{"id":1214,"date":"2023-09-17T14:03:12","date_gmt":"2023-09-17T17:03:12","guid":{"rendered":"https:\/\/datamining.dc.uba.ar\/datamining\/?p=1214"},"modified":"2023-10-02T12:38:17","modified_gmt":"2023-10-02T15:38:17","slug":"defensa-de-tesis-javier-vasquez-saenz-02-10-2023-11-hs","status":"publish","type":"post","link":"https:\/\/datamining.dc.uba.ar\/datamining\/2023\/09\/17\/defensa-de-tesis-javier-vasquez-saenz-02-10-2023-11-hs\/","title":{"rendered":"Defensa de Tesis – Javier V\u00e1squez S\u00e1enz – 02\/10\/2023 – 11 hs."},"content":{"rendered":"

El Lunes 02\/10 se realiz\u00f3 la Defensa de Tesis del Lic. Javier V\u00e1squez S\u00e1enz titulada “Caracter\u00edsticas basadas en agrupamiento de series temporales para predicci\u00f3n de valores de bolsa”. Cont\u00f3 con la direcci\u00f3n del Dr. Facundo Manuel Quiroga y con la co-direcci\u00f3n del Dr. Aurelio Fern\u00e1<\/span><\/span><\/span><\/span>ndez Bariviera. Los jurados fueron la Dra. Lisana Mart\u00ednez, Dra. Mar\u00eda Teresa Sorrosal Forradellas, Mg. Gustavo Denicolay Pacheco. La defensa se realiz\u00f3 de manera online. La misma puede verse a trav\u00e9s del siguiente link: https:\/\/www.youtube.com\/watch?v=D2uzka6zx_A<\/a><\/p>\n

Resumen:<\/p>\n

En esta tesis proponemos la hip\u00f3tesis que incluir informaci\u00f3n de acciones relacionadas a la acci\u00f3n a predecir mejora la calidad de dicha predicci\u00f3n. Estas acciones relacionadas pueden obtenerse mediante distintos m\u00e9todos de agrupamiento sobre informaci\u00f3n de precios, rendimientos y ratios derivados de los balances trimestrales.<\/p>\n

Nuestro conjunto de datos se bas\u00f3 en las acciones de las empresas que cotizan en los mercados de valores de los Estados Unidos, dentro de las 3000 empresas con mayor capitalizaci\u00f3n burs\u00e1til, listadas en el \u00edndice Russell 3000. Dentro de dichas empresas, se han impuesto dos condiciones: (i) que hayan cotizado y (ii) que haya presentado balances en el per\u00edodo de enero de 2017 a junio de 2022, representando un total de 2359 acciones para trabajar. Utilizamos los balances trimestrales para generar 10 ratios financieros y los convertimos en series temporales. Tambi\u00e9n utilizamos las series habituales de precios de cierre y rendimientos diarios. Con estos conjuntos de series temporales agrupamos las acciones representadas con estas caracter \u0301\u0131sticas utilizando K-Means o K-Medoids con distancias basadas en Dynamic Time Warping, Extended Frobenius Norm y en la distancia euclidiana de los coeficientes generados por transformadas r\u00e1pidas de Fourier. La informaci\u00f3n derivada de estos agrupamientos permiti\u00f3 seleccionar las caracter\u00edsticas a proporcionar a modelos de ARIMA y Redes Neuronales, utilizando no solo los datos de la acci\u00f3n a predecir, sino la correspondiente al conjunto de acciones presentes en el grupo al cual pertenece la acci\u00f3n, para cada tipo de agrupamiento.<\/p>\n

El objetivo de este proceso es mejorar la predicci\u00f3n de precios o rendimientos y generar a partir de ello recomendaciones de inversi\u00f3n. Para validar la estrategia propuesta, comparamos el efecto de los diferentes modelos de agrupamiento y predicci\u00f3n sobre los rendimientos generados por una estrategia de inversi\u00f3n. Comparamos asimismo estos resultados con los obtenidos a trav\u00e9s de m\u00e9todos tradicionales de inversi\u00f3n en los mercados de valores. Nuestros resultados muestran una ventaja en el uso de la informaci\u00f3n adicional proporcionada por los m\u00e9todos de agrupaci\u00f3n. Adem\u00e1s, los m\u00e9todos basados en Redes Neuronales resultan en una mayor tasa de rendimiento que aquellos basados en m\u00e9todos tradicionales como ARIMA. Las principales contribuciones de esta tesis fueron introducir las series temporales derivadas de los balances financieros para realizar agrupamientos de datos y usar los resultados de estas agrupaciones para seleccionar as \u0301\u0131 la informaci\u00f3n m\u00e1s \u00fatil para los modelos de ARIMA y Redes Neuronales.<\/p>\n","protected":false},"excerpt":{"rendered":"

El Lunes 02\/10 se realiz\u00f3 la Defensa de Tesis del […]<\/p>\n","protected":false},"author":1,"featured_media":43,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1214","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sin-categoria"],"yoast_head":"\nDefensa de Tesis - Javier V\u00e1squez S\u00e1enz - 02\/10\/2023 - 11 hs. - Maestr\u00eda en Data Mining<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datamining.dc.uba.ar\/datamining\/2023\/09\/17\/defensa-de-tesis-javier-vasquez-saenz-02-10-2023-11-hs\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Defensa de Tesis - Javier V\u00e1squez S\u00e1enz - 02\/10\/2023 - 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