使用收益电话数据和转移学习预测企业破产
Hafeez Ur Rehman Siddiqui1, Beatriz Sainz de Abajo2, Isabel de la Torre Díez2
1Faculty of Computer Science and Information Technology, Khawaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan.
这项研究使用首席执行官的情绪从利电话预测企业破产. 一个深度学习模型实现了0.93准确度和F1得分,超过了传统方法.
科学领域:
- 会计与财务 会计与财务
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 企业崩是影响利益相关者的重大经济事件.
- 传统的破产预测仅依赖于历史财务数据.
- 首席执行官的沟通,包括收益电话中的语气和情绪,可能表明公司的健康状况.
研究的目的:
- 通过从收益电话中提取的情感来调查公司破产的预测.
- 提出和评估一个深度学习模型用于破产预测,该模型基于对收益的情绪分析.
主要方法:
- 情绪分析从收益电话记录中提取CEO情绪.
- 利用长短期记忆 (LSTM) 网络从情感数据中提取特征.
- 训练机器学习模型在LSTM提取的特征从合成少数群体过量采样技术 (SMOTE) 均衡数据.
主要成果:
- 提出的深度学习模型实现了高绩效分数.
- 在从SMOTE平衡数据中对LSTM特征进行训练时,获得了0.93的准确度和F1得分.
- 与传统的字袋和TF-IDF特征相比,LSTM提取的特征表现出了更高的性能.
结论:
- 在收益电话中表达的CEO情绪可以成为破产预测的有价值指标.
- 深度学习模型,特别是LSTM,在利用这些情感特征方面是有效的.
- 这种方法为基于金融数据的传统破产预测模型提供了一个有希望的替代方案或补充.
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