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Related Experiment Videos

Exchange Rate Forecasting Based on Deep Learning and NSGA-II Models.

Jun Chen1, Chenyang Zhao1, Kaikai Liu1

  • 1SILC Business School, Shanghai University, Shanghai 201800, China.

Computational Intelligence and Neuroscience
|October 4, 2021
PubMed
Summary

This study introduces a novel dual-objective optimization model for exchange rate forecasting and portfolio analysis. It utilizes deep learning and NSGA-II algorithms to enhance investment decisions in the global exchange market.

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Area of Science:

  • Quantitative Finance
  • Computational Economics
  • Financial Forecasting

Background:

  • The global exchange market is a significant financial arena with trillions of dollars in daily trading volume.
  • Effective investment strategies require accurate exchange rate prediction and optimal portfolio construction.

Purpose of the Study:

  • To propose an innovative dual-objective optimization model for exchange rate forecast analysis and portfolio management.
  • To enhance investment decision-making for both individual investors and institutions.

Main Methods:

  • Development of two novel algorithms: a deep learning model for exchange rate volatility prediction and a Non-dominated Sorting Genetic Algorithm II (NSGA-II)-based model for dual-objective optimization.
  • Integration of theoretical frameworks with practical applications in financial markets.

Main Results:

  • The deep learning model demonstrates superior accuracy in exchange rate prediction compared to traditional methods.
  • The NSGA-II-based model effectively optimizes investment portfolio selection, leading to more rational investment plans.

Conclusions:

  • The proposed model offers a significant advancement in exchange rate investment strategies.
  • This approach empowers investors with data-driven tools for improved decision-making and risk management in the exchange market.