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Análisis de la relación dinámica entre la comunicación en X y los CDs


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Título :
Análisis de la relación dinámica entre la comunicación en X y los CDs
Autor :
García Martínez, Miguel Ángel
Tutor:
Gimeno Blanes, Francisco Javier  
Rodríguez Ibáñez, Margarita  
Editor :
Universidad Miguel Hernández de Elche
Departamento:
Departamentos de la UMH::Ingeniería de Comunicaciones
Fecha de publicación:
2024-09
URI :
https://hdl.handle.net/11000/33914
Resumen :
En la era de la información, el análisis de datos y la predicción de comportamientos en los mercados financieros se han vuelto herramientas esenciales para inversores y analistas. Este proyecto se enfoca en desarrollar un modelo predictivo que analice la relación entre la actividad en X (Twitter) y...  Ver más
In the information age, data analysis and the prediction of behaviour in financial markets have become essential tools for investors and analysts. This project focuses on developing a predictive model that analyses the relationship between activity on X (Twitter) and the values of Credit Default Swaps (CDs) of various financial institutions. This study focuses on four European banks: Credit Suisse, Deutsche Bank, Commerzbank and Banca Monte dei Paschi di Siena, institutions that have faced significant financial challenges in recent years. The analysis uses advanced Natural Language Processing (NLP) and machine learning (ML) techniques in Matlab, exploring how social media activity can impact the perception of financial risk, as reflected in Credit Default Swaps (CDS). To conduct this research, we used historical CD data extracted from the DataStream database with tweets obtained through the former Twitter API and the Graphext platform. The study period spans from January 2017 to May 2023. The development of the predictive model follows several key steps. First, a sentiment analysis of the tweets was performed, classifying them as positive, negative or neutral, which allows us to quantify the emotions expressed on social networks with respect to the companies analysed. Subsequently, this data was integrated with additional variables such as user account verification and number of followers to improve the relevance of the analysis. Finally, historical information from the CDS was used to train the model and improve the accuracy of the predictions. The use of Matlab as the central tool of the project facilitates the processing of large volumes of data and the execution of complex prediction algorithms, given its capacity to handle intensive mathematical calculations and its wide range of analysis tools. The methodological approach applied includes tokenisation and normalisation of tweets, frequency and intensity analysis, and the implementation of predictive models based on statistical and machine learning techniques. This project aims to provide a solid basis for future research in the area of the correlation between social networks and Credit Default Swaps, exploring how communication on X can have an impact on the perception of credit risk of financial institutions. In addition, the study offers a useful tool for understanding the relationship between critical events and CDS fluctuations, in order to predict abnormal behaviour in financial markets.
Palabras clave/Materias:
redes sociales
X (Twitter)
Creadit Default Swaps (CDS)
Procesamiento del Lenguaje Natural (NLP)
aprendizaje automático (machine learning)
Área de conocimiento :
CDU: Ciencias aplicadas: Ingeniería. Tecnología
Tipo de documento :
info:eu-repo/semantics/bachelorThesis
Derechos de acceso:
info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Aparece en las colecciones:
TFG- Ingeniería de Tecnologías de Telecomunicación



Creative Commons La licencia se describe como: Atribución-NonComercial-NoDerivada 4.0 Internacional.