A carregar...
Publicação
Portfolio optimization: from markowitz to machine learning
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 1.23 MB | Adobe PDF |
Orientador(es)
Resumo(s)
In the past few decades, substantial progress has been made in portfolio optimization, especially
with the emergence of machine learning. Therefore, it is essential to find the models that not only
achieve the best results but also simplify the process. This project aims to demonstrate that to
achieve optimal portfolios cannot be based only on traditional statistical methods. Therefore the
Random Forest regression model, a machine learning model, was chosen to predict stock prices to
complement the Markowitz model, a classical portfolio selection model.
To evaluate the efficacy of the modified model compared to the classical model the following
methodology was adopted: data was collected (from 2012 to 2019 from 10 companies and it was
divided in 15 periods) and treated; some common technical indicators were extracted; one stock
price was predicted per period; expected returns and partially estimated volatility were derived from
the predictions and introduced in the classical model; 15 portfolios were constructed by each model;
and finally, a performance analysis was conducted. The results obtained show that the 1-day
predictions were quite accurate, almost 90%, and the modified model’s portfolios’ outperformed the
classical model’s portfolios for most periods analyzed.
Descrição
Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and Management
Palavras-chave
Portfolio Optimization Machine Learning Random Forest Markowitz Sharpe Ratio SDG 9 - Industry, innovation and infrastructure