2022年1月10日 · The theoretical background of this paper is Modern Portfolio Theory (MPT) - optimizing through machine learning. For this purpose, we used ‘compustat2’, ‘CRSP’, ‘Name’ …

2021年3月1日 · Since machine learning and deep learning models have shown overwhelming superiority than time series models, this paper combines return prediction in portfolio formation …

2024年1月1日 · Our experiments’ aim is twofold: (a) predict REITs, stocks, and bonds prices through five machine learning algorithms: linear regression (LR), support vector regression …

2021年2月5日 · Our research proposes a portfolio-creation methodology based on the random forest machine learning tool and one of two optimizers (the mean-variance or HRP). This …

learning to predict the stock direction in the rst phase of the portfolio construction. We aim at comparing the performances of a portfo-lio construc. ed with the classic structure with one …

In the context of Modern Portfolio Theory (MPT), this study delves into the application of Ma-chine Learning techniques to enhance the accuracy of expected return estimations. The research …

2021年3月1日 · Since machine learning and deep learning models have shown overwhelming superiority than time series models, this paper combines return prediction in portfolio formation …

2020年6月15日 · Adopting a machine learning technique in the asset allocation process, allows to develop a more robust portfolio in terms of risk minimization and performance metrics. The …

By doing extensive experimental analysis, it was found that, the model combining LSTM network and Ichimoku Indicator, paired with Modern Portfolio Theory and Sharpe Ratio giving …

By avoiding the inversion of the covariance matrix and identifying a hierarchical structure in the portfolio weights, HRP applies graph theory and machine learning techniques to construct a …