将经济理论,领域知识和社会知识整合到混合情绪模型中,用于预测原油市场和原油市场
Himmet Kaplan1, Albert Weichselbraun1,2, Adrian M P Braşoveanu3,4
1University of Applied Sciences of the Grisons, Chur, Switzerland.
Cognitive computation
|June 26, 2023
概括
新的情绪分析模型CrudeBERT和CrudeBERT+通过整合经济理论和外部知识来改善原油价格预测. 在预测WTI原油期货方面,CrudeBERT+显著优于现有模型.
科学领域:
- 金融情绪分析 金融情绪分析
- 商品市场预测 商品市场预测
- 自然语言处理 (NLP) 用于融资.
背景情况:
- 传统的情绪分析模型缺乏特定领域的知识来准确预测商品市场.
- 预测原油价格需要了解超越标准情感模型的供需动态.
研究的目的:
- 为原油市场引入CrudeBERT和CrudeBERT+领域特定的情感模型.
- 通过结合经济理论和外部知识来源来增强情绪分析.
- 评估这些模型的预测性能与现有基准相比.
主要方法:
- 通过整合经济理论,实体知识和谷歌趋势数据,开发了CrudeBERT和CrudeBERT+.
- 利用动态时间扭曲来比较模型预测与WTI原油期货价格.
- 从2012年1月到2021年4月,使用新闻头条和价格数据进行了实验.
主要成果:
- 在接近WTI原油期货价格波动方面,CrudeBERT+表现优异.
- 在9年的时间里,超越了像RavenPack,BERT和FinBERT这样的既定模型.
- 在分析的大多数单个年份中取得了更好的结果,突出显示了一致的预测能力.
结论:
- 综合经济理论和外部知识的特定领域情感模型显著改善了金融情绪分析.
- CrudeBERT+显示出在波动的原油市场中加强决策的巨大潜力.
- 该研究证实了将NLP与市场预测的经济原则相结合的好处.
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