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.
Li, P.; Peng, X.; Zhang, C.; Baležentis, T. 2023. Financial Cycle With Text Information Embedding Based on LDA Measurement and Nowcasting. Journal of Organizational and End User Computing (JOEUC). ISSN 1546-2234. eISSN 1546-5012. 36, 1, p. 1–-25. DOI: 10.4018/JOEUC.335082. [Scopus; Citation Index Expanded (Web of Science).