authors: Lefteris Loukas, Manos Fergadiotis, Androutsopoulos, Ion, Malakasiotis, Prodromos
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 01.10.2021
• Subjects: Annual reports; Computer Science - Computation and Language; Management reports; Source code
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2109.14394

We release EDGAR-CORPUS, a novel corpus comprising annual reports from all the publicly traded companies in the US spanning a period of more than 25 years. To the best of our knowledge, EDGAR-CORPUS is the largest financial NLP corpus available to date. All the reports are downloaded, split into their corresponding items (sections), and provided in a clean, easy-to-use JSON format. We use EDGAR-CORPUS to train and release EDGAR-W2V, which are WORD2VEC embeddings for the financial domain. We employ these embeddings in a battery of financial NLP tasks and showcase their superiority over generic GloVe embeddings and other existing financial word embeddings. We also open-source EDGAR-CRAWLER, a toolkit that facilitates downloading and extracting future annual reports.