Prioritizing the Net Sentiment Score: A Banking Industry Case Study

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The Anáhuac Journal
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Published: Jun 26, 2024
DOI: https://doi.org/10.36105/theanahuacjour.2024v24n1.10
Keywords:
stock performance, net sentiment score (NSS), robust linear model regression, brand, value impact

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José Guadalupe Mendoza Macías
Quantum Analytics, Sinaloa, Mexico
Román Alejandro Mendoza Urdiales
Quantum Analytics, Sinaloa, Mexico
https://orcid.org/0000-0003-2888-156X

Abstract

This study analyzes the impact of social media comments on the stock performance of banks registered on the United States stock market. We use artificial intelligence to monitor and extract comments in real time, together with natural language processing, to identify the sentiment of each comment. Comments were classified as positive or negative and were added by the hour for each bank during the observed period. Our results showed that both positive and negative comments have a significant effect on the stock performance, with negative comments having a more pronounced, asymmetrical impact. This study contributes to understanding how social media interactions influence the market value of companies, highlighting the importance of monitoring and managing the online perception of the companies listed on the stock market.

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How to Cite
Mendoza Macías, J. G., & Mendoza Urdiales, R. A. (2024). Prioritizing the Net Sentiment Score: A Banking Industry Case Study. The Anáhuac Journal, 24(1), Págs. 272–293. https://doi.org/10.36105/theanahuacjour.2024v24n1.10
Issue
Vol. 24 No. 1 (2024): The Anáhuac Journal, Vol. 24, num. 1, 2024, First Semester
Section
Artículos
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

This work is licensed under a Creative Commons Atribución-NoComercial-CompartirIgual 4.0 Internacional.

 

Author Biographies

José Guadalupe Mendoza Macías, Quantum Analytics, Sinaloa, Mexico

José Mendoza is a distinguished professional in the technology and financial sectors, with over three decades of experience in leadership roles and systems development. He demonstrates exceptional skill in integrating technological solutions into complex business operations. Throughout his career, José has led a variety of innovative projects, excelling in the implementation of biometric systems and customer identification platforms that have transformed banking and retail operations in various countries. His commitment to continuous technological advancement and his ability to adapt new technologies to business demands highlight his strategic vision and executive competence. José contributes not only his vast technical knowledge but also his practical experience in process optimization and the application of strategic technologies, which is essential for companies in the technology and financial sectors aspiring to lead in a highly competitive market.

Román Alejandro Mendoza Urdiales, Quantum Analytics, Sinaloa, Mexico

Román Mendoza is a leader in the artificial intelligence industry within consulting, working on developing solutions, strengthening capabilities, and generating knowledge in the field. His work focuses on delivering cutting-edge projects that integrate big data analytics and natural language processing to drive data-driven decision-making. With a solid track record of over 12 years in consulting projects, Román has specialized in implementing advanced technologies for operations research.

References

Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8. https://doi.org/10.1016/j.jocs.2010.12.007 DOI: https://doi.org/10.1016/j.jocs.2010.12.007

Chung, M.H., & Chang, Y.K. (2024). Financial reporting complexity, investor sentiment, and stock prices. Finance Research Letters, 62(Part A), article 105026. https://doi.org/10.35692/07183992.13.1.8 DOI: https://doi.org/10.1016/j.frl.2024.105026

Colicev, A., & O’Connor, P. (2020). How Social Media Impacts Brand Value: The Mediating Role of Customer Satisfaction. Multidisciplinary Business Review, 12(1), 15-26. https://doi.org/10.35692/07183992.13.1.8 DOI: https://doi.org/10.35692/07183992.13.1.8

Croux, C., & Rousseeuw, P. J. (1992). Time-efficient algorithms for two highly robust estimators of scale. Computational Statistics, Physica-Verlag HD. DOI: https://doi.org/10.1007/978-3-662-26811-7_58

Donthu, N., Kumar, S., Pandey, N., Pandey, N., & Mishra, A. (2021). Mapping the Electronic Word-of-Mouth (eWOM) Research: A Systematic Review and Bibliometric Analysis. Journal of Business Research, 135, 758-773. https://doi.org/10.1016/j.jbusres.2021.07.015 DOI: https://doi.org/10.1016/j.jbusres.2021.07.015

Evans, D. M. (1859). The history of the commercial crisis, 1857-58, and the stock exchange panic of 1859. Groombridge and Sons.

Huber, P. J. (1973). The 1972 Wald Memorial Lectures: Robust regression: Asymptotics, conjectures, and Monte Carlo. The Annals of Statistics, 1(5), 799-821. https://www.jstor.org/stable/2958283 DOI: https://doi.org/10.1214/aos/1176342503

Huber, P. J. (1981). Robust statistics. John Wiley & Sons. DOI: https://doi.org/10.1002/0471725250

Kirtac, K., & Germano, G. (2024). Sentiment Trading with Large Language Models. Finance Research Letters, 62(Part B), article 105227. https://doi.org/10.1016/j.frl.2024.105227 DOI: https://doi.org/10.1016/j.frl.2024.105227

Li, J., & Yang, J. (2024). Financial shocks, investor sentiment, and heterogeneous firms’ output volatility: Evidence from credit asset securitization markets. Finance Research Letters, 60, article 104860. https://doi.org/10.1016/j.frl.2023.104860 DOI: https://doi.org/10.1016/j.frl.2023.104860

Mendoza-Urdiales, R. A., García-Medina, A., & Núñez-Mora, J. A. (2021). Measuring information flux between social media and stock prices with Transfer Entropy. PLOS ONE, 16(9), article 0257686. https://doi.org/10.1371/journal.pone.0257686 DOI: https://doi.org/10.1371/journal.pone.0257686

Mendoza-Urdiales, R. A., Núñez-Mora, J. A., Santillán-Salgado, R. J., & Valencia-Herrera, H. (2022). Twitter Sentiment Analysis and Influence on Stock Performance Using Transfer Entropy and EGARCH Methods. Entropy, 24(7), 874. https://doi.org/10.3390/e24070874 DOI: https://doi.org/10.3390/e24070874

Núñez-Mora, J. A., & Mendoza-Urdiales, R. A. (2023). Social sentiment and impact in US equity market: an automated approach. Social Network Analysis and Mining, 13, 111. https://doi.org/10.1007/s13278-023-01116-6 DOI: https://doi.org/10.1007/s13278-023-01116-6

Plourde, A. (2023). Criticizing companies via social media and digital platforms: an alternative forum of justice for consumers? Option consommateurs. CID: 20.500.12592/m8xnp1. https://canadacommons.ca/artifacts/8246598/criticizing-companies-via-socialmedia-and-digital-platforms/9163537/

Reichheld, F. (2011). The Ultimate Question 2.0 (revised and expanded edition): How Net Promoter Companies Thrive in a Customer-Driven World. Harvard Business Review Press.

Tetlock, P. (2007). Giving content to Investor Sentiment: The role of Media in the Stock Market. The Journal of Finance, 62(3), 1139-1168. https://doi.org/10.1111/j.1540-6261.2007.01232.x DOI: https://doi.org/10.1111/j.1540-6261.2007.01232.x

Thaler, Richard H. (2017). The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2017. Nobel Prize 2017. https://www.nobelprize.org/prizes/economic-sciences/2017/thaler/facts/

Van Velthoven, S.T.M. (2014). Sentiment analysis on social media to predict Net Promoter Score (master’s thesis), Eindhoven University of Technology Research. https://pure.tue.nl/ws/portalfiles/portal/46988096/783003-1.pdf