Portfolio Optimization under Multi-Period Scenario in the Uncertain Environment using Neural Network

Authors

  • Sunil Kumar Mittal
  • Namita Srivastava

Abstract

In this paper, a portfolio optimization problem is proposed in an uncertain environment. For this portfolio optimization problem, stocks are assumed to function as zigzag uncertain variables. Transaction price is also included in the optimized version. A mean-VaR (value at risk) bi-objective portfolio optimization model is devised to account for market uncertainty. Cardinality, bounding restrictions, and liquidity are considered in addition to risk and return to make the model more effective. A gradient-based neural network approach is applied to solve the planned model. Finally, an example portfolio is presented to display the efficacy and the feasibility of the model suggested in this paper.

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Published

2020-04-13

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Section

Articles