DETERMINING THE OPTIMAL NUMBER OF BOARD MEMBERS: IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORKS

Abstract

Purpose- The goal of this research is to delve into the complexities of board structure and composition within firms. Specifically, it aims to examine how various factors such as firm performance and firm-based play a role in determining the most appropriate number of board members. Methodology- A neural network model is created to identify the ideal number of board members based on financial performance metrics. Financial performance indicators (return on assets, return on equity, profits per share, and market to book value ratio) and firm-based variables compose the model's input layer (company age, company size, total sales, and leverage). The output layer displays the ideal number of board members for each organization. The model's design has one or more hidden layers to represent the intricate interactions between the input variables and the desired output. Findings- As compared to the other factors, the significance of the return on assets variable as a predictor is much higher. At least one of the intervals is affected by each of the eight factors, and each of those eight variables has a statistically significant influence. Conclusion- Through a comprehensive analysis and review of existing literature, the study intends to shed light on the interplay between these factors and their impact on board effectiveness and decision-making. By exploring the relationship between firm-based factors and board composition, the research hopes to provide valuable insights and recommendations for firms looking to optimize their governance structure and improve their overall performance.

Keywords

• Board size
• optimal number of board members
• artificial neural network
• return on assets

References

1. Abdul Manaf, K. B., Amran, N. A., & Zainol Abidin, A. (2014). Board size and accounting conservatism of Malaysian listed firms. Australian Journal of Basic and Applied Sciences, 8(23), 207-211.
2. Boone, A. L., Field, L. C., Karpoff, J. M., & Raheja, C. G. (2007). The determinants of corporate board size and composition: An empirical analysis. Journal of financial Economics, 85(1), 66-101.
3. Boozang, K. M. (2007). Does an independent board improve nonprofit corporate governance. Tennessee Literature Review, 75, 83-94.
4. Chen, C. H., & Al-Najjar, B. (2012). The determinants of board size and independence: Evidence from China. International Business Review, 21(5), 831-846.
5. Cheng, S. (2008). Board size and the variability of corporate performance. Journal of Financial Economics, 87(1), 157-176.
6. Hahn, P. D., & Lasfer, M. (2016). Impact of foreign directors on board meeting frequency. International Review of Financial Analysis, 46, 295-308.
7. Karami, G., & BeikBoshrouyeh, S. (2011). Corporate governance and equity valuation: the model by using artificial neural network. Accounting and Auditing Review, 18(64), 129-150.
8. Almagtome, A., Khaghaany, M., & Önce, S. (2020). Corporate governance quality, stakeholders' pressure, and sustainable development: An integrated approach. International Journal of Mathematıcal Engıneerıng and Management Sciences, 5(6), 321-340.

9. Larmou, S., & Vafeas, N. (2010). The relation between board size and firm performance in firms with a history of poor operating performance. Journal of Management & Governance, 14, 61-85.
10. Lin, T. T., & Lee, Y. C. (2008). Organizational characteristics, board size and corporate performance. Journal of Global Business Management, 4(2), 338-347.
11. Raheja, C. G. (2005). Determinants of board size and composition: A theory of corporate boards. Journal of Financial and Quantitative Analysis, 40(2), 283-306.
12. Yermack, D. (1996). Higher market valuation of companies with a small board of directors. Journal of Financial Economics, 40(2), 185-211.
13. Zakaria, M., Mabrouka, A. S., & Sarhan, S. (2014). Artificial neural network: a brief overview. Neural Networks, 1, 2-12.