Board Expertise Background and Firm Performance
This study presents a novel financial performance forecasting method that combines the threshold technique with Artificial Neural Networks (ANN). It applies the threshold regression method to identify the factors within the board of directors that influence the financial performance of traditional i...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-02-01
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| Series: | International Journal of Financial Studies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7072/12/1/17 |
| _version_ | 1797240733781983232 |
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| author | Chiou-Yann Lee Chun-Ru Wen Binh Thi-Thanh-Nguyen |
| author_facet | Chiou-Yann Lee Chun-Ru Wen Binh Thi-Thanh-Nguyen |
| author_sort | Chiou-Yann Lee |
| collection | DOAJ |
| description | This study presents a novel financial performance forecasting method that combines the threshold technique with Artificial Neural Networks (ANN). It applies the threshold regression method to identify the factors within the board of directors that influence the financial performance of traditional industries in Taiwan. The findings indicate that the ANN method effectively predicts financial performance by using relevant board structure data. Furthermore, the empirical results suggest that boards with more members demonstrate increased profitability. Additionally, a more significant presence of board members with accounting expertise contributes to more consistent profits. In contrast, an increased presence of members with financial expertise has a more pronounced impact on profitability. |
| first_indexed | 2024-04-24T18:12:08Z |
| format | Article |
| id | doaj.art-30a6f46f13c34bba8c77f7d681de6a69 |
| institution | Directory Open Access Journal |
| issn | 2227-7072 |
| language | English |
| last_indexed | 2024-04-24T18:12:08Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Financial Studies |
| spelling | doaj.art-30a6f46f13c34bba8c77f7d681de6a692024-03-27T13:44:47ZengMDPI AGInternational Journal of Financial Studies2227-70722024-02-011211710.3390/ijfs12010017Board Expertise Background and Firm PerformanceChiou-Yann Lee0Chun-Ru Wen1Binh Thi-Thanh-Nguyen2Department of Accounting, Chaoyang University of Technology, 168 Jifong E. Road, Wufong District, Taichung City 41349, TaiwanDepartment of Accounting, Chaoyang University of Technology, 168 Jifong E. Road, Wufong District, Taichung City 41349, TaiwanDepartment of Accounting, Chaoyang University of Technology, 168 Jifong E. Road, Wufong District, Taichung City 41349, TaiwanThis study presents a novel financial performance forecasting method that combines the threshold technique with Artificial Neural Networks (ANN). It applies the threshold regression method to identify the factors within the board of directors that influence the financial performance of traditional industries in Taiwan. The findings indicate that the ANN method effectively predicts financial performance by using relevant board structure data. Furthermore, the empirical results suggest that boards with more members demonstrate increased profitability. Additionally, a more significant presence of board members with accounting expertise contributes to more consistent profits. In contrast, an increased presence of members with financial expertise has a more pronounced impact on profitability.https://www.mdpi.com/2227-7072/12/1/17artificial neural networksmulti-threshold modelfinancial performance |
| spellingShingle | Chiou-Yann Lee Chun-Ru Wen Binh Thi-Thanh-Nguyen Board Expertise Background and Firm Performance International Journal of Financial Studies artificial neural networks multi-threshold model financial performance |
| title | Board Expertise Background and Firm Performance |
| title_full | Board Expertise Background and Firm Performance |
| title_fullStr | Board Expertise Background and Firm Performance |
| title_full_unstemmed | Board Expertise Background and Firm Performance |
| title_short | Board Expertise Background and Firm Performance |
| title_sort | board expertise background and firm performance |
| topic | artificial neural networks multi-threshold model financial performance |
| url | https://www.mdpi.com/2227-7072/12/1/17 |
| work_keys_str_mv | AT chiouyannlee boardexpertisebackgroundandfirmperformance AT chunruwen boardexpertisebackgroundandfirmperformance AT binhthithanhnguyen boardexpertisebackgroundandfirmperformance |