Dynamic portfolio insurance strategy: a robust machine learning approach

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Siamak Dehghanpour and Akbar Esfahanipour",
  title =        "Dynamic portfolio insurance strategy: a robust machine
                 learning approach",
  journal =      "Journal of Information and Telecommunication",
  keywords =     "genetic algorithms, genetic programming, Robust
                 genetic programming (RGP), portfolio insurance
                 strategy, machine learning, portfolio optimization
                 model, constant proportion portfolio insurance (CPPI)",
  publisher =    "Taylor \& Francis",
  DOI =          "doi:10.1080/24751839.2018.1431447",
  abstract =     "we propose a robust genetic programming (RGP) model
                 for a dynamic strategy of stock portfolio insurance.
                 With portfolio insurance strategy, we divide the money
                 in a risky asset and a risk-free asset. Our applied
                 strategy is based on a constant proportion portfolio
                 insurance strategy. For determining the amount for
                 investing in the risky asset, a critical parameter is a
                 constant risk multiplier that is calculated in our
                 proposed model using RGP to reflect market dynamics.
                 Our model includes four main steps: (1) Selecting the
                 best stocks for constructing a portfolio using a
                 density-based clustering strategy. (2) Enhancing the
                 robustness of our proposed model with an application of
                 the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for
                 forecasting the future prices of the selected stocks.
                 The findings show that using ANFIS, instead of a
                 regular multi-layer artificial neural network improves
                 the prediction accuracy and our model's robustness. (3)
                 Implementing the RGP model for calculating the risk
                 multiplier. Risk variables are used to generate
                 equation trees for calculating the risk multiplier. (4)
                 Determining the optimal portfolio weights of the assets
                 using the well-known Markowitz portfolio optimization
                 model. Experimental results show that our proposed
                 strategy outperforms our previous model.",
  notes =        "Published online: 27 Feb 2018",

Genetic Programming entries for Siamak Dehghanpour Akbar Esfahanipour