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Estimating expected returns with forecast combinations
dc.rights.license | Atribución-NoComercial-CompartirIgual 4.0 Internacional |
dc.contributor.advisor | Gómez Portilla, Karoll |
dc.contributor.author | Richter, Robert |
dc.date.accessioned | 2021-09-03T22:41:41Z |
dc.date.available | 2021-09-03T22:41:41Z |
dc.date.issued | 2021-09-03 |
dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/80095 |
dc.description | Ilustraciones |
dc.description.abstract | This thesis proposes to apply forecasts produced by expert aggregation as novel predictor of expected returns to 2 different portfolio strategies: 1) mean-variance as proposed by (Markowitz, 1952) and 2) shrinkage of the covariance matrix S as in (Ledoit, 2004). Experts were built by generating forecasts with quantile regression as in generalized random forests and automatised versions of exponential smoothing and ARIMA. This study evaluates the predictive performance of two forecast combination algorithms 1) ML-Prod and 2) ML-Poly using a simulation study, before applying the superior method to a portfolio scenario. After evaluating prediction accuracy, the superior ML-Poly algorithm was chosen to forecast expected returns and showed promising out-of-sample results for the considered portfolios, returning superior values for the selected performance parameter and only marginal inferior results in terms of turnover ratio. Using the simulation study, the results of the portfolios were also validated. |
dc.description.abstract | Esta tesis propone aplicar los pronósticos generados por la agregación de expertos como un novedoso predictor de los rendimientos esperados a 2 estrategias de portafolio diferentes: 1) Mean-Variance como propone (Markowitz, 1952) y 2) contracción de la matriz de covarianza S como en (Ledoit, 2004). Los expertos se construyeron generando pronósticos con Quantile Regression de Generalized Random Forests y versiones automatizadas de Exponential Smoothing y ARIMA. Este estudio evalúa la precisión de los pronósticos de dos algoritmos de agregación de expertos 1) ML-Prod y 2) ML-Poly mediante un estudio de simulación, antes de aplicar el método superior a un portafolio diversificado. Después de evaluar la precisión de los pronósticos, se eligió el algoritmo superior ML-Poly para pronosticar los rendimientos esperados y mostró resultados prometedores fuera de la muestra para los portafolios considerados, devolviendo valores superiores para los parámetros de rendimiento seleccionados y resultados inferiores marginales en términos de ratio de rotación. Mediante el estudio de simulación, también se validaron los resultados de los portafolios. (Texto tomado de la fuente). |
dc.format.extent | xii, 48 páginas |
dc.format.mimetype | application/pdf |
dc.language.iso | eng |
dc.publisher | Universidad Nacional de Colombia |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ |
dc.subject.ddc | 330 - Economía |
dc.subject.other | Financial Forecasting and Simulation |
dc.subject.other | Predicción y simulación financiera |
dc.title | Estimating expected returns with forecast combinations |
dc.type | Trabajo de grado - Maestría |
dc.type.driver | info:eu-repo/semantics/masterThesis |
dc.type.version | info:eu-repo/semantics/acceptedVersion |
dc.publisher.program | Bogotá - Ciencias Económicas - Maestría en Administración |
dc.description.notes | Mención Meritoria |
dc.description.notes | Tesis de grado presentada como requisito parcial para optar al título de: Magister en Administración de Negocios (Universidad Europea de Viadrina) |
dc.contributor.researchgroup | Grupo Interdisciplinario en Teoría e Investigación Aplicada en Ciencias Económicas |
dc.description.degreelevel | Maestría |
dc.description.degreename | Magister en Administración |
dc.description.methods | Estudio Empirico |
dc.description.researcharea | Seminario de Investigación II |
dc.identifier.instname | Universidad Nacional de Colombia |
dc.identifier.reponame | Repositorio Institucional Universidad Nacional de Colombia |
dc.identifier.repourl | https://repositorio.unal.edu.co/ |
dc.publisher.department | Escuela de Administración y Contaduría Pública |
dc.publisher.faculty | Facultad de Ciencias Económicas |
dc.publisher.place | Bogotá, Colombia |
dc.publisher.branch | Universidad Nacional de Colombia - Sede Bogotá |
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dc.rights.accessrights | info:eu-repo/semantics/openAccess |
dc.subject.ecm | Financial Forecasting and Simulation |
dc.subject.ecm | Predicción y simulación financiera |
dc.subject.jel | C53 Forecasting Models; Simulation Methods |
dc.subject.lemb | Economic forecasting |
dc.subject.lemb | Pronóstico de la economía |
dc.subject.lemb | Forecasting techniques |
dc.subject.lemb | Técnicas de predicción |
dc.subject.proposal | Shrinkage |
dc.subject.proposal | Decision tress |
dc.subject.proposal | Expert aggregation |
dc.subject.proposal | Media-varianza |
dc.subject.proposal | Mean-variance |
dc.subject.proposal | Generalized random forest |
dc.subject.proposal | Automatic arima |
dc.subject.proposal | Portfolio optimisation |
dc.subject.proposal | Exponential smoothing |
dc.subject.proposal | Árboles de decision |
dc.subject.proposal | Arima automatizado |
dc.subject.proposal | Agregación de expertos |
dc.subject.proposal | Optimización de portafolios |
dc.title.translated | Estimación de los rendimientos esperados con combinaciones de previsiones |
dc.type.coar | http://purl.org/coar/resource_type/c_bdcc |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa |
dc.type.content | Text |
dc.type.redcol | http://purl.org/redcol/resource_type/TM |
oaire.accessrights | http://purl.org/coar/access_right/c_abf2 |
dcterms.audience.professionaldevelopment | Público general |
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