Predicting Market Sensitivity: The Role of Board Structure in the Beta Coefficient of Software Companies on the NASDAQ Global Select Market

Year 2025, Issue: 123, 14 - 34, 01.04.2025

Ahmet Akusta

Abstract

This study examines how board structure influences market sensitivity, measured by Beta, in software companies listed on the NASDAQ Global Select Market. Focusing on governance metrics such as board size, meeting frequency, and executive compensation, the research analyzes their impact on Beta from 2014 to 2023. Machine learning models, including Decision Trees and Bagging Classifiers, evaluate this relationship, using accuracy, precision, recall, and F1 scores. Findings suggest that governance factors significantly affect market sensitivity, offering valuable insights for corporate leaders and investors managing firm risk in volatile sectors like software.

Keywords

Beta Coefficient, Corporate Governance, Market Sensitivity Prediction, Board Structure

Piyasa Duyarlılığını Tahmin Etmek: NASDAQ Global Select Market'teki Yazılım Şirketlerinin Beta Katsayısında Yönetim Kurulu Yapısının Rolü

Abstract

Bu çalışma, NASDAQ Global Select Market'te listelenen yazılım şirketlerinde yönetim kurulu yapısının Beta ile ölçülen piyasa duyarlılığını nasıl etkilediğini incelemektedir. Yönetim kurulu büyüklüğü, toplantı sıklığı ve yönetici ücretleri gibi yönetişim ölçütlerine odaklanan araştırma, bunların 2014-2023 yılları arasında Beta üzerindeki etkisini analiz etmektedir. Karar Ağaçları ve Torbalı Sınıflandırıcılar dahil olmak üzere makine öğrenimi modelleri, doğruluk, kesinlik, geri çağırma ve F1 puanlarını kullanarak bu ilişkiyi değerlendirmektedir. Bulgular, yönetişim faktörlerinin piyasa duyarlılığını önemli ölçüde etkilediğini ve yazılım gibi değişken sektörlerde firma riskini yöneten kurumsal liderler ve yatırımcılar için değerli içgörüler sunduğunu göstermektedir.

Keywords

: Beta Katsayısı, Kurumsal Yönetim, Piyasa Duyarlılığı Tahmini, Yönetim Kurulu Yapısı

References

• Aldahmani, S., & Zoubeidi, T. (2020). Graphical group ridge. Journal of Statistical Computation and Simulation, 90(18), 3422–3432. https://doi.org/10.1080/00949655.2020.1803320
• Alessi, L., & Savona, R. (2021). Machine Learning for Financial Stability. 65–87. https://doi.org/10.1007/978-3-030-66891-4_4
• Amin, M., Qasim, M., Afzal, S., & Naveed, K. (2022). New ridge estimators in the inverse Gaussian regression: Monte Carlo simulation and application to chemical data. Communications in Statistics: Simulation and Computation, 51(10), 6170–6187. https://doi.org/10.1080/03610918.2020.1797794
• Arisoy, Y. E., Altay-Salih, A., & Akdeniz, L. (2015). Aggregate volatility expectations and threshold CAPM. North American Journal of Economics and Finance, 34, 231–253. https://doi.org/10.1016/j.najef.2015.09.013
• Berglund, T. (2020). Liquidity and Corporate Governance. Journal of Risk and Financial Management, 13(3). https://doi.org/10.3390/jrfm13030054
• Bhavani, S., & Subhash Chandra, N. (2023). Histogram Based Initial Centroids Selection for K-Means Clustering. In Lecture Notes on Data Engineering and Communications Technologies (Vol. 137, pp. 535–548). https://doi.org/10.1007/978-981-19-2600-6_38
• Cai, C., & Wang, L. (2020). Application of improved k-means k-nearest neighbor algorithm in the movie recommendation system. Proceedings - 2020 13th International Symposium on Computational Intelligence and Design, ISCID 2020, 314–317. https://doi.org/10.1109/ISCID51228.2020.00076
• Chaudhary, P. (2021). Impact of board structure, board activities and institutional investors on the firm risk: evidence from India. Managerial Finance, 47(4), 506–524. https://doi.org/10.1108/MF-05-2020-0281
• Elumaro, A., & Ibrahim, U. A. (2023). Effect of Corporate Governance on the Financial Performance of Listed Consumer Goods Firms in Nigeria. African Journal of Business and Economic Research, 18(4), 143–163. https://doi.org/10.31920/1750-4562/2023/v18n4a7
• Enalpe, M. (2022). Corporate Social Responsibility Activities and Impact on Firm Value: The Case of the Technology Company Group. Springer Proceedings in Business and Economics, 19–46. https://doi.org/10.1007/978-3-030-81663-6_2
• Enders, W. (2010). Applied Econometric Time Series-Wiley. Wiley. https://www.wiley.com/en-au/Applied+Econometric+Time+Series%2C+4th+Edition-p-9781118808566
• Fama, E. F., & French, K. R. (1992). The Cross‐Section of Expected Stock Returns. The Journal of Finance, 47(2), 427–465. https://doi.org/10.1111/j.1540-6261.1992.tb04398.x
• Gerged, A. M., Yao, S., & Albitar, K. (2023). Board composition, ownership structure and financial distress: insights from UK FTSE 350. Corporate Governance (Bingley), 23(3), 628–649. https://doi.org/10.1108/CG-02-2022-0069
• Giot, P. (2005). Relationships between implied volatility indexes and stock index returns. Journal of Portfolio Management, 31(3), 92-100+6. https://doi.org/10.3905/jpm.2005.500363
• Goel, A., Dhiman, R., Rana, S., & Srivastava, V. (2022). Board composition and firm performance: empirical evidence from Indian companies. Asia-Pacific Journal of Business Administration, 14(4), 771–789. https://doi.org/10.1108/APJBA-09-2021-0483
• Hermadi, I., Nurhadryani, Y., Ranggadara, I., & Amin, R. (2020). A Review of Contribution and Challenge in Predictive Machine Learning Model at Financial Industry. Journal of Physics Conference Series, 1477(3), 032021. https://doi.org/10.1088/1742-6596/1477/3/032021
• Jeantet, I., Miklós, Z., & Gross-Amblard, D. (2020). Overlapping Hierarchical Clustering (OHC). Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12080 LNCS, 261–273. https://doi.org/10.1007/978-3-030-44584-3_21
• Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305–360. https://doi.org/10.1016/0304-405X(76)90026-X
• Jin, Y., & Xiao, F. (2013). Eliminating error accumulation in hierarchical clustering algorithms. Proceedings - 4th International Conference on Emerging Intelligent Data and Web Technologies, EIDWT 2013, 639–642. https://doi.org/10.1109/EIDWT.2013.115
• Kandula, A. R., Divya, K., Movva, S. R. K. D., Motineni, M. S., Pappala, H., & Jakkireddy, K. V. S. R. (2024). Comparative Analysis for Loan Approval Prediction System Using Machine Learning Algorithms. Lecture Notes in Networks and Systems, 897, 205–215. https://doi.org/10.1007/978-981-99-9704-6_18
• Koerniadi, H., Krishnamurti, C., & Tourani-Rad, A. (2014). Corporate governance and risk-taking in New Zealand. Australian Journal of Management, 39(2), 227–245. https://doi.org/10.1177/0312896213478332
• Lamba, A. K., Sharma, P., Kumar, R., Khullar, V., Kansal, I., & Popli, R. (2024). The Nasdaq Composite Index Prediction Using LSTM and Bi-LSTM Multivariate Deep Learning Approaches. Lecture Notes in Networks and Systems, 1047 LNNS, 78–85. https://doi.org/10.1007/978-3-031-64836-6_8
• Li, Z., Wang, Z., & Gao, X. (2024). Corporate governance and capital market risk: New evidence from firm-specific measures among Chinese listed companies. Journal of Infrastructure, Policy and Development, 8(9). https://doi.org/10.24294/jipd.v8i9.7413
• Maheshwari, S., & Naik, D. R. (2024). Efficiency in Operations of NASDAQ Listed Technology Companies from 2011 to 2023. Journal of Risk and Financial Management, 17(5). https://doi.org/10.3390/jrfm17050205
• Malhotra, R., Malhotra, D. K., & Malhotra, K. (2024). Predicting Credit Outlook of Banking and Nonbanking Finance Companies: A Comparative Analysis of Machine Learning Models. Journal of Financial Data Science, 6(3), 214–251. https://doi.org/10.3905/jfds.2024.1.158
• Mishra, R. K., & Kapil, S. (2018). Effect of board characteristics on firm value: evidence from India. South Asian Journal of Business Studies, 7(1), 41–72. https://doi.org/10.1108/SAJBS-08-2016-0073
• Miura, R., Pichl, L., & Kaizoji, T. (2019). Artificial Neural Networks for Realized Volatility Prediction in Cryptocurrency Time Series. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11554 LNCS, 165–172. https://doi.org/10.1007/978-3-030-22796-8_18
• Montevechi, A. A., Miranda, R. D. C., Medeiros, A. L., & Montevechi, J. A. B. (2024). Advancing credit risk modelling with Machine Learning: A comprehensive review of the state-of-the-art. Engineering Applications of Artificial Intelligence, 137. https://doi.org/10.1016/j.engappai.2024.109082
• Ongore, V. O., K’Obonyo, P. O., Ogutu, M., & Bosire, E. M. (2015). Board composition and financial performance: Empirical analysis of companies listed at the Nairobi securities exchange. International Journal of Economics and Financial Issues, 5(1), 23–43. https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979837466&partnerID=40&md5=716f805e3b82cc21b0748ffb48c69540
• Pandharbale, P. B., Choudhury, S., Mohanty, S. N., & Jagadev, A. K. (2021). Web Benefit Utilizations with K-means Clustering Approach for Efficient Clustering. CEUR Workshop Proceedings, 3283, 1–9. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143202146&partnerID=40&md5=7fa25010bccc6f7d6b21fc4de71ec99f
• Pearce, J. A., & Patel, P. C. (2018). Board of director efficacy and firm performance variability. Long Range Planning, 51(6), 911–926. https://doi.org/10.1016/j.lrp.2017.12.001
• Raju, G. S. B., Manasa, C., Bhavani, N. D., Amulya, J., & Shirisha, D. (2023). Comparative Analysis of Different Machine Learning Algorithms on Different Datasets. Proceedings of the 7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023, 104–109. https://doi.org/10.1109/ICICCS56967.2023.10142906
• Rama Iyer, S., Sankaran, H., & Walsh, S. T. (2020). Influence of Director Expertise on Capital Structure and Cash Holdings in High-Tech Firms. Technological Forecasting and Social Change, 158. https://doi.org/10.1016/j.techfore.2020.120060
• Shanthi, A., & Thamilselvan, R. (2019). Modelling and forecasting volatility for BSE and NSE stock index: Linear vs. Nonlinear approach. Afro-Asian Journal of Finance and Accounting, 9(4), 363–380. https://doi.org/10.1504/AAJFA.2019.102995
• Sharma, R. B., Al-Jalahma, A., Kukreja, G., & Almoosawi, Z. S. M. (2023). Impact of Corporate Governance on Financial Performance: Empirical Evidence of the Selected Telecommunication Companies of GCC. Lecture Notes in Networks and Systems, 621 LNNS, 643–655. https://doi.org/10.1007/978-3-031-26956-1_60
• Sherif, M., El-Diftar, D., & Shahwan, T. (2024). Do Internal Corporate Governance Practices Influence Stock Price Volatility? Evidence from Egyptian Non-Financial Firms. Journal of Risk and Financial Management, 17(6). https://doi.org/10.3390/jrfm17060243
• Suleymanov, E., Gubadli, M., & Yagubov, U. (2024). Test of Volatile Behaviors with the Asymmetric Stochastic Volatility Model: An Implementation on Nasdaq-100. Risks, 12(5). https://doi.org/10.3390/risks12050076
• Tadele, H., Ruan, X., & Li, W. (2022). Corporate governance and firm-level jump and volatility risks. Applied Economics, 54(22), 2529–2553. https://doi.org/10.1080/00036846.2021.1998325
• Tan, M., & Liu, B. (2016). CEO’s managerial power, board committee memberships and idiosyncratic volatility. International Review of Financial Analysis, 48, 21–30. https://doi.org/10.1016/j.irfa.2016.09.003
• Tang, W., Nguyen, H. H., & Nguyen, V. T. (2013). The effects of listing changes between NASDAQ market segments. Journal of Economics and Finance, 37(4), 584–605. https://doi.org/10.1007/s12197-011-9198-3
• Tran, T. K., Senkerik, R., Hanh, V. T. X., Huan, V. M., Ulrich, A., Musil, M., & Zelinka, I. (2023). Initial Coin Offering Prediction Comparison Using Ridge Regression, Artificial Neural Network, Random Forest Regression, and Hybrid ANN-Ridge. Mendel, 29(2), 283–294. https://doi.org/10.13164/mendel.2023.2.283
• Tsai, C.-F., Lu, Y.-H., Hung, Y.-C., & Yen, D. C. (2016). Intangible assets evaluation: The machine learning perspective. Neurocomputing, 175(PartA), 110–120. https://doi.org/10.1016/j.neucom.2015.10.041
• Tsay, R. S. (2005). Analysis of financial time series. 605. https://www.wiley.com/en-us/Analysis+of+Financial+Time+Series%2C+2nd+Edition-p-9780471746188
• Turel, O., Liu, P., & Bart, C. (2019). Is board IT governance a silver bullet? A capability complementarity and shaping view. International Journal of Accounting Information Systems, 33, 32–46. https://doi.org/10.1016/j.accinf.2019.03.002
• Wang, T., & Hsu, C. (2013). Board composition and operational risk events of financial institutions. Journal of Banking and Finance, 37(6), 2042–2051. https://doi.org/10.1016/j.jbankfin.2013.01.027
• Yasser, Q. R., Mamun, A. A., & Rodrigs, M. (2017). Impact of board structure on firm performance: evidence from an emerging economy. Journal of Asia Business Studies, 11(2), 210–228. https://doi.org/10.1108/JABS-06-2015-0067
• Yermack, D. (1996). Higher market valuation of companies with a small board of directors. Journal of Financial Economics, 40(2), 185–211. https://doi.org/10.1016/0304-405X(95)00844-5
• Younas, Z. I., Klein, C., Trabert, T., & Zwergel, B. (2019). Board composition and corporate risk-taking: a review of listed firms from Germany and the USA. Journal of Applied Accounting Research, 20(4), 526–542. https://doi.org/10.1108/JAAR-01-2018-0014
• Yuan, F., Hussain, R. T., Khalid, I., & Li, M. (2023). Does board activeness strengthen the relationship between structure of corporate ownership and firm performance? Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.1104178