Hot topic detection in central bankers’ speeches

Abstract

Analysis of central bankers’ statements is essential to understand current economic conditions and predict future market trends. The most popular method used for this purpose – qualitative monitoring and evaluation (QME) – is based on the input of domain experts, which is expensive and subjective by nature. Several researchers have attempted to use text mining techniques as alternatives. However, they have primarily focused on aligning textual information with macroeconomic indicators, rather than directly extracting keywords. The primary aim of this study is to identify the possibility of automatically detecting potential financial risk factors by applying text mining techniques to central bankers’ speeches. We propose a text mining framework to extract risk factors ex ante by detecting hot topics in speeches made by chairs of the Federal Reserve System. In the framework, we use a simple and effective unsupervised keyword-scoring method,11The source code used for this study is available at https://github.com/sophia-jihye/hot_topic_detection_in_central_bankers_speeches. which treats bigrams as keywords and incorporates the temporal importance of keywords by estimating the “emergence” of a term at a certain time period relative to previous time periods. In-depth analysis through extensive experiments was conducted to compare risk factor detection performance using eight existing methods Including statistical approaches and recent pretrained language model-based approaches. Experimental results demonstrate that our proposed method adopting a statistical approach effectively captures potential risk factors in central bankers’ speeches. Application of the proposed framework to manuscripts of speeches made between 1997 and 201922The dataset collected for this study is available at https://github.com/sophia-jihye/bis_speeches_text_dataset. revealed that the recurrence of the terms Such as “east asia” “information technology” and “subprime mortgage” which describe the Asian financial crisis Dot-com bubble And global financial crisis Respectively Could have been detected prior to the onset of the respective crises. Furthermore The quantitative evaluation revealed that the frequency of appearance of the phrase “subprime mortgage” increased by more than 400% in speeches made during the second quarter of 2007 as compared to the previous quarter Whereas the number of the New York times news articles containing the word “subprime mortgage” only increased by approximately 0%. our results show that central bankers’ speeches can be used as an appropriate data source for the ex ante identification of market risk factors. We anticipate that this study will contribute to broadening the understanding of trends in hot topics in the financial market Monitoring potential risk factors And developing preemptive response plans to deal with impending financial crises.