货币政策文件分析用于预测货币政策委员会的决策
1Department of Data Science, Sejong University, Republic of Korea.
Heliyon
|October 25, 2023
概括
本研究使用文本分析预测韩国货币政策委员会基准利率投票. 支持向量机器比基线模型提高了29.5%的预测准确度.
科学领域:
- 金融经济学 金融经济学
- 计算语言学计算语言学
- 数据挖掘是一种数据挖掘.
背景情况:
- 债券市场比股票市场更大,受宏观经济指标的影响.
- 关于债券市场预测的研究有限,这为数据挖掘提供了机会.
- 宏观经济指标显著影响债券市场,需要先进的分析方法.
研究的目的:
- 提出一种新的数据挖掘方法,用于预测韩国货币政策委员会 (KMPC) 的基准利率决定.
- 为预测情绪,利用先前货币政策声明的文本分析.
- 为了提高预测KMPC投票结果的准确性.
主要方法:
- 利用以前的货币政策决策文本作为分类模型的输入来预测句子情绪.
- 采用支持矢量机 (SVM) 来进行情绪分类,达到83.7%的准确性.
- 从文件中提取大图来识别关键经济指标,并通过汇总句子情绪来计算文档情绪.
主要成果:
- 在预测句子情绪方面,SVM模型显示了83.7%的性能.
- 提取的bigrams提供了对韩国经济的重要见解.
- 预测KMPC投票结果使用汇总的文档情绪与SVM比基线提高了29.5%的表现.
- 统计测试证实了KMPC一致和非一致决定 (p <0.05) 之间的文件情绪有显著差异.
结论:
- 对货币政策文件的文本情绪分析可以有效预测基准利率决策.
- 拟议的数据挖掘方法显著提高了KMPC投票的预测准确性.
- 文件情绪分析显示,在一致与非一致的KMPC决策期间,沟通的显著差异.
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