Skip to content
AI Journal of BUsiness AI Journal of BUsiness AI Journal of BUsiness AI Journal of BUsiness
Home

Article

Development of a Machine Learning-based Corporate Management Strategy Prediction Model Reflecting ESG Rating Information

Kyung Gu Kang Primary Contact
PDF
Abstract

In this study, the model reflected not only financial data but also corporate ESG rating information to predict corporate management strategies. Five Machine learning classifiers such as RF, SVM, XGBoost, LightGBM, and CatBoost were used to develop a model that predicts management strategies using machine learning classification techniques. The research results of this paper are as follows. First, the model that used financial data and ESG rating information together showed better prediction performance than the prediction model that used only financial data. In particular, the best performance was shown when the ESG rating was included, followed by the high performance in the management strategy prediction model in the order of governance, environmental, and social ratings. This means that ESG rating information is important in predicting corporate management strategies. Second, among the five machine learning classifiers, LightGBM performed the best when predicting corporate management strategies, followed by CatBoost, XGBoost, SVM, and RF. These results suggest that the Boost classifier is effective in predicting management strategies. The corporate management strategy prediction model developed in this study can be a useful tool for promoting the sustainable development of a company and is expected to provide important insights to managers and investors. In addition, these models are expected to contribute to improving a company's long-term performance by supporting the establishment of sustainable management strategies.

References
  1. 고세훈, 유왕진, & 이윤보. (2013). 중소벤처기업의 경쟁전략과 경영성과 간의 구조적 관계 에 관한 실증연구. 생산성연구: 국제융합학술지, 27(1), 225-260.
  2. 공석진, & 양해술. (2014). 경영전략이 경영성과에 미치는 영향. Journal of Digital Convergence, 12(1), 177-192.
  3. 남정민, & 박준혁. (2022). 벤처기업의 ESG 경영전략 도출을 위한 계층분석과정 (AHP) 적용. Entrpreneurship & ESG 연구, 2(1), 1-25.
  4. 박다인, & 박찬희. (2018). 벤처기업의 성장단계별 기업경쟁력 및 기업 성과 창출 전략. 벤처창업연구, 13(6), 177-189.
  5. 윤성용. (2023). 경영전략이 ESG 와 기업가치와의 관계에 미치는 영향. 비즈니스융복합연구, 8(4), 33-38.
  6. 윤종록, & 김형철. (2009). 벤처기업의 창업가특성과 차별화전략이 경영성과에 미치는 영향에 관한 연구. 대한경영학회지, 22(6), 3693-3721.
  7. 주성한, & 정남호. (2023). 호텔기업의 지식경영전략이 종사원의 몰입과 직무유지의도에 미치는 영향. 인터넷전자상거래연구, 23(3), 205-231.
  8. 최규담. (2016). 경영전략과 발생액의 질. 회계저널, 25(3), 261-305.
  9. 추승엽, 유정민, & 임성준. (2009). 경영환경, 경쟁전략 및 기업 내부역량 간의 적합성이 성과에 미치는 영향에 관한 연구. 전략경영연구, 12(1), 101-126.
  10. 한규동. (2019). 벤처기업 창업가의 특성과 경영전략이 경영성과에 미치는 영향. 벤처창업연구, 14(6), 29-43.
  11. 홍난희. (2024). 경영전략과 투자부동산 그리고 기업가치. 대한경영학회지, 37(4), 611-630.
  12. Abraham, F., Cortina Lorente, J. J., & Schmukler, S. L. (2020). Growth of global corporate debt: Main facts and policy challenges. World Bank Policy Research Working Paper, (9394).
  13. Akmaeva, R., Arykbaev, R., Epifanova, N., & Glinchevskiy, E. (2020). Influence of digitalization upon formation of corporate strategy and new business models of modern organizations. In SHS Web of Conferences (Vol. 89, p. 03003). EDP Sciences.
  14. Al‐Najjar, B., & Anfimiadou, A. (2012). Environmental policies and firm value. business Strategy and the Environment, 21(1), 49-59.
  15. Al‐Shaer, H., Kuzey, C., Uyar, A., & Karaman, A. S. (2023). Corporate strategy, board composition, and firm value. International Journal of Finance & Economics.
  16. Asselman, A., Khaldi, M., & Aammou, S. (2023). Enhancing the prediction of student performance based on the machine learning XGBoost algorithm. Interactive Learning Environments, 31(6), 3360-3379.
  17. Avanesova, N., Tahajuddin, S., Hetman, O., Serhiienko, Y., & Makedon, V. (2021). Strategic management in the system model of the corporate enterprise organizational development. Economics and Finance, 1(2021), 9.
  18. Aydoğmuş, M., Gülay, G., & Ergun, K. (2022). Impact of ESG performance on firm value and profitability. Borsa Istanbul Review, 22, S119-S127.
  19. Behl, A., Kumari, P. R., Makhija, H., & Sharma, D. (2022). Exploring the relationship of ESG score and firm value using cross-lagged panel analyses: Case of the Indian energy sector. Annals of Operations Research, 313(1), 231-256.
  20. Bentley, K. A., Omer, T. C., & Sharp, N. Y. (2013). management strategy, financial reporting irregularities, and audit effort. Contemporary accounting research, 30(2), 780-817.
  21. Bowman, C., & Ambrosini, V. (2007). Firm value creation and levels of strategy. Management Decision, 45(3), 360-371.
  22. Bowman, E. H., & Helfat, C. E. (2001). Does corporate strategy matter?. Strategic management journal, 22(1), 1-23.
  23. Brewster, C. (2017). The integration of human resource management and corporate strategy. Policy and practice in European human resource management, 22-35.
  24. Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. (2020). A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408, 189-215.
  25. Cuevas-Rodriguez, G., Guerrero-Villegas, J., & Valle-Cabrera, R. (2016). Corporate governance changes, firm strategy and compensation mechanisms in a privatization context. Journal of Organizational Change Management, 29(2), 199-221.
  26. D'Amato, A., & Falivena, C. (2020). Corporate social responsibility and firm value: Do firm size and age matter? Empirical evidence from European listed companies. Corporate Social Responsibility and Environmental Management, 27(2), 909-924.
  27. Dewi, N. W. S. K., Ratnadi, N. M. D., Yadnyana, I. K., & Suaryana, I. G. N. A. (2022). Selection of profit management strategy: Testing at the company life cycle stage. Linguistics and Culture Review, 6(S1), 530-549.
  28. Gholizadeh, M., Jamei, M., Ahmadianfar, I., & Pourrajab, R. (2020). Prediction of nanofluids viscosity using random forest (RF) approach. Chemometrics and Intelligent Laboratory Systems, 201, 104010.
  29. González-Ramos, M. I., Guadamillas, F., & Donate, M. J. (2023). The relationship between knowledge management strategies and corporate social responsibility: Effects on innovation capabilities. Technological Forecasting and Social Change, 188, 122287.
  30. Grant, R. M. (2002). Corporate strategy: managing scope and strategy content. Handbook of strategy and management, 72-97.
  31. Gunarathne, A. N., Lee, K. H., & Hitigala Kaluarachchilage, P. K. (2021). Institutional pressures, environmental management strategy, and organizational performance: The role of environmental management accounting. management Strategy and the Environment, 30(2), 825-839.
  32. Hancock, J. T., & Khoshgoftaar, T. M. (2020). CatBoost for big data: an interdisciplinary review. Journal of big data, 7(1), 94.
  33. Higgins, D., Omer, T. C., & Phillips, J. D. (2015). The influence of a firm's management strategy on its tax aggressiveness. Contemporary Accounting Research, 32(2), 674-702.
  34. Ibrahim, A. A., Ridwan, R. L., Muhammed, M. M., Abdulaziz, R. O., & Saheed, G. A. (2020). Comparison of the CatBoost classifier with other machine learning methods. International Journal of Advanced Computer Science and Applications, 11(11).
  35. Jabeur, S. B., Gharib, C., Mefteh-Wali, S., & Arfi, W. B. (2021). CatBoost model and artificial intelligence techniques for corporate failure prediction. Technological Forecasting and Social Change, 166, 120658.
  36. Junius, D., Adisurjo, A., Rijanto, Y. A., & Adelina, Y. E. (2020). The impact of ESG performance to firm performance and market value. Jurnal Aplikasi Akuntansi, 5(1), 21-41.
  37. Khajavi, H., & Rastgoo, A. (2023). Predicting the carbon dioxide emission caused by road transport using a Random Forest (RF) model combined by Meta-Heuristic Algorithms. Sustainable Cities and Society, 93, 104503.
  38. Khozeimeh, F., Sharifrazi, D., Izadi, N. H., Joloudari, J. H., Shoeibi, A., Alizadehsani, R., ... & Islam, S. M. S. (2022). RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance. Scientific reports, 12(1), 11178.
  39. Kumari, R., & Srivastava, S. K. (2017). Machine learning: A review on binary classification. International Journal of Computer Applications, 160(7).
  40. Kurani, A., Doshi, P., Vakharia, A., & Shah, M. (2023). A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting. Annals of Data Science, 10(1), 183-208.
  41. Liu, J., Gao, Y., & Hu, F. (2021). A fast network intrusion detection system using adaptive synthetic oversampling and LightGBM. Computers & Security, 106, 102289.
  42. Lopez-Martinez, F., Schwarcz, A., Núñez-Valdez, E. R., & Garcia-Diaz, V. (2018). Machine learning classification analysis for a hypertensive population as a function of several risk factors. Expert Systems with Applications, 110, 206-215.
  43. Lubatkin, M. H., Lane, P. J., & Schulze, W. S. (2005). A strategic management model of agency relationships in firm governance. The Blackwell handbook of strategic management, 225-254.
  44. Luo, M., Wang, Y., Xie, Y., Zhou, L., Qiao, J., Qiu, S., & Sun, Y. (2021). Combination of feature selection and catboost for prediction: The first application to the estimation of aboveground biomass. Forests, 12(2), 216.
  45. Maxwell, A. E., Warner, T. A., & Fang, F. (2018). Implementation of machine-learning classification in remote sensing: An applied review. International journal of remote sensing, 39(9), 2784-2817.
  46. Menz, M., Kunisch, S., Birkinshaw, J., Collis, D. J., Foss, N. J., Hoskisson, R. E., & Prescott, J. E. (2021). Corporate Strategy and the Theory of the Firm in the Digital Age. Journal of Management Studies, 58(7), 1695-1720.
  47. Narayanan, B. N., Djaneye-Boundjou, O., & Kebede, T. M. (2016, July). Performance analysis of machine learning and pattern recognition algorithms for malware classification. In 2016 IEEE national aerospace and electronics conference (NAECON) and ohio innovation summit (OIS) (pp. 338-342). IEEE.
  48. Osisanwo, F. Y., Akinsola, J. E. T., Awodele, O., Hinmikaiye, J. O., Olakanmi, O., & Akinjobi, J. (2017). Supervised machine learning algorithms: classification and comparison. International Journal of Computer Trends and Technology (IJCTT), 48(3), 128-138.
  49. Pirscoveanu, R. S., Hansen, S. S., Larsen, T. M., Stevanovic, M., Pedersen, J. M., & Czech, A. (2015, June). Analysis of malware behavior: Type classification using machine learning. In 2015 International conference on cyber situational awareness, data analytics and assessment (CyberSA) (pp. 1-7). IEEE.
  50. Pisner, D. A., & Schnyer, D. M. (2020). Support vector machine. In Machine learning (pp. 101-121). Academic Press.
  51. Qiu, Y., Zhou, J., Khandelwal, M., Yang, H., Yang, P., & Li, C. (2022). Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration. Engineering with Computers, 38(Suppl 5), 4145-4162.
  52. Quintiliani, A. (2022). ESG and firm value. Accounting and Finance Research, 11(4), 37.
  53. Roy, A., & Chakraborty, S. (2023). Support vector machine in structural reliability analysis: A review. Reliability Engineering & System Safety, 233, 109126.
  54. Rufo, D. D., Debelee, T. G., Ibenthal, A., & Negera, W. G. (2021). Diagnosis of diabetes mellitus using gradient boosting machine (LightGBM). Diagnostics, 11(9), 1714.
  55. Sagi, O., & Rokach, L. (2021). Approximating XGBoost with an interpretable decision tree. Information sciences, 572, 522-542.
  56. Shafiq, M., Yu, X., Laghari, A. A., Yao, L., Karn, N. K., & Abdessamia, F. (2016, October). Network traffic classification techniques and comparative analysis using machine learning algorithms. In 2016 2nd IEEE International Conference on Computer and Communications (ICCC) (pp. 2451-2455). IEEE.
  57. Soofi, A. A., & Awan, A. (2017). Classification techniques in machine learning: applications and issues. Journal of Basic & Applied Sciences, 13, 459-465.
  58. Wang, D. N., Li, L., & Zhao, D. (2022). Corporate finance risk prediction based on LightGBM. Information Sciences, 602, 259-268.
  59. Wang, P., Fan, E., & Wang, P. (2021). Comparative analysis of image classification algorithms based on traditional machine learning and deep learning. Pattern recognition letters, 141, 61-67.
  60. Wen, H., Zhong, Q., & Lee, C. C. (2022). Digitalization, competition strategy and corporate innovation: Evidence from Chinese manufacturing listed companies. International Review of Financial Analysis, 82, 102166.
  61. Wongvibulsin, S., Wu, K. C., & Zeger, S. L. (2020). Clinical risk prediction with random forests for survival, longitudinal, and multivariate (RF-SLAM) data analysis. BMC medical research methodology, 20, 1-14.
  62. Yan, J., Xu, Y., Cheng, Q., Jiang, S., Wang, Q., Xiao, Y., ... & Wang, X. (2021). LightGBM: accelerated genomically designed crop breeding through ensemble learning. Genome biology, 22, 1-24.
  63. Yu, X., & Xiao, K. (2022). Does ESG performance affect firm value? Evidence from a new ESG-scoring approach for Chinese enterprises. Sustainability, 14(24), 16940.
  64. Yuan, Y., Lu, L. Y., Tian, G., & Yu, Y. (2020). management strategy and corporate social responsibility. Journal of management ethics, 162, 359-377.
  65. Zhang, F., Qin, X., & Liu, L. (2020). The interaction effect between ESG and green innovation and its impact on firm value from the perspective of information disclosure. Sustainability, 12(5), 1866.
  66. Zhang, J., Ma, X., Zhang, J., Sun, D., Zhou, X., Mi, C., & Wen, H. (2023). Insights into geospatial heterogeneity of landslide susceptibility based on the SHAP-XGBoost model. Journal of environmental management, 332, 117357.
  67. Zimon, G., Nakonieczny, J., Chudy-Laskowska, K., Wójcik-Jurkiewicz, M., & Kochański, K. (2021). An analysis of the financial liquidity management strategy in construction companies operating in the Podkarpackie Province. Risks, 10(1), 5.
Keywords
management strategy machine learning management strategy prediction ESG rating
Details
Section Articles
Issue Vol. 1 No. 2 (2025): Volume 1 Issue 2 (May 2025)
Published 2025-05-30
Pages 36-55
Authors
Kyung Gu Kang Primary Contact

  • Ph. D in AI Strategy Management
  • Research Professor, The Institute for Industrial Policy Studies (IPS)
  • Chairman, Commerce Holdings Inc.
  • Areas of Interest: Received a Ph.D in research on predicting future corporate value using ESG ratings, and is cu rrently conducting research on global management, GPU Cloud System,AI management strategy, and ESG investment strategy.
License
How to Cite
Development of a Machine Learning-based Corporate Management Strategy Prediction Model Reflecting ESG Rating Information. (2025). AI Journal of BUsiness, 1(2), 36-55. https://jnl.ampla.page/aijb/article/view/117