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’ and ‘S&P 500’...

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 with two machine learning models, i.e., random forest (RF) and support vector regression (SVR), and three deep learning models, i.e., LSTM neural network, deep multilayer ...

The research demonstrates the potential of machine learning to enhance portfolio optimization, particularly through improved predictive accuracy and dynamic risk management.

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 (SVR), XGBoost, LSTM, and KNN, and (b) demonstrate the improvements in the performance of a portfolio including stocks, bonds, and REITs that can be achieved by including ML ...

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 methodology is effective in outperforming the market benchmark.

2024年9月3日 · In this paper, a novel deep portfolio optimization (DPO) framework was proposed, combining deep learning and reinforcement learning with modern portfolio theory. DPO not only has the advantages of machine learning methods in investment decision-making, but also retains the essence of modern portfolio theory in portfolio optimization.

Stock market portfolio optimization is a very important aspect for stock market trading. Various techniques have been proposed in the literature ranging from te

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 encompasses a comprehensive three-fold objective that traverses multiple dimensions of financial prediction and portfolio optimization.

2024年10月7日 · In this paper we describe the integration of machine learning (ML) techniques into the framework of Markowitz's portfolio selection and show how they can help advance the robust mathematical strategies necessary for modern financial markets.

2021年3月1日 · Ma et al. (2021) used two machine learning models, random forest (RF) and support vector regression, as well as three deep learning models, convolutional neural network, long short-term memory (LSTM) neural network, and deep multi-layer perceptron (DMLP) to predict the return in portfolio construction.