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Evaluation of an unsupervised learning approach for portfolio optimization
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Autores
Orientador(es)
Resumo(s)
Throughout this directed research, we aim to identify opportunities for machine learning to
support portfolio optimization. Based on a thorough literature review we decide to pursue an
unsupervised learning approach and test its performance by conducting benchmarking against
classic portfolio optimization techniques. To ensure the validity of our findings we explore the
model’s robustness by conducting an array of experiments. In summary, we deem our version
of the clustering algorithm to provide a suitable investment framework for return-focused
investors with lower risk aversion. We suggest further research towards mitigating the
algorithm’s inconsistencies and exploring additional tuning methodologies.
Descrição
Palavras-chave
Unsupervised learning K-means Omega ratio Minkowski distance Portfolio optimization