  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.
