Results 281 to 290 of about 7,461,083 (331)
Some of the next articles are maybe not open access.
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, Hai-Sheng 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, Hai-Sheng Yang
semanticscholar +1 more source
Optimal Control of the Portfolio
Automation and Remote Control, 2001zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kibzun, A. I., Kuznetsov, E. A.
openaire +1 more source
Drawdown Measure in Portfolio Optimization [PDF]
International Journal of Theoretical and Applied Finance, 2004 A new one-parameter family of risk measures called Conditional Drawdown (CDD) has been proposed. These measures of risk are functionals of the portfolio drawdown (underwater) curve considered in active portfolio management. For some value of the tolerance parameter Alpha, in the case of a single sample path, drawdown functional is defined as the mean ...
ALEXEI CHEKHLOV +2 more
openaire +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
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
On the Optimality of Portfolio Insurance
The Journal of Finance, 1985ABSTRACTThis paper examines the optimality of an insurance strategy in which an investor buys a risky asset and a put on that asset. The put's striking price serves as the insurance level. In complete markets, it is highly unlikely that an investor would utilize such a strategy.
Benninga, Simon, Blume, Marshall E
openaire +1 more source
Quantitative stock portfolio optimization by multi-task learning risk and return
Information FusionSelecting profitable stocks for investments is a challenging task. Recent research has made significant progress on stock ranking prediction to select top-ranked stocks for portfolio optimization.
Yu Ma +4 more
semanticscholar +1 more source