Predicción de la dirección de variación del precio de una acción de la Bolsa de Nueva York usando información de la red social Stocktwits mediante algoritmos de minería de datos y aprendizaje automático

dc.contributorRomero, Robert
dc.creatorRodriguez Perez, Andrés Felipe
dc.date.accessioned2019-01-17T16:02:20Z
dc.date.available2019-01-17T16:02:20Z
dc.date.created2019-01-17T16:02:20Z
dc.date.issued2019-01-16
dc.identifierRodríguez, A. (2018). Predicción de la dirección de variación del precio de una acción de la Bolsa de Nueva York usando información de la red social StockTwits mediante algoritmos de minería de datos y aprendizaje automático (Trabajo de Pregrado). Universidad Santo Tomás. Bogotá, Colombia
dc.identifierhttp://hdl.handle.net/11634/14794
dc.identifierhttp://dx.doi.org/10.15332/tg.pre.2018.00001
dc.identifierreponame:Repositorio Institucional Universidad Santo Tomás
dc.identifierinstname:Universidad Santo Tomás
dc.identifierrepourl:https://repository.usta.edu.co
dc.description.abstractInternet has changed the way traders trade in stock markets, due to the instant access to specific resources, articles and statistics about any stock. However, by using social networks; the traders took an important role in the generation of large ammounts of information in wich the main opinion about the market is contained. This work digs about if this information has any predictive power over the price direction variation of a stock traded in the New York Stock Exchange, using data mining techniques and machine learning algorithms.
dc.languagespa
dc.publisherUniversidad Santo Tomás
dc.publisherPregrado Estadística
dc.publisherFacultad de Estadística
dc.relationAsur, S., & Huberman, B. A. (2010, August). Predicting the future with social media. In Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology-Volume 01 (pp. 492-499). IEEE Computer Society.
dc.relationBeysolow II, T. Applied Natural Language Processing with Python.
dc.relationCopestake, A. (2005). Natural language processing. Lecture Notes, Computer Laboratory, University of Cambridge.
dc.relationCoyne, S., Madiraju, P., & Coelho, J. (2017, November). Forecasting Stock Prices Using Social Media Analysis. In Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence & Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), 2017 IEEE 15th Intl (pp. 1031-1038). IEEE.
dc.relationDale, R., Moisl, H., & Somers, H. (2000). Handbook of natural language processing. CRC Press.
dc.relationFeldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82-89.
dc.relationFriedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189-1232.
dc.relationFriedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367-378.
dc.relationFriedman, J., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). The annals of statistics, 28(2), 337-407.
dc.relationFriedman, J. H., & Popescu, B. E. (2003). Importance sampled learning ensembles. Journal of Machine Learning Research, 94305.
dc.relationHebb, D. O. (1949). The organization of behavior.
dc.relationIzenman, A. J. (2008). Modern multivariate statistical techniques. Regression, classification and manifold learning.
dc.relationJurafsky, D. (2000). Speech and language processing: An introduction to natural language processing. Computational linguistics, and speech recognition.
dc.relationKimoto, T., Asakawa, K., Yoda, M., & Takeoka, M. (1990, June). Stock market prediction system with modular neural networks. In Neural Networks, 1990., 1990 IJCNN International Joint Conference on (pp. 1-6). IEEE.
dc.relationKooijman, J. F. (2014). Stock market prediction using social media data and finding the covariance of the LASSO.
dc.relationLeBaron, B. (2006). Agent-based computational finance. Handbook of computational economics, 2, 1187-1233.
dc.relationMcCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4), 115-133.
dc.relationMarquez, L. (2000). Machine learning and natural language processing. In Complementary documentation for the conference “Aprendizaje automático aplicado al procesamiento del lenguaje natural”, Soria.
dc.relationMcEnery, T., Xiao, R., & Tono, Y. (2006). Corpus-based language studies: An advanced resource book. Taylor & Francis.
dc.relationNausheen, S., Kumar, A., & Amrutha, K. K. SURVEY ON SENTIMENT ANALYSIS OF STOCK MARKET. ISO 690
dc.relationOh, C., & Sheng, O. (2011, December). Investigating Predictive Power of Stock Micro Blog Sentiment in Forecasting Future Stock Price Directional Movement. In Icis (pp. 1-19).
dc.relationOliveira, N., Cortez, P., & Areal, N. (2013, September). On the predictability of stock market behavior using stocktwits sentiment and posting volume. In Portuguese Conference on Artificial Intelligence (pp. 355-365). Springer, Berlin, Heidelberg.
dc.relationSamanidou, E., Zschischang, E., Stauffer, D., & Lux, T. (2007). Agent-based models of financial markets. Reports on Progress in Physics, 70(3), 409.
dc.relationTheeramunkong, T., Kongkachandra, R., & Supnithi, T. Advances in Natural Language Processing, Intelligent Informatics and Smart Technology.
dc.relationTsui, D. Predicting Stock Price Movement Using Social Media Analysis. Stanford University, Technical Report.
dc.relationWerbos, P.J. (1974). Beyond regression: new tools for prediction and analysis in the behavioral sciences, Ph.D. dissertation, Harvard University.
dc.relationZhang, X., Fuehres, H., & Gloor, P. A. (2011). Predicting stock market indicators through twitter “I hope it is not as bad as I fear”. Procedia-Social and Behavioral Sciences, 26, 55-62.
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/2.5/co/
dc.rightsAbierto (Texto Completo)
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.rightsAtribución-NoComercial-SinDerivadas 2.5 Colombia
dc.titlePredicción de la dirección de variación del precio de una acción de la Bolsa de Nueva York usando información de la red social Stocktwits mediante algoritmos de minería de datos y aprendizaje automático


Este ítem pertenece a la siguiente institución