Trabajo de grado, Doctorado / doctoral Degree Work
A comprehensive analysis of behavioural economics applied to social media using automated methods and asymmetric modelling
Date
2022Registration in:
904372
57272941600
Author
GARCIA MEDINA, ANDRES; 298598
Mendoza Urdiales, Román Alejandro
Institutions
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’ movements with the daily closing stock price of individual companies
using transfer entropy, our estimations confirmed that the intensity of the impact
of negative and positive news on the daily stock prices is statistically different, as
well as that the intensity with which negative news affects stock prices is greater
than that of positive news. The results support the idea of the asymmetric effect
that negative sentiment has a greater effect than positive sentiment, and these
results were confirmed with the EGARCH model