Cryptocurrency Curated News Event Database From GDELT

Abstract

Abstract Event studies in general rely on having a high-quality curated database of events. In this paper we introduce CryptoGDelt2022, a news event dataset extracted from the Global Database of Events, Language and Tone (GDELT) containing more than 243 thousands cryptocurrency related news events between the 31st of March 2021 and 30th of April 2022. The dataset is enriched with supervised machine learning scores for Relevance, Sentiment and Strength. Supervised Relevance Score measures how related to Cryptocurrency the topic is using news web scrapped from Yahoo in general and from the Cryptocurrency part of the site, after a comparison of approaches; Latent Dirichlet Allocation (LDA), BERT and Naive Bayes, Naive Bayes was chosen and the hyper-parameter tuned model reached accuracy: 97.84 % in the train set and 91.70% in the test set. Supervised Sentiment Score measures the negative, neutral or positive tone of the news, after hyper-parameter tuning, the retrained FinBERT model achieved accuracy of 92.63% in the train set and 86.11% in the test set. Supervised Strength Score measures how strong the news by using the abnormal return using Fama French 3-factor model as target output variable, after hyper-parameter tuning, the trained Naive Bayes model reached accuracy of 63.34%. The work concludes that GDELT is more reliable source of event when compared to news selected from cryptocurrency specialized websites as it presents a more balanced positive and negative number of news. All data sets and Python Jupyter Notebooks are available in the project's GitHub.