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ETDs @PUC-Rio
Estatística
Título: ALGORITHMS FOR ONLINE PORTFOLIO SELECTION PROBLEM
Autor: CHARLES KUBUDI CORDEIRO E SILVA
Colaborador(es): MARCO SERPA MOLINARO - Orientador
Catalogação: 15/ABR/2019 Língua(s): PORTUGUESE - BRAZIL
Tipo: TEXT Subtipo: THESIS
Notas: [pt] Todos os dados constantes dos documentos são de inteira responsabilidade de seus autores. Os dados utilizados nas descrições dos documentos estão em conformidade com os sistemas da administração da PUC-Rio.
[en] All data contained in the documents are the sole responsibility of the authors. The data used in the descriptions of the documents are in conformity with the systems of the administration of PUC-Rio.
Referência(s): [pt] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=37745&idi=1
[en] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=37745&idi=2
DOI: https://doi.org/10.17771/PUCRio.acad.37745
Resumo:
Online portfolio selection is a financial engineering problem which aims to sequentially allocate capital among a set of assets in order to maximize long-term return. With the recent advances in the field of machine learning, several models have been proposed to address this problem. Some algorithms approach the problem with a Follow-the-winner (FTW) methodology, which increases the weights of more successful stocks based on their historical performance. Contrarily, a second approach, Follow-theloser (FTW), increases the weights of less successful stocks, betting on the reversal of their prices. Some state-of-the-art FTW type algorithms have the guarantee to asymptotically approach the same performance as the best stock chosen in hindsight, while FTL algorithms have empirical evidence of overperforming the previous. Our goal is to explore the idea of learning when to use each of those two algorithm categories. We do this by using online learning algorithms that are capable of switching between the described regimes. We review the literature for existing measures of time series memory and predictability, and explicitly use this information for chosing between FTW and FTL. Later, we propose a method for choosing between this two types of algorithms in an online and dynamic manner for usage together with online learning algorithms. The method outperforms the chosen benchmark UCRP in our experiments with 36.76 percent excess returns.
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