Author(s): Nimtz, Julius
Date: 2019
Persistent ID: http://hdl.handle.net/10362/73608
Origin: Repositório Institucional da UNL
Subject(s): Support vector machine; Asset allocation; Risk parity; Volatility forecast
Document details
Asset allocation using machine learning
Description
This paper,Asset Allocation using Machine Learning, proposes a two step model, forecasting rst volatility through an GJR-GARCH model and using a Support Vector machine to do the investment decision between the market portfolio and a risk parity portfolio. Besides the volatility forecast, the Support Vector Machine is based on economic, price, fundamental and sentiment data. It manages to outperform both the market (S&P 500) and a risk parity portfolio in terms of returns and risk adjusted returns.
Document Type
Master thesis
Language English
Contributor(s) RUN; Lameira, Pedro
Language English
Contributor(s) RUN; Lameira, Pedro
