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dc.contributor.advisor | España Boquera, Salvador | es_ES |
dc.contributor.advisor | Zamora Martínez, Francisco | es_ES |
dc.contributor.author | Muñoz Castro, Juan Francisco | es_ES |
dc.date.accessioned | 2014-10-03T10:34:46Z | |
dc.date.available | 2014-10-03T10:34:46Z | |
dc.date.created | 2014-09-30 | |
dc.date.issued | 2014-10-03T10:34:46Z | |
dc.identifier.uri | http://hdl.handle.net/10251/40619 | |
dc.description.abstract | In the present project, a comparison of di erent types of arti cial neu- ral networks has been used to analyze their behavior with noisy time series prediction, with the goal of maximizing the bene t obtainable by investing in them. To do so, a wide range of datasets has been used, containing stock market prices until September 2014 and starting from January 2000 on- wards. The starting experiment has been a regular multilayer perceptron using a sliding window of the latest values as the input of the network and three outputs representing three possible actions as buy, sell or keep. Fur- ther experiments have been tested, such as the replacement of the three outputs classi er by a single one, converting the system in a forecasting model with only one output, or the use of di erent averages of recent val- ues instead of a simple sliding window as the network's input. Also, it has been tested the use of a single dataset from where each sample is used rst to test and validate, and to train the network later on in a new step instead of the traditional way of training-validation-test splitting of data. Finally, two new models that seize all the data have been tested, one with a speci c period of data validation, and the other one with an implicit period, as it has been skipped by doing some networks pre-training. After a comprehensive applying of these methods to the time series, certain pre- dictability was found. Some networks were able to predict the direction of change for the next day with an error rate of around the 40%, which in some optimistic cases decreases to about 30% when rejecting examples where the system has low con dence in its prediction. A practical simu- lation has been explained, showing an average gain close to the 0.33% by acting the half of the times. | es_ES |
dc.format.extent | 59 | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Universitat Politècnica de València | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject.classification | LENGUAJES Y SISTEMAS INFORMATICOS | es_ES |
dc.subject.other | Ingeniería Informática-Enginyeria Informàtica | es_ES |
dc.title | The use of neural networks for tendency prediction in financial series | es_ES |
dc.type | Proyecto/Trabajo fin de carrera/grado | es_ES |
dc.rights.accessRights | Cerrado | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica | es_ES |
dc.description.bibliographicCitation | Muñoz Castro, JF. (2014). The use of neural networks for tendency prediction in financial series. http://hdl.handle.net/10251/40619. | es_ES |
dc.description.accrualMethod | Archivo delegado | es_ES |