Regression approach to Portfolio Optimization
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This study introduces a convenient approach to portfolio optimization by integrating regression and machine learning techniques into the traditional Mean-Variance Optimization (MVO) framework. We establish a connection between MVO and Ordinary Least Squares (OLS) regression, redefining asset allocation through a regression perspective. The methodology includes generating target returns for regression analysis and applying machine learning algorithms for enhanced portfolio management. This approach not only reinterprets portfolio optimization but also showcases the potential of predictive models in dynamic financial markets.
Abstract
This study introduces a convenient approach to portfolio optimization by integrating regression and machine learning techniques into the traditional Mean-Variance Optimization (MVO) framework. We establish a connection between MVO and Ordinary Least Squares (OLS) regression, redefining asset allocation through a regression perspective. The methodology includes generating target returns for regression analysis and applying machine learning algorithms for enhanced portfolio management. This approach not only reinterprets portfolio optimization but also showcases the potential of predictive models in dynamic financial markets.
참고문헌 (29)
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