摘 要：针对当前财务预警模型存在预测精度低、预测满意度低的问题，提出了基于深度学习神经网络的财务危机预警模型。该模型构建了 6 个一级指标和 12 个二级指标预警指标，模型由两个 RBM 和 1 个 BP 神经网络构成，使用鲸鱼算法进行模型参数的优化。在仿真实验中，通过上市企业的财务数据进行验证，此模型相比于 LSSVM 模型有更好的预测效果，为当前的财务危机预警提供了一种有益的参考。
关键词：财务危机；预警模型；鲸鱼算法
DOI:10.19850/j.cnki.2096-4706.2021.24.026
课题项目：浙江省教育厅一般科研项目 （Y202146332）
中图分类号：TP18 文献标识码：A 文章编号：2096-4706（2021）24-0101-04
Research on Early Warning Model of Enterprise Financial Crisis Based on Deep Learning Neural Network
LI Sha, CHEN Xuan
(Zhejiang Industry Polytechnic College, Shaoxing 312099, China)
Abstract: In view of the problems of low prediction accuracy and low satisfaction of current financial early warning models, a financial crisis early warning model based on deep learning neural network is proposed. The model constructs 6 first-level indicators and 12 second-level indicators for early warning. The model is composed of two RBMs and one BP neural network, and uses whale optimization algorithm to optimize model parameters. In the simulation experiment, carries out the verification through the financial data of listed companies, this model in this paper has a better predictive effect than the LSSVM model, which provides a useful reference for the current financial crisis early warning.
Keywords: financial crisis; early warning model; WOA
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作者简介：李莎（1981.08—），女，汉族，湖南邵阳人，副教授， 硕士，研究方向：财务管理、会计；陈暄（1979.03—），男，汉族， 江西南昌人，副教授，硕士，研究方向：人工智能、算法设计。