El índice de sentimiento en las redes sociales y su impacto en los rendimientos del S&P 500
Contenido principal del artículo
Resumen
El estudio de la construcción y el análisis de índices de sentimiento en redes sociales es una técnica reciente que ha captado interés por su capacidad para identificar tendencias en los precios de las acciones. Además, la aplicación de inteligencia artificial para analizar rápidamente grandes volúmenes de datos de diversas fuentes de información ha creado una nueva forma de evaluar información masiva de redes sociales. El procesamiento del lenguaje natural (NLP, por sus siglas en inglés) es el método preferido que se sigue en la investigación. Originado en los años cincuenta, el NLP surgió de la intersección entre la inteligencia artificial y la lingüística. En un comienzo se empleó para recuperar información textual, con métodos basados en estadísticas para indexar y buscar de manera eficaz en grandes secciones de texto.
Descargas
PLUMX Metrics
Detalles del artículo
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
The Anáhuac Journal se distribuye bajo Licencia Creative Commons Atribución-NoComercial-CompartirIgual 4.0 Internacional.
Citas
Akhtaruzzaman, M., Boubaker, S., & Goodell, J. W. (2023). Did the collapse of Silicon Valley Bank catalyze financial contagion? Finance Research Letters, 56, 104082. https://doi.org/10.1016/j.frl.2023.104082 DOI: https://doi.org/10.1016/j.frl.2023.104082
Broadstock, D. C., & Zhang, D. (2019). Social-media and intraday stock returns: The pricing power of sentiment. Finance Research Letters, 30, 116-123. https://www.sciencedirect.com/science/article/pii/S1544612318307888 DOI: https://doi.org/10.1016/j.frl.2019.03.030
Brooks, C. (2019). Introductory econometrics for finance. Cambridge University Press. DOI: https://doi.org/10.1017/9781108524872
Derakhshan, A., & Beigy, H. (2019). Sentiment analysis on stock social media for stock price movement prediction. Engineering Applications of Artificial Intelligence, 85, 569-578. https://www.sciencedirect.com/science/article/pii/S0952197619301666 DOI: https://doi.org/10.1016/j.engappai.2019.07.002
Feuerriegel, S., Heitzmann, S. F., & Neumann, D. (2015, January). Do investors read too much into news? How news sentiment causes price formation. In the 48th Hawaii International Conference on System Sciences, IEEExplore, 4803-4812. https://ieeexplore.ieee.org/abstract/document/7070391 DOI: https://doi.org/10.1109/HICSS.2015.571
Gidófalvi, G. (2001). Using news articles to predict stock price movements. Department of Computer Science and Engineering, University of California, San Diego. https://people.kth.se/~gyozo/docs/financial-prediction.pdf
Heston, S. L., & Sinha, N. R. (2016). News versus Sentiment: Predicting Stock Returns from News Stories. (FEDS Working Paper No. 2016-48). Finance and Economics Discussion Series, 1–35. https://doi.org/10.17016/feds.2016.048 DOI: https://doi.org/10.17016/feds.2016.048
Jia, J., Pan, H., & Su, J. (2023). Analysis of Motivations, Process, and Implications of Elon Musk’s Acquisition of Twitter. BCP Business & Management, 47, 145-153. https://doi.org/10.54691/bcpbm.v47i.5185 DOI: https://doi.org/10.54691/bcpbm.v47i.5185
Jia, Q., & Xu, S. (2022). An Overall Analysis of Twitter and Elon Musk M&A Deal. Highlights in Business, Economics and Management, 2, 436-441. https://doi.org/10.54097/hbem.v2i.2399 DOI: https://doi.org/10.54097/hbem.v2i.2399
Kliestik, T., Misankova, M., Valaskova, K., & Svabova, L. (2018). Bankruptcy prevention:New effort to reflect on legal and social changes. Science and Engineering Ethics, 24, 791-803. https://doi.org/10.1007/s11948-017-9912-4 DOI: https://doi.org/10.1007/s11948-017-9912-4
Lee, H., Lee, N., Seo, H., & Song, M. (2020). Developing a supervised learning-based social media business sentiment index. The Journal of Supercomputing, 76, 3882-3897. https://doi.org/10.1007/s11227-018-02737-x DOI: https://doi.org/10.1007/s11227-018-02737-x
Li, Q., Wang, T., Li, P., Liu, L., Gong, Q., & Chen, Y. (2014). The effect of news and public mood on stock movements. Information Sciences, 278, 826-840. https://doi.org/10.1016/j.ins.2014.03.096 DOI: https://doi.org/10.1016/j.ins.2014.03.096
Lugmayr, A. (2012). Predicting the future of investor sentiment with social media in stock exchange investments: A basic framework for the DAX performance index. In M. Friedrichsen, & W. Mühl-Benninghaus (Eds.), Handbook of social media management:value chain and business models in changing media markets (pp. 565-589).Springer. https://doi.org/10.1007/978-3-642-28897-5_33 DOI: https://doi.org/10.1007/978-3-642-28897-5_33
Manda, V. K. (2023). The Collapse of Silicon Valley Bank. MAR-Ekonomi: Jurnal Manajemen, Akuntansi Dan Rumpun Ilmu Ekonomi, 2(1), 59-70. https://jurnal.seaninstitute.or.id/index.php/marekonomi/article/view/232
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
Nam, K., & Seong, N. (2019). Financial news-based stock movement prediction using causality analysis of influence in the Korean stock market. Decision Support Systems, 117, 100-112. https://doi.org/10.1016/j.dss.2018.11.004 DOI: https://doi.org/10.1016/j.dss.2018.11.004
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(1), 111. https://doi.org/10.1007/s13278-023-01116-6 DOI: https://doi.org/10.1007/s13278-023-01116-6
O’Shaughnessy, J. P. (2006). Predicting the markets of tomorrow: A contrarian investment strategy for the next twenty years. Penguin. https://shorturl.at/hxHIV
Pandey, D. K., Hassan, M. K., Kumari, V., & Hasan, R. (2023). Repercussions of the Silicon Valley Bank collapse on global stock markets. Finance Research Letters, 55, 104013. https://doi.org/10.1016/j.frl.2023.104013 DOI: https://doi.org/10.1016/j.frl.2023.104013
Radio 5 [@radio5_rne]. (2023, March 13). "Europa llama a la tranquilidad frente a la quiebra del Silicon Valley Bank. La Comisión recuerda que su presencia en Europa es muy limitada, pero aseguran que estarán muy pendientes." [Tweet] X (formerly Tweeter). https://twitter.com/radio5_rne/status/1635251865624674304
Ruan, Y., Durresi, A., & Alfantoukh, L. (2018). Using Twitter trust network for stock market analysis. Knowledge-Based Systems, 145, 207-218. https://www.sciencedirect.com/science/article/pii/S0950705118300248 DOI: https://doi.org/10.1016/j.knosys.2018.01.016
Schindler, F. (2013). Predictability and persistence of the price movements of the S&P/Case-Shiller house price indices. The Journal of Real Estate Finance and Economics, 46(1), 44-90. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2192128 DOI: https://doi.org/10.1007/s11146-011-9316-1
Shah, D., Isah, H., & Zulkernine, F. (2018, December 10–13). Predicting the effects of news sentiments on the stock market. In 2018 IEEE International Conference on Big Data, Seattle, WA, United States. https://doi.org/10.1109/BigData.2018.8621884 DOI: https://doi.org/10.1109/BigData.2018.8621884
Smith, Molly (2023; May 23) Fed Tracks Market Sentiment With New Index Built from 4.4 Million Tweets. Bloomberg. https://www.bloomberg.com/news/articles/2023-05-23/fed-index-built-from-4-4-million-tweets-predicts-size-ofhikes?embedded-checkout=true
Soroka, S. (2015; May 25). Why do we pay more attention to negative news than to positive news? British Politics and Policy at LSE [blog entry]. https://eprints.lse.ac.uk/62222/
Steinert, L., & Herff, C. (2018). Predicting altcoin returns using social media. Plos one, 13(12), article 0208119. https://doi.org/10.1371/journal.pone.0208119 DOI: https://doi.org/10.1371/journal.pone.0208119
Tikkanen, M. (2021). Predicting the FTSE All-Share index daily close-to-close price direction using sentiment analysis on tweets from UK. LUT University. https://lutpub.lut.fi/handle/10024/163481
Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of Finance, 62(3), 1139-1168. https://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.2007.01232.x DOI: https://doi.org/10.1111/j.1540-6261.2007.01232.x
Thaler, R. H. (2015). Misbehaving: The making of behavioral economics. W.W. Norton & Company. https://shorturl.at/adC17
Van Vo, L., & Le, H. T. T. (2023). From Hero to Zero: The Case of Silicon Valley Bank. Journal of Economics and Business, 127, article 106138. https://doi.org/10.1016/j.jeconbus.2023.106138 DOI: https://doi.org/10.1016/j.jeconbus.2023.106138
Yousaf, I., & Goodell, J. W. (2023). Responses of US equity market sectors to the Silicon Valley Bank implosion. Finance Research Letters, 55, article 103934. https://doi.org/10.1016/j.frl.2023.103934 DOI: https://doi.org/10.1016/j.frl.2023.103934