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Foreign exchange forecasting and portfolio optimization strategy based on hybrid-molecular differential evolution algorithms

  • Application of soft computing
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Abstract

At present, the COVID-19 epidemic is still spreading at home and abroad, and the foreign exchange market is highly volatile. From financial institutions to individual investors, foreign exchange asset allocation has become important contents worthy of attention. However, most intelligent optimization algorithms (hereinafter IOAS) adopt the existing data and ignore the forecasted one in the foreign exchange portfolio allocation, which will result in a huge difference between portfolio allocation and actual demand; at the same time, many IOAS are less adaptable and have lower optimization ability in portfolio problems. To solve the aforementioned problems, this paper first proposed a DETS based on hybrid tabu search and differential evolution algorithms (DEAs), which has excellent optimization ability. Subsequently, the DETS algorithm was applied to support vector machine (SVM) model. Experiments show that, compared with other algorithms, the MAE and RMSE obtained by using DETS optimization parameters are reduced by at least 3.79 and 1.47%, while the CTR is improved by at least 2.19%. Then combined with the DETS algorithm and Pareto sorting theory, an algorithm suitable for multi-objective optimization was further proposed, named NSDE-TS. Finally, by applying NSDE-TS algorithm, the optimal foreign exchange portfolio is acquired. The empirical analysis shows that the Pareto front obtained by this algorithm is better than that of NSGA-II. Since the lower the uniformity index and convergence index, the stronger the optimization performance of the corresponding algorithm, compared with NSGA-II, its uniformity and convergence index decreased by 15.7 and 39.6%.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Authors and Affiliations

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

    Xuecong Zhang & Chen Zhong

  2. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan

    Laith Abualigah

  3. Faculty of Information Technology, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

Authors
  1. Xuecong Zhang
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  2. Chen Zhong
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  3. Laith Abualigah
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Contributions

XZ and CZ contributed to conception and design of the manuscript and interpretation of data, literature searches and analyses, clinical evaluations, manuscript preparation, and writing the paper; LA made substantial contributions to conception and design, literature searches and analyses, participated in revising the article, and gave final approval of the version to be submitted.

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Correspondence to Chen Zhong.

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Zhang, X., Zhong, C. & Abualigah, L. Foreign exchange forecasting and portfolio optimization strategy based on hybrid-molecular differential evolution algorithms. Soft Comput 27, 3921–3939 (2023). https://doi.org/10.1007/s00500-022-07526-6

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