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Related Experiment Video

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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Foreign exchange forecasting and portfolio optimization strategy based on hybrid-molecular differential evolution

Xuecong Zhang1, Chen Zhong1, Laith Abualigah2,3

  • 1School of Business Administration, South China University of Technology, Guangzhou, Guangdong China.

Soft Computing
|November 21, 2022
PubMed
Summary

This study introduces novel algorithms for foreign exchange portfolio allocation, improving accuracy by incorporating forecasted data. The new methods enhance optimization capabilities for financial institutions and investors navigating volatile markets.

Keywords:
Differential evolution algorithmExchange rate forecastForeign exchange portfolioPareto principleTabu search algorithm

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Area of Science:

  • Financial Mathematics
  • Computational Finance
  • Optimization Algorithms

Background:

  • The COVID-19 pandemic has increased foreign exchange market volatility, highlighting the need for effective asset allocation strategies.
  • Existing intelligent optimization algorithms (IOAS) often overlook forecasted data, leading to suboptimal foreign exchange portfolio allocations.
  • Current IOAS exhibit limitations in adaptability and optimization ability for complex portfolio problems.

Purpose of the Study:

  • To develop advanced algorithms for accurate foreign exchange portfolio allocation.
  • To address the shortcomings of existing intelligent optimization algorithms in handling forecasted data and improving optimization performance.
  • To propose a novel multi-objective optimization approach for acquiring optimal foreign exchange portfolios.

Main Methods:

  • Proposed a DETS algorithm, a hybrid of tabu search and differential evolution algorithms (DEAs), for enhanced optimization.
  • Applied the DETS algorithm to a support vector machine (SVM) model to optimize parameters.
  • Developed the NSDE-TS algorithm, combining DETS with Pareto sorting theory for multi-objective optimization.

Main Results:

  • DETS algorithm optimized SVM parameters, reducing Mean Absolute Error (MAE) by at least 3.79% and Root Mean Squared Error (RMSE) by 1.47%, while improving Click-Through Rate (CTR) by 2.19%.
  • The proposed NSDE-TS algorithm demonstrated superior performance in multi-objective optimization compared to NSGA-II.
  • NSDE-TS achieved a 15.7% decrease in uniformity index and a 39.6% decrease in convergence index compared to NSGA-II, indicating stronger optimization.

Conclusions:

  • The developed DETS and NSDE-TS algorithms offer significant improvements in foreign exchange portfolio allocation accuracy and optimization.
  • These novel algorithms provide a more robust solution for financial institutions and investors managing assets in volatile markets.
  • The findings suggest that incorporating forecasted data and advanced optimization techniques like NSDE-TS is crucial for effective financial portfolio management.