A Method Based on Genetic Programming to Automatically Construct Factors for Annual Report Scenarios

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@InProceedings{Ma:2024:ICNC-FSKD,

• author = "Yan Ma and Changsheng Zhang and Yan Gao and Ying Guo",
• title = "A Method Based on Genetic Programming to Automatically Construct Factors for Annual Report Scenarios",
• booktitle = "2024 20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)",
• year = "2024",
• month = jul,
• keywords = "genetic algorithms, genetic programming, Industries, Prediction algorithms, Knowledge discovery, Boosting, Decision trees, Stock markets, Random forests, Fuzzy systems",
• DOI = "doi:10.1109/ICNC-FSKD64080.2024.10702271",
• abstract = "Factors have always played an important role in stock analysis, but they are only effective for specific problems in specific scenarios. Therefore, constructing factors timely and quickly for different scenarios is an urgent problem to be solved. Although some experts have constructed factors, they need to manually construct factors for each scenario, and the construction process consumes time and effort. The annual report is an important and common scenario. It is an important form of information disclosure and financial reporting. Therefore, this paper proposes a method for automatically constructing factors based on genetic programming that combines expert experience for this scenario. It incorporates the knowledge and insights of experts in the field of stock analysis into the process of automati-cally constructing factors, and continuously adjusts and improves the combination of factors through genetic programming to adapt to the needs of the scenario. The effectiveness of this method is verified through empirical analysis and its advantages in specific scenarios are demonstrated.",
• notes = "Also known as \cite{10702271}",

}

Genetic Programming entries for Yan Ma Changsheng Zhang Yan Gao Ying Guo

Citations