Gestión de Portafolios con Inteligencia Artificial y Geopolítica en Estados Unidos y Canadá
| dc.contributor.advisor | Cely Ramirez, José Alexander | |
| dc.contributor.author | Espinosa Velandia, Linda Lucia | |
| dc.contributor.author | Gil Bastidas, Erika Juliana | |
| dc.contributor.corporatename | Universidad Santo Tomás | |
| dc.contributor.googlescholar | https://scholar.google.com/citations?user=tc6-gWwAAAAJ | |
| dc.contributor.orcid | https://orcid.org/0000-0002-7928-7600 | |
| dc.date.accessioned | 2026-03-25T23:05:17Z | |
| dc.date.available | 2026-03-25T23:05:17Z | |
| dc.date.issued | 2026-03-20 | |
| dc.description | Esta investigación consideró la gestión de portafolios financieros mediante la integración de la inteligencia artificial y el análisis de factores geopolíticos en los mercados de Estados Unidos y Canadá, siendo un contexto internacional caracterizado por la volatilidad económica y los cambios políticos constantes. El objetivo principal fue detallar cómo el uso de herramientas basadas en inteligencia artificial, de la mano de variables geopolíticas, contribuyen a mejorar la identificación de riesgos y oportunidades en la toma de decisiones de inversión; para alcanzar este propósito, se desarrolló un enfoque cualitativo descriptivo, sustentado en la revisión documental y bibliográfica de artículos científicos, los cuales fueron verificados mediante la técnica de análisis. Los resultados evidenciaron que la inteligencia artificial facilita el procesamiento y análisis de grandes volúmenes de información financiera, optimizando la gestión de portafolios, mientras que el enfoque geopolítico ha permitido anticipar impactos derivados de la incertidumbre de conflictos, tensiones internacionales y cambios regulatorios. Con relación a lo antes expuesto, la combinación de inteligencia artificial y análisis geopolítico fortalece la gestión de portafolios financieros, promoviendo decisiones de inversión más informadas, estratégicas y adaptadas a las dinámicas cambiantes del entorno económico global, contribuyendo tanto a inversionistas como a instituciones financieras. | |
| dc.description.abstract | This research examined financial portfolio management through the integration of artificial intelligence and the analysis of geopolitical factors in the U.S. and Canadian markets, within an international context characterized by economic volatility and constant political changes. The main objective was to detail how the use of tools based on artificial intelligence, in conjunction with geopolitical variables, contributes to improving the identification of risks and opportunities in investment decision-making; to achieve this goal, a descriptive qualitative approach was developed, based on a documentary and bibliographic review of scientific articles, which were verified using analytical techniques. The results showed that artificial intelligence facilitates the processing and analysis of large volumes of financial information, optimizing portfolio management, while the geopolitical approach has made it possible to anticipate impacts stemming from the uncertainty of conflicts, international tensions, and regulatory changes. In light of the above, the combination of artificial intelligence and geopolitical analysis strengthens financial portfolio management, promoting more informed, strategic investment decisions adapted to the changing dynamics of the global economic environment, benefiting both investors and financial institutions. | |
| dc.description.degreelevel | Pregrado | spa |
| dc.description.degreename | Profesional en Negocios Internacionales | spa |
| dc.description.domain | http://www.ustatunja.edu.co/investigacion | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Espinosa & Gil (2026). Gestión de Portafolios con Inteligencia Artificial y Geopolítica en Estados Unidos y Canadá [Trabajo de grado, Universidad Santo Tomás].Repositorio Institucional | |
| dc.identifier.instname | instname:Universidad Santo Tomás | spa |
| dc.identifier.reponame | reponame:Repositorio Institucional Universidad Santo Tomás | spa |
| dc.identifier.repourl | repourl:https://repository.usta.edu.co | spa |
| dc.identifier.uri | http://hdl.handle.net/11634/71968 | |
| dc.language.iso | spa | |
| dc.publisher | Universidad Santo Tomás | spa |
| dc.publisher.branch | CRAI-USTA Tunja | |
| dc.publisher.faculty | Facultad de Negocios Internacionales | spa |
| dc.publisher.program | Pregrado Negocios Internacionales | spa |
| dc.relation.references | Al-Khatib, A. wael. (2023). Drivers of generative artificial intelligence to fostering exploitative and exploratory innovation: A TOE framework. Technology in Society, 75. https://doi.org/10.1016/j.techsoc.2023.102403 | |
| dc.relation.references | Adarkwah, G. K., Dorobantu, S., Sabel, C. A., & Zilja, F. (2024). Geopolitical Volatility and Subsidiary Investments. Strategic Management Journal, 45(11), 2275-2306. https://doi.org/10.1002/smj.363 | |
| dc.relation.references | Agudelo Aguirre., A. A. (2017). Teoría de Portafolio, aplicación al Mercado de Valores Colombiano y a la conformación de un portafolio diversificado. Redalyc.org. https://www.redalyc.org/journal/5713/571360714010/571360714010.pdf. | |
| dc.relation.references | Aladdin® by BlackRock - software for portfolio management. (s/f). BlackRock. Recuperado el 28 de enero de 2026, de https://www.blackrock.com/aladdin | |
| dc.relation.references | Banerjee, A., Sensoy, A., & Goodell, J. W. (2024). Volatility connectedness between geopolitical risk and financial markets: Insights from pandemic and military crisis periods. International Review of Economics & Finance, 103, 740–760. https://doi.org/10.1016/j.iref.2024.103740 | |
| dc.relation.references | Bollerslev, T. (1986). Heterocedasticidad condicional autorregresiva generalizada. 307– 327. https://doi.org/10.1016/0304-4076(86)90063-1. | |
| dc.relation.references | Caldara, D., & Iacoviello, M. (2022). Measuring geopolitical risk. American Economic Review, 112(4), 1194–1225. https://doi.org/10.1257/aer.20191823 | |
| dc.relation.references | Cartwright, R. (2025). Siete principios de la resiliencia en las carteras. MFS. https://www.mfs.com/es-es/investment-professional/insights/equity/seven-principles-of- portfolio-resilience.html. | |
| dc.relation.references | Cattlin, R. (2023). ¿Qué es la volatilidad de los mercados financieros? Forex.com. https://www.forex.com/es/news-and-analysis/volatilidad-en-mercados-financieros/. | |
| dc.relation.references | Chen, H., & Chen, S. (2017). The fading of investment–cash flow sensitivity and global development. Pacific-Basin Finance Journal, 43, 53–72. https://www.sciencedirect.com/science/article/abs/pii/S0929119917306533 | |
| dc.relation.references | Coeurdacier, N., & Guibaud, S. (2011). International portfolio diversification is better than you think. Journal of International Money and Finance, 30(2), 289–308. https://doi.org/10.1016/j.jimonfin.2010.10.003 | |
| dc.relation.references | Côté, L. (2024). Economic diplomacy and home-state responsibility for human rights abuses involving extractive industries abroad: The case of Canada. Business and Human Rights Journal. https://www.cambridge.org/core/.../economic_diplomacy... | |
| dc.relation.references | Davenport, T. H. (2014). Big Data at Work: Dispelling the Myths, Uncovering the Opportunities. https://books.google.com.co/books?id=apjBAgAAQBAJ | |
| dc.relation.references | Engle, R. F. (1982). Heteroscedasticidad condicional autorregresiva con estimaciones de la varianza de la inflación del Reino Unido. Econométrica, Vol. 50, No. 4 , 21. https://www.jstor.org/stable/1912773?read-now=1&seq=1. | |
| dc.relation.references | Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417. https://doi.org/10.2307/2325486. | |
| dc.relation.references | García, S. V. (2023). Aplicación de modelos de inteligencia artificial y aprendizaje automático para la previsión de precios y la optimización de portafolios: un enfoque integrado con datos estructurados y no estructurados con el fin de compararse con el S&P 500 como benchmark. https://repository.eafit.edu.co/server/api/core/bitstreams/1fe67909-dfe1-4390-b392- 9ff4c595cabb/content | |
| dc.relation.references | Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Deeplearningbook.org. https://www.deeplearningbook.org/contents/intro.html. | |
| dc.relation.references | Gu, S., Kelly, B. T., & Xiu, D. (2018). Empirical asset pricing via machine learning. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3159577 | |
| dc.relation.references | Holland, J. H. (1992). Complex Adaptive Systems. Daedalus, 121(1), 17–30. http://www.jstor.org/stable/20025416. | |
| dc.relation.references | Hopkin, P. (2018). Fundamentals of Risk Management: Understanding, Evaluating and Implementing Effective Risk Management. https://books.google.com.co/books?id=bzFiDwAAQBAJ | |
| dc.relation.references | Jothimani, D., Singh, A., & Gupta, M. (2022). Machine learning: The backbone of intelligent trade credit–based systems. Security and Communication Networks, 2022, Article 7149902. https://doi.org/10.1155/2022/7149902 | |
| dc.relation.references | Kairós, Lara-Haro, D. M., Negrete-Usuño, E., Paredes-León, J., & Sánchez-Sarzosa, M. J. (2023). La inteligencia artificial para la predicción de tendencias en el comercio global: Un enfoque bibliométrico y analítico. KAIRÓS, Revista de Ciencias Económicas, Jurídicas y Administrativas, 8(14), 108–125. https://www.redalyc.org/journal/7219/721980753006/721980753006.pdf | |
| dc.relation.references | Kaplan, R. D. (2013). The revenge of geography: what the map tells us about coming conflicts and the battle against fate. Choice, 50(09), 50-5262–50-5262. https://doi.org/10.5860/choice.50-5262 | |
| dc.relation.references | Kotliar, D. M. (2021). Who gets to choose? On the socio-algorithmic construction of choice. Science, Technology & Human Values, 46(2), 346–375. https://doi.org/10.1177/0162243920925147 | |
| dc.relation.references | Liu, W.-J., Ge, Y.-B., & Gu, Y.-C. (2024). News-driven stock market index prediction based on trellis network and sentiment attention mechanism. Expert Systems with Applications, 250(123966). https://doi.org/10.1016/j.eswa.2024.123966 | |
| dc.relation.references | Luo, J., Cepni, O., Demirer, R., & Gupta, R. (2025). Forecasting multivariate volatilities with exogenous predictors: An application to industry diversification strategies. Journal of Empirical Finance, 81(101595). https://doi.org/10.1016/j.jempfin.2025.101595 | |
| dc.relation.references | Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91. https://www.jstor.org/stable/1818410 | |
| dc.relation.references | Martínez, J. (2010). Managerial information system for optimizing investment portfolios. Universidad Politécnica de Madrid. https://oa.upm.es/10808/2/INVE_MEM_2010_97348.pdf | |
| dc.relation.references | Norman, A. T. (2021). Aprendizaje automático en acción. https://books.google.es/books?id=iTIREAAAQBAJ | |
| dc.relation.references | Administrativas, 8(14), 108–125. https://www.redalyc.org/journal/7219/721980753006/721980753006.pdf | |
| dc.relation.references | Panda, K. (2024). Artificial intelligence-based analysis of change in public finance between US and international markets. 2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT), 1234–1238. https://www.sciencedirect.com/science/article/abs/pii/S1059056024007329 | |
| dc.relation.references | Poon, J. (2024). The geoeconomics of globalization 2.0. Environment and Planning A: Economy and Space. https://doi.org/10.1177/0308518X241269366 | |
| dc.relation.references | Rouhiainen, L. (2018). Inteligencia artificial: 101 cosas que debes saber hoy sobre nuestro futuro. https://planetadelibrosar0.cdnstatics.com/libros_contenido_extra/40/39307_Inteligencia_artificial .pdf | |
| dc.relation.references | Rubiolo, F., & Busilli, V. S. (2021). Diplomacia económica: Aproximaciones conceptuales y su aplicación en la política de Xi Jinping hacia el Sur Global. OASIS, 34, 127–150. https://www.redalyc.org/journal/531/53169476008/53169476008.pdf | |
| dc.relation.references | Serrano, J. (2022). Building investment portfolios using the risk parity approach. Revista de Métodos Cuantitativos para la Economía y la Empresa. https://dialnet.unirioja.es/servlet/articulo?codigo=9896849 | |
| dc.relation.references | Richard S. Sutton and Andrew G. Barto. (2018). Reinforcement learning. Incompleteideas.net. http://incompleteideas.net/book/RLbook2020.pdf. | |
| dc.relation.references | Sheikh, M. J., Prins, B. C., & Schrijvers, L. (2023). Artificial intelligence: Definition and background. En Mission AI: The New System Technology. Springer. https://doi.org/10.1007/978- 3-031-21448-6_2 | |
| dc.relation.references | Tigani, S., Tadist, K., Saadane, R., Chehri, A., & Chaibi, H. (2022). Deep learning based currency exchange volatility classifier for best trading time recommendation. Procedia Computer Science, 207, 1591–1597. https://doi.org/10.1016/j.procs.2022.09.216 | |
| dc.relation.references | Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1984). Forecasting: Methods and applications (2nd ed.). John Wiley & Sons. https://www.researchgate.net/publication/52008212_Forecasting_Methods_and_Applications | |
| dc.relation.references | Yang, Z., Liu, X.-Y., & Wang, Y. (2022). FinRL: Deep reinforcement learning framework to automate trading in quantitative finance. Proceedings of the 2022 ACM Conference.https://www.scopus.com/inward/record.uri?eid=2-s2.0- 85128301239&doi=10.1145/3490354.3494366. | |
| dc.rights | Attribution-NonCommercial-NoDerivs 2.5 Colombia | en |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | |
| dc.rights.coar | http://purl.org/coar/access_right/c_abf2 | |
| dc.rights.local | Abierto (Texto Completo) | spa |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/2.5/co/ | |
| dc.subject.keyword | Financial Markets | |
| dc.subject.keyword | Risk Analysis | |
| dc.subject.keyword | International Finance | |
| dc.subject.keyword | Risk Forecasting | |
| dc.subject.keyword | Financial Analysis | |
| dc.subject.keyword | Geopolitical Conflicts | |
| dc.subject.keyword | Artificial Intelligence | |
| dc.subject.keyword | International Cooperation | |
| dc.subject.lemb | Mercados Financieros | |
| dc.subject.lemb | Análisis de riesgos | |
| dc.subject.lemb | Finanzas internacionales | |
| dc.subject.lemb | Predicción de riesgos | |
| dc.subject.lemb | Análisis financiero | |
| dc.subject.lemb | Conflictos geopolíticos | |
| dc.subject.lemb | Inteligencia artificial | |
| dc.subject.lemb | Cooperación internacional | |
| dc.subject.proposal | Inteligencia artificial | |
| dc.subject.proposal | Geopolítica | |
| dc.subject.proposal | Portafolios | |
| dc.subject.proposal | Riesgo | |
| dc.subject.proposal | Finanzas | |
| dc.subject.proposal | Volatilidad | |
| dc.subject.proposal | Predicción | |
| dc.subject.proposal | Diversificación | |
| dc.subject.proposal | Inversión | |
| dc.title | Gestión de Portafolios con Inteligencia Artificial y Geopolítica en Estados Unidos y Canadá | |
| dc.type | bachelor thesis | |
| dc.type.coar | http://purl.org/coar/resource_type/c_7a1f | |
| dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | |
| dc.type.drive | info:eu-repo/semantics/bachelorThesis | |
| dc.type.local | Trabajo de grado | spa |
| dc.type.version | info:eu-repo/semantics/acceptedVersion |
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