Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 211000017-7.doi: 10.11896/jsjkx.211000017

• Artificial Intelligence • Previous Articles     Next Articles

Empirical Study on the Forecast of Large Stock Dividends of Listed Companies Based on DE-lightGBM

CEN Jian-ming1,2, FENG Quan-xi1,2, ZHANG Li-li1, TONG Rui-chao1   

  1. 1 College of Science,Guilin University of Technology,Guilin,Guangxi 541004,China
    2 Guangxi Colleges and Universities Key Laboratory of Applied Statistics,Guilin,Guangxi 541004,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:CEN Jian-ming,born in 1994,postgraduate.His main research interests include Intelligent algorithms and machine learning.
    FENG Quan-xi,born in 1980,Ph.D,professor.His main research research interests include computational intelligence and machine learning in real world and so on.
  • Supported by:
    National Natural Science Foundation of China(62166015,61763008,62166013) and Key Science and Technology Project of Fanghenggang city(Fangcaijiao [2014] No.42).

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Abstract: Large stock dividends refers to the transfer of a large proportion of shares by listed companies.Aiming at the prediction problem of large stock dividends phenomenon implemented by listed companies,this paper proposes alightGBM based on Differential Evolution algorithm hyperparametric optimization(Named as DE-lightGBM).The model mainly includes two aspects:Firstly,Differential Evolution algorithm is used to adjust the weight of a few categories and the coefficient of regular term in the loss function of lightGBM to deal with the problem of data category imbalance.Secondly,taking F1 and AUC as evaluation indexes,Differential Evolution algorithm is used to optimize the important hyperparametric variables of lightGBM model again to find a group of parameter combinations with the best prediction effect.The numerical results show that the DE-lightGBM has achieved good results,and the F1 and AUC are 0.536 8 and 0.873 4 respectively.DE-lightGBM proposed in this paper can effectively identify the listed companies that will implement stock dividends next year.

Key words: Large Stock Dividends, Differential Evolution, LightGBM, Unbalance treatment, Machine learning

CLC Number: 

  • TP181
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