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A local multi-granularity fuzzy rough set method for multi-attribute decision making based on MOSSO-LSTM and its application in stock market

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Abstract

Multi-attribute decision-making, based on historical data of attributes, considers multiple attributes and strives to find the optimal solution among numerous possible choices. Historical data cannot accurately reflect future situations of the attributes. To address this issue, this paper proposes a local multi-granularity fuzzy rough set (LMGFRS) method for multi-attribute decision making based on long short-term memory (LSTM) neural networks. Firstly, the LSTM is conducted to forecast the future trends of key attributes. And an algorithm of multi-objective salp swarm optimization (MOSSO) is employed to optimize the hyper-parameters of the LSTM. Then, based on the MOSSO-LSTM forecasting attribute trends, the prospect theory and grey relation analysis are utilized to construct different prospect value matrices and the objective concept. The risk preference, risk aversion, and risk neutral of decision-makers in the actual decision-making process are characterized. Next, by integrating the local rough set and multi-granularity fuzzy rough set, a LMGFRS method is constructed. The calculation of approximations of the LMGFRS based on the information granules of the objective concept can greatly reduce calculation complexity. Additionally, the overfitting problems are avoided by tuning the values of \((\alpha , \beta )\). Finally, the proposed LMGFRS decision-making method is applied to stock market. The results indicate that the LMGFRS method enriches rough set theory and decision-making methodology, and provides a feasible decision-making solution for investment institutions in practice.

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Data Availability

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

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Acknowledgements

The work was partly supported by the National Natural Science Foundation of China (No. 72071152), Shaanxi National Funds for Distinguished Young Scientists, China (No. 2023-JC-JQ-11), the Fundamental Research Funds for the Central Universities (No. ZYTS24049), the Humanities and Social Science Research Program of Ministry of Education (No. 22YJA630008), Guangzhou Key Research and Development Program (No. 202206010101), Guangdong Basic and Applied Basic Research Foundation (No. 2022A1515110703), Guangdong Provincial Hospital of Chinese Medicine Science and Technology Research Project (No. YN2022QN33).

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Authors and Affiliations

  1. School of Economics and Management, Xidian University, Xi’an, 710126, China

    Juncheng Bai, Bingzhen Sun, Jin Ye & Yuqi Guo

  2. College of Electronic and Information Engineering, Tongji University, Shanghai, 201804, China

    Dehua Xie

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Contributions

Juncheng Bai: Writing - Original Draft. Bingzhen Sun: Validation, Funding acquisition. Jin Ye: Conceptualization, Formal analysis. Dehua Xie: Investigation, Validation. Yuqi Guo: Supervision, Formal analysis.

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Correspondence to Bingzhen Sun.

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The data used in this study was sourced from the Wind database. As a user of the Wind database, this study obtained the necessary permissions and subscriptions to access and utilize the data. The usage of the data complied with the terms and conditions set by Wind Information Co., Ltd., the provider of the Wind database. These terms and conditions ensure the proper and ethical use of the data, protecting the rights and interests of the data contributors and the integrity of the database.

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Bai, J., Sun, B., Ye, J. et al. A local multi-granularity fuzzy rough set method for multi-attribute decision making based on MOSSO-LSTM and its application in stock market. Appl Intell 54, 5728–5747 (2024). https://doi.org/10.1007/s10489-024-05468-0

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