Results 221 to 230 of about 4,935,448 (269)
Energy Science &Engineering, EarlyView.A new energy paradigm assisted by AI. ABSTRACT The tremendous penetration of renewable energy sources and the integration of power electronics components increase the complexity of the operation and power system control. The advancements in Artificial Intelligence and machine learning have demonstrated proficiency in processing tasks requiring ...
Balasundaram Bharaneedharan +4 more
wiley +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Portfolio optimization with return prediction using deep learning and machine learning
Expert systems with applications, 2021Integrating return prediction of traditional time series models in portfolio formation can improve the performance of original portfolio optimization model.
Yilin Ma, Ruizhu Han, Weizhong Wang
semanticscholar +1 more source
Mean-variance portfolio optimization using machine learning-based stock price prediction
Applied Soft Computing, 2021The success of portfolio construction depends primarily on the future performance of stock markets. Recent developments in machine learning have brought significant opportunities to incorporate prediction theory into portfolio selection.
Wei Chen +3 more
semanticscholar +1 more source
ACM Computing Surveys, 2022
Portfolio optimization can be roughly categorized as the mean-variance approach and the exponential growth rate approach based on different theoretical foundations, trading logics, optimization objectives, and methodologies. The former and the latter are
Zhao-Rong Lai, Haisheng Yang
semanticscholar +1 more source
Portfolio optimization can be roughly categorized as the mean-variance approach and the exponential growth rate approach based on different theoretical foundations, trading logics, optimization objectives, and methodologies. The former and the latter are
Zhao-Rong Lai, Haisheng Yang
semanticscholar +1 more source
European Journal of Operational Research, 2021
In single-period portfolio optimization settings, Mean-Variance (MV) optimization can result in notoriously unstable asset allocations due to small changes in the underlying asset parameters. This has resulted in the widespread questioning of whether and
Pieter M. van Staden +2 more
semanticscholar +1 more source
In single-period portfolio optimization settings, Mean-Variance (MV) optimization can result in notoriously unstable asset allocations due to small changes in the underlying asset parameters. This has resulted in the widespread questioning of whether and
Pieter M. van Staden +2 more
semanticscholar +1 more source
Metrika, 2002
We address the problem of estimating risk-minimizing portfolios from a sample of historical returns, when the underlying distribution that generates returns exhibits departures from the standard Gaussian assumption. Specifically, we examine how the underlying estimation problem is influenced by marginal heavy tails, as modeled by the univariate Student-
G. J. Lauprete +2 more
openaire +1 more source
We address the problem of estimating risk-minimizing portfolios from a sample of historical returns, when the underlying distribution that generates returns exhibits departures from the standard Gaussian assumption. Specifically, we examine how the underlying estimation problem is influenced by marginal heavy tails, as modeled by the univariate Student-
G. J. Lauprete +2 more
openaire +1 more source
, 2021
This paper deals with a portfolio optimization problem with uncertain returns. Here, the returns of risky assets are regarded as uncertain variables which are estimated by experienced experts.
B. Li, Ran Zhang
semanticscholar +1 more source
This paper deals with a portfolio optimization problem with uncertain returns. Here, the returns of risky assets are regarded as uncertain variables which are estimated by experienced experts.
B. Li, Ran Zhang
semanticscholar +1 more source