Financial Cycle With Text Information Embedding Based on LDA Measurement and Nowcasting

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Author(s): Peijin Li (Shanghai University of International Business and Economics, China), Xinyi Peng (East China Normal University, China), Chonghui Zhang (Zhejiang Gongshang University, China)and Tomas Baležentis (Lithuanian Institute of Agrarian Economics, Lithuania)
Copyright: 2024
Volume: 36
Issue: 1
Pages: 25
Source title: Journal of Organizational and End User Computing (JOEUC)
Editor(s)-in-Chief: Sangbing (Jason) Tsai (International Engineering and Technology Institute (IETI), Hong Kong)and Wei Liu (Qingdao University, China)
DOI: 10.4018/JOEUC.335082

Keywords: Computer Science & IT / End-User Computing / Human-Computer Interaction / Information Science Reference

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Abstract

When compared to traditional indicators, text information can capture market sentiment, investor confidence, and public opinion more effectively. Meanwhile, the mixed-frequency dynamic factor model (MF-DFM) can capture current changes. In this study, the authors constructed a financial cycle measurement and nowcasting framework by incorporating text information into factors derived from MF-DFM. The findings reveal that, first, the financial cycle indicator (FCI) provides a more detailed and forward-looking perspective on major events. Second, it can serve as an effective “early warning system” by cross-referencing economic indicators. Third, financial cycles exhibit five short cycles, with contraction periods being longer than expansion phases and expansion amplitudes surpassing contractions. Lastly, the analysis suggests a potential turning point in the second half of 2023. This research represents a valuable attempt to integrate big data for more sensitive, timely, and accurate monitoring of financial dynamics.

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Financial Cycle With Text Information Embedding Based on LDA Measurement and Nowcasting

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