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

Román A Mendoza-Urdiales1, José Antonio Núñez-Mora1, Roberto J Santillán-Salgado2

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Negative news significantly impacts stock prices more than positive news. This study used AI to analyze Twitter data, confirming this asymmetric effect in financial markets.

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Area of Science:

  • Financial Economics
  • Computational Social Science
  • Artificial Intelligence

Background:

  • Extensive financial economic research indicates that negative news disproportionately affects stock prices compared to positive news.
  • The 'asymmetric response effect' is a well-documented phenomenon in financial markets.
  • Traditional methods may not fully capture the nuances of real-time sentiment impact on stock prices.

Purpose of the Study:

  • To investigate the asymmetric response effect of news sentiment on stock prices using artificial intelligence.
  • To quantify the differential impact of negative versus positive news on stock market behavior.
  • To validate existing financial theories with novel computational approaches.

Main Methods:

  • Web-scraping Twitter data for top tweets related to 24 large-cap companies over a decade using an AI algorithm.
  • Analyzing tweet content with a second AI algorithm to generate social sentiment indexes and time series.
  • Employing transfer entropy to compare social sentiment indexes with daily stock prices.
  • Utilizing the Exponential GJR GARCH (EGARCH) model for further validation.

Main Results:

  • Confirmed a statistically significant difference in the impact intensity of negative and positive news on daily stock prices.
  • Demonstrated that negative news has a greater impact on stock prices than positive news.
  • Validated the asymmetric effect of sentiment, where negative sentiment exerts a stronger influence.

Conclusions:

  • The study provides empirical evidence supporting the asymmetric effect of news sentiment on stock prices.
  • Artificial intelligence methods effectively capture and quantify real-time market reactions to sentiment.
  • Findings reinforce the importance of monitoring social media for understanding market dynamics.