@article {KATSIKIS2023127700,
	title = {Time-varying minimum-cost portfolio insurance problem via an adaptive fuzzy-power LVI-PDNN},
	journal = {Applied Mathematics and Computation},
	volume = {441},
	year = {2023},
	pages = {127700},
	abstract = {It is well known that minimum-cost portfolio insurance (MPI) is an essential investment strategy. This article presents a time-varying version of the original static MPI problem, which is thus more realistic. Then, to solve it efficiently, we propose a powerful recurrent neural network called the linear-variational-inequality primal-dual neural network (LVI-PDNN). By doing so, we overcome the drawbacks of the static approach and propose an online solution. In order to improve the performance of the standard LVI-PDNN model, an adaptive fuzzy-power LVI-PDNN (F-LVI-PDNN) model is also introduced and studied. This model combines the fuzzy control technique with LVI-PDNN. Numerical experiments and computer simulations confirm the F-LVI-PDNN model{\textquoteright}s superiority over the LVI-PDNN model and show that our approach is a splendid option to accustomed MATLAB procedures.},
	keywords = {Fuzzy logic system, Neural networks, Portfolio insurance, portfolio optimization, Time-varying linear programming},
	issn = {0096-3003},
	doi = {https://doi.org/10.1016/j.amc.2022.127700},
	url = {https://www.sciencedirect.com/science/article/pii/S0096300322007688},
	author = {Vasilios N. Katsikis and Spyridon D. Mourtas and Predrag S. Stanimirovi{\'c} and Shuai Li and Cao, Xinwei}
}
