Discovering causal relationships among financial variables associated with firm value using a dynamic Bayesian network
This study investigated the causal relationships among financial variables associated with firm value using a Causal Dynamic Bayesian Network (CDBN), which is an extension of the basic Bayesian network that captures both temporal and contemporaneous causal relationships. The CDBN model was construct...
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| Format: | Article |
| Language: | English |
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AIMS Press
2025-01-01
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| Series: | Data Science in Finance and Economics |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/DSFE.2025001 |
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| _version_ | 1850125218086912000 |
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| author | Ji Young Choi Chae Young Lee Man-Suk Oh |
| author_facet | Ji Young Choi Chae Young Lee Man-Suk Oh |
| author_sort | Ji Young Choi |
| collection | DOAJ |
| description | This study investigated the causal relationships among financial variables associated with firm value using a Causal Dynamic Bayesian Network (CDBN), which is an extension of the basic Bayesian network that captures both temporal and contemporaneous causal relationships. The CDBN model was constructed using a panel dataset of listed manufacturing companies in Korea over a 14-year period (2009–2022). By visualizing the interactions between financial factors, the model makes it easy to understand their dynamic and instantaneous relationships, offering valuable insights into corporate finance. Key findings in the model include evidence of autocorrelation in all dynamic variables, a lagged feedback loop between the intangible assets ratio and firm value, the widespread impact of the COVID-19 pandemic on the financial sector, and important causal relationships involving key financial metrics such as the fixed assets ratio, firm value, and return on assets ratio. |
| format | Article |
| id | doaj-art-c2b3d69c9a0d4de1b1211c4fbfc56cf8 |
| institution | OA Journals |
| issn | 2769-2140 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Data Science in Finance and Economics |
| spelling | doaj-art-c2b3d69c9a0d4de1b1211c4fbfc56cf82025-08-20T02:34:09ZengAIMS PressData Science in Finance and Economics2769-21402025-01-015111810.3934/DSFE.2025001Discovering causal relationships among financial variables associated with firm value using a dynamic Bayesian networkJi Young Choi0Chae Young Lee1Man-Suk Oh2Department of Statistics, Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul 03760, KoreaDepartment of Statistics, Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul 03760, KoreaDepartment of Statistics, Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul 03760, KoreaThis study investigated the causal relationships among financial variables associated with firm value using a Causal Dynamic Bayesian Network (CDBN), which is an extension of the basic Bayesian network that captures both temporal and contemporaneous causal relationships. The CDBN model was constructed using a panel dataset of listed manufacturing companies in Korea over a 14-year period (2009–2022). By visualizing the interactions between financial factors, the model makes it easy to understand their dynamic and instantaneous relationships, offering valuable insights into corporate finance. Key findings in the model include evidence of autocorrelation in all dynamic variables, a lagged feedback loop between the intangible assets ratio and firm value, the widespread impact of the COVID-19 pandemic on the financial sector, and important causal relationships involving key financial metrics such as the fixed assets ratio, firm value, and return on assets ratio.https://www.aimspress.com/article/doi/10.3934/DSFE.2025001firm valuedynamic bayesian networkcausalitypanel datalagged effect |
| spellingShingle | Ji Young Choi Chae Young Lee Man-Suk Oh Discovering causal relationships among financial variables associated with firm value using a dynamic Bayesian network Data Science in Finance and Economics firm value dynamic bayesian network causality panel data lagged effect |
| title | Discovering causal relationships among financial variables associated with firm value using a dynamic Bayesian network |
| title_full | Discovering causal relationships among financial variables associated with firm value using a dynamic Bayesian network |
| title_fullStr | Discovering causal relationships among financial variables associated with firm value using a dynamic Bayesian network |
| title_full_unstemmed | Discovering causal relationships among financial variables associated with firm value using a dynamic Bayesian network |
| title_short | Discovering causal relationships among financial variables associated with firm value using a dynamic Bayesian network |
| title_sort | discovering causal relationships among financial variables associated with firm value using a dynamic bayesian network |
| topic | firm value dynamic bayesian network causality panel data lagged effect |
| url | https://www.aimspress.com/article/doi/10.3934/DSFE.2025001 |
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