Twitter Sentiment Analysis and Influence on Stock Performance Using Transfer Entropy and EGARCH Methods.

ABSTRACT: Financial economic research has extensively documented the fact that the impact of the arrival of negative news on stock prices is more intense than that of the arrival of positive news. The authors of the present study followed an innovative approach based on the utilization of two artificial intelligence algorithms to test that asymmetric response effect. Methods: The first algorithm was used to web-scrape the social network Twitter to download the top tweets of the 24 largest market-capitalized publicly traded companies in the world during the last decade. A second algorithm was then used to analyze the contents of the tweets, converting that information into social sentiment indexes and building a time series for each considered company. After comparing the social sentiment indexes' mo

SUBMITTER: Mendoza-Urdiales RA 

PROVIDER: S-EPMC9324505 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature