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korean management review - Vol. 53 , No. 2
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[ Article ]
korean management review - Vol. 53, No. 2, pp. 345-383
Abbreviation: kmr
ISSN: 1226-1874 (Print)
Print publication date 30 Apr 2024
Received 23 Aug 2023 Revised 25 Jan 2024 Accepted 08 Feb 2024
DOI: https://doi.org/10.17287/kmr.2024.53.2.345

A Study on the Development of Future Corporate Value Forecasting Classifier Reflecting ESG Information
Kyung Gu Kang ; Hyung Jong Na ; Cheong Yeul Park
(First Author) Research Professor, The Institute for Industrial Policy Studies, IPS (thomas3877@naver.com)
(Corresponding Author) Professor, Department of Accounting and Taxation at Semyung University (freshna77@semyung.ac.kr)
(Co-Author) Associate Professor, Seoul Business School, aSSIST University (cypark@assist.ac.kr)

ESG 정보를 반영한 미래 기업가치 예측 분류기 개발에 관한 연구

Copyright 2024 THE KOREAN ACADEMIC SOCIETY OF BUSINESS ADMINISTRATION
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract

Companies above a certain size that operate globally are showing increasing commitment to ESG (environmental, social, and governance) activities. The main goal of this study is to design a model that can predict future corporate value based on ESG score data. To this end, this study compares the predictions of the basic future corporate value prediction model on which previous studies have been based and those of the future corporate value prediction model proposed herein that includes ESG ratings. For a more rigorous analysis that obtains more comprehensive results, the current study presents results using five machine learning methods: CatBoost, Extra Trees, LGBM, Random Forest, and Gradient Boost. These results indicate that models that encompass ESG data consistently outperform models that do not encompass ESG data in terms of predicting future corporate value. This paper is characterized by its use of an interdisciplinary research methodology that uniquely introduces machine learning techniques, which are rarely used for empirical analysis in the financial and accounting fields. This innovative and futureoriented research method is expected to inspire subsequent scholars in these domains and others in which machine learning techniques are not typically used.

Keywords: Future Corporate Value, Tobin’s Q, ESG rating, Machine Learning, Classifier
References
1. Abbas, S., Z. Jalil, A. R. Javed, I. Batool, M. Z. Khan, A. Noorwali, and A. Akbar(2021), “BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely ran- domized tree algorithm,” PeerJ Computer Science, 7, e390.
2. Abdi, Y., X. Li, and X. Càmara-Turull(2022), “Exploring the impact of sustainability (ESG) disclosure on firm value and financial per- formance (FP) in airline industry: the moderating role of size and age,” Environment, Development and Sustainability, 24(4), pp. 5052-5079.
3. Abdul Rahman, R. and M. F. Alsayegh(2021), “Determinants of corporate environment, social and governance (ESG) reporting among Asian firms,” Journal of Risk and Financial Management, 14(4), p.167.
4. Abedin, M. Z., G. Chi, M. M. Uddin, M. S. Satu, M. I. Khan, and P. Hajek(2020), “Tax default prediction using feature transformation-based machine learning,” IEEE Access, 9, pp. 19864-19881.
5. Aboud, A. and A. Diab(2018), “The impact of social, environmental and corporate governance disclosures on firm value: Evidence from Egypt,” Journal of Accounting in Emerging Economies, 8(4), pp.442-458.
6. Aggarwal, R., V. Sounderajah, G. Martin, D. S. Ting, A. Karthikesalingam, D. King, and A. Darzi, (2021), “Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis,” NPJ Digital Medicine, 4(1), p.65.
7. Alakus, T. B. and I. Turkoglu(2020), “Comparison of deep learning approaches to predict COVID-19 infection,” Chaos, Solitons and Fractals, 140, p.110120.
8. Alareeni, B. A. and A. Hamdan(2020), “ESG impact on performance of US SandP 500-listed firms,” Corporate Governance: The International Journal of Business in Society, 20(7), pp. 1409-1428.
9. Alsariera, Y. A., V. E. Adeyemo, A. O. Balogun, and A. K. Alazzawi(2020), “Ai meta-learners and extra-trees algorithm for the detection of phishing websites,” IEEE Access, 8, pp. 142532-142542.
10. Aouadi, A. and S. Marsat(2018), “Do ESG controversies matter for firm value? Evidence from inter- national data,” Journal of Business Ethics, 151, pp.1027-1047.
11. Arvidsson, S. and J. Dumay(2022), “Corporate ESG reporting quantity, quality and performance: Where to now for environmental policy and practice?,” Business Strategy and the Environment, 31(3), pp.1091-1110.
12. Asim, A. and A. Ismail(2019), “Impact of leverage on earning management: Empirical evidence from the manufacturing sector of Pakistan,” Journal of Finance and Accounting Research, 1(1), pp.70-91.
13. Barua, A., L. F. Davidson, D. V. Rama, and S. Thiruvadi(2010), “CFO gender and accruals quality,” Accounting Horizons, 24(1), pp. 25-39.
14. Behl, A., P. R. Kumari, H. Makhija, and D. Sharma (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), pp. 231-256.
15. Bentéjac, C., A. Csörgő, and G. Martínez-Muñoz (2021), “A comparative analysis of gradient boosting algorithms,” Artificial Intelligence Review, 54, pp.1937-1967.
16. Chelgani, S. C., H. Nasiri, A. Tohry, and H. R. Heidari(2023), “Modeling industrial hydro- cyclone operational variables by SHAP- CatBoost-A “conscious lab” approach,” Powder Technology, 420, p.118416.
17. Chen, H., X. Li, Z. Feng, L. Wang, Y. Qin, M. J. Skibniewski, and Y. Liu(2023), “Shield attitude prediction based on Bayesian-LGBM machine learning,” Information Sciences, 632, pp.105-129.
18. Chen, L., M. Pelger, and J. Zhu, (2023), “Deep learning in asset pricing,” Management Science, 70(2), pp.714-750.
19. Chen, Y., J. Wu, and Z. Wu(2022), “China’s commercial bank stock price prediction using a novel K-means-LSTM hybrid approach,” Expert Systems with Applications, 202, p.117370.
20. Chen, Y., W. Zheng, W. Li, and Y. Huang(2021), “Large group activity security risk assessment and risk early warning based on random forest algorithm,” Pattern Recognition Letters, 144, pp.1-5.
21. Choi, J. H., M. H. Ahn, C. H. Lee, M. S. Kim, Y. J. Jang, J. H. Lee, and T. E. Sung, (2021), “Deep learning based sales estimation research for technology valuation: focusing on marine fisheries,” Journal of the Society of Technological Innovation, 24(5), pp.951-965.
22. Chouaibi, S. and J. Chouaibi(2021), “Social and ethical practices and firm value: The moderating effect of green innovation: Evidence from international ESG data,” International Journal of Ethics and Systems, 37(3), pp.442-465.
23. Christopher, M., A. Belghith, C. Bowd, J. A. Proudfoot, M. H. Goldbaum, R. N. Weinreb, and L. M. Zangwill(2018), “Performance of deep learning architectures and transfer learning for detecting glaucomatous optic neuropathy in fundus photographs,” Scientific Reports, 8 (1), p.16685.
24. Clarkson, P., Y. Li, G. Richardson, and A. Tsang (2019), “Causes and consequences of voluntary assurance of CSR reports: International evidence involving Dow Jones Sustainability Index Inclusion and Firm Valuation,” Accounting, Auditing and Accountability Journa, 32(8), pp.2451-2474.
25. Csizmadia, G., K. Liszkai-Peres, B. Ferdinandy, Á. Miklósi, and V. Konok(2022), “Human activity recognition of children with wearable devices using LightGBM machine learning,” Scientific Reports, 12(1), p.5472.
26. D'Amato, A. and C. Falivena(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), pp.909-924.
27. D'Amato, A. and C. Falivena(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), pp.909-924.
28. Douiba, M., S. Benkirane, A. Guezzaz, and M. Azrour (2023), “An improved anomaly detection model for IoT security using decision tree and gradient boosting,” The Journal of Supercomputing, 79(3), pp.3392-3411.
29. Duan, T., A. Anand, D. Y. Ding, K. K. Thai, S. Basu, A. Ng, and A. Schuler(2020), “Ngboost: Natural gradient boosting for probabilistic prediction,” In International Conference on Machine Learning, PMLR, pp.2690-2700.
30. El Bilali, A., T. Abdeslam, N. Ayoub, H. Lamane, M. A. Ezzaouini, and A. Elbeltagi(2023), “An interpretable machine learning approach based on DNN, SVR, Extra Tree, and XGBoost models for predicting daily pan evaporation,” Journal of Environmental Management, 327, p.116890.
31. Eslami, E., A. K. Salman, Y. Choi, A. Sayeed, and Y. Lops(2020), “A data ensemble approach for real-time air quality forecasting using extremely randomized trees and deep neural networks,” Neural Computing and Applications, 32, pp.7563-7579.
32. Fatemi, A., M. Glaum, and S. Kaiser(2018), “ESG performance and firm value: The moderating role of disclosure,” Global Finance Journal, 38, pp.45-64.
33. Feng, Z. and Z. Wu(2021), “ESG disclosure, REIT debt financing and firm value,” The Journal of Real Estate Finance and Economics, pp. 1-35.
34. Ghorbani, A., D. Ouyang, A. Abid, B. He, J. H. Chen, R. A. Harrington, and J. Y. Zou(2020), “Deep learning interpretation of echocardiograms,” NPJ Digital Medicine, 3(1), p.10.
35. Gong, D. and Y. Liu(2022), “A Mechine Learning Approach for Botnet Detection Using Light GBM,” 2022 3rd International Conference on Computer Vision, Image and Deep Learning and International Conference on Computer Engineering and Applications (CVIDL and ICCEA), IEEE, pp.829-833.
36. Gu, Z. and H. Kim(2002), “Determinants of restaurant systematic risk: A reexamination,” The Journal of Hospitality Financial Management, 10(1), pp.1-13.
37. Gupta, S., W. Zhang, and F. Wang(2016), “Model accuracy and runtime tradeoff in distributed deep learning: A systematic study,” 2016 IEEE 16th International Conference on Data Mining (ICDM), IEEE, pp.171-180.
38. Hamid, S. A. and A. Habib(2014), “Financial fore- casting with neural networks,” Academy of Accounting and Financial Studies Journal, 18(4), p.37.
39. Hancock, J. T. and T. M. Khoshgoftaar(2020), “CatBoost for big data: an interdisciplinary review,” Journal of Big Data, 7(1), pp.1-45.
40. Hijab, A., M. A. Rushdi, M. M. Gomaa, and A. Eldeib (2019), “Breast cancer classification in ultra- sound images using transfer learning,” 2019 Fifth international conference on advances in biomedical engineering (ICABME), IEEE, pp.1-4.
41. HU, W., Y. Y. CHAN, J. Huang, W. Zhou, and X. Li (2023), “Innovation Novelty and Firm Value: Deep Learning based Text Understanding,” International Conference on Information Systems 2023 (ICIS23).
42. Huang, D. Z.(2021), “Environmental, social and governance (ESG) activity and firm performance: A review and consolidation,” Accounting and Finance, 61(1), pp.335-360.
43. Husna, A. and I. Satria(2019), “Effects of return on asset, debt to asset ratio, current ratio, firm size, and dividend payout ratio on firm value,” International Journal of Economics and Financial Issues, 9(5), pp.50-54.
44. Islam, S. and S. H. Amin(2020), “Prediction of probable backorder scenarios in the supply chain using Distributed Random Forest and Gradient Boosting Machine learning techniques,” Journal of Big Data, 7, pp.1-22.
45. Iwendi, C., A. K. Bashir, A. Peshkar, R.Sujatha, J. M. Chatterjee, S. Pasupuleti, and O. Jo (2020), “COVID-19 patient health prediction using boosted random forest algorithm,” Frontiers in Public Health, 8, p.357.
46. Jiang, M., L. Jia, Z. Chen, and W. Chen(2022), “The two-stage machine learning ensemble models for stock price prediction by combining mode decomposition, extreme learning machine and improved harmony search algorithm,” Annals of Operations Research, pp.1-33.
47. Jing, N., Z. Wu, and H. Wang(2021), “A hybrid model integrating deep learning with investor sentiment analysis for stock price prediction,” Expert Systems with Applications, 178, p. 115019.
48. Joo, C., H. Park, J. Lim, H. Cho, and J. Kim(2023), “Machine learning-based heat deflection temperature prediction and effect analysis in polypropylene composites using catboost and shapley additive explanations,” Engineering Applications of Artificial Intelligence, 126, p.106873.
49. Kalbuana, N., B. Prasetyo, P. Asih, Y. Arnas, S. L. Simbolon, A. Abdusshomad, and F. M. Mahdi (2021), “Earnings management is affected by firm size, leverage and roa: evidence from Indonesia,” Academy of Strategic Management Journal, 20, pp.1-12.
50. Kang K. G., J. Y. Park, and H. J. Na(2023), “A comparative study of machine learning-based future corporate value prediction models: the impact of including ESG ratings,” Journal of the Korean Society of Management, 36 (9), pp.1515-1537.
51. Kezelj, T. and R. Gruenbichler(2021), “A Systematic Literature Review on Corporate Insolvency Prevention Using Artificial Intelligence Algorithms,” Journal of Strategic Innovation and Sustainability, 16(4).
52. Kilimci, Z. H., A. O. Akyuz, M. Uysal, S. Akyokus, M. O. Uysal, B. Atak Bulbul, and M. A. Ekmis(2019), “An improved demand forecasting model using deep learning approach and proposed decision integration strategy for supply chain,” Complexity, 2019.
53. Kim M. S., J. S. Lee, E. S. Oh, C. H. Lee, J. H. Choi, Y. J. Jang, and T. E. Sung(2021), “A study on deep learning-based intelligent technology valuation: a model for predicting qualitative evaluation indicators through deep neural network learning,” Journal of Technological Innovation, 24(6), pp.1141- 1162.
54. Kim, H., H. Cho, and D. Ryu(2022), “Corporate bankruptcy prediction using machine learning methodologies with a focus on sequential data,” Computational Economics, 59(3), pp. 1231-1249.
55. Kiran, R., P. Kumar, and B. Bhasker(2020), “DNNRec: A novel deep learning based hybrid recom- mender system,” Expert Systems with Applications, 144, p.113054
56. Krylov, S.(2018), “Target financial forecasting as an instrument to improve company financial health,” Cogent Business and Management, 5(1), p.1540074.
57. Kureljusic, M. and E. Karger(2023), “Forecasting in financial accounting with artificial intelligence –A systematic literature review and future research agenda,” Journal of Applied Accounting Research.
58. Lee H. J., D. W. Shin, and H. E. Kim(2021), “Machine Learning-based enterprise value prediction model: utilizing online enterprise reviews,” Journal of Korean Society for Internet Information, 22(5).
59. Lee, J., D. Jang, and S. Park(2017), “Deep learning- based corporate performance prediction model considering technical capability,” Sustainability, 9(6), 899.
60. Li, M., C. Liu, and T. Scott(2019), “Share pledges and firm value,” Pacific-Basin Finance Journal, 55, pp.192-205.
61. Li, Y., M. Gong, X. Y. Zhang, and L. Koh(2018), “The impact of environmental, social, and governance disclosure on firm value: The role of CEO power,” The British Accounting Review, 50(1), pp.60-75.
62. Litjens, G., C. I. Sánchez, N. Timofeeva, M. Hermsen, I. Nagtegaal, I. Kovacs, and J. Van Der Laak(2016), “Deep learning as a tool for increased accuracy and efficiency of his- topathological diagnosis,” Scientific reports, 6(1), p.26286.
63. Long, J., Z. Chen, W. He, T. Wu, and J. Ren(2020), “An integrated framework of deep learning and knowledge graph for prediction of stock price trend: An application in Chinese stock exchange market,” Applied Soft Computing, 91, p.106205.
64. Louk, M. H. L. and B. A. Tama(2023), “Dual-IDS: A bagging-based gradient boosting decision tree model for network anomaly intrusion detection system,” Expert Systems with Applications, 213, p.119030.
65. Luo, M., Y. Wang, Y. Xie, L. Zhou, J. Qiao, S. Qiu, and Y. Sun(2021), “Combination of feature selection and catboost for prediction: The first application to the estimation of above- ground biomass,” Forests, 12(2), p.216.
66. Mai, F., S.Tian, C. Lee, and L. Ma(2019), “Deep learning models for bankruptcy prediction using textual disclosures,” European Journal of Operational Research, 274(2), pp.743-758.
67. Manavalan, B., S. Basith, T. H. Shin, L. Wei, and G. Lee(2019), “AtbPpred: a robust sequence- based prediction of anti-tubercular peptides using extremely randomized trees,” Com- putational and Structural Biotechnology Journal, 17, pp.972-981.
68. Manikandan, G. and G. Bhuvaneswari(2022), “Know- ledge discovery in data of prostate cancer by applying ensemble learning,” Indian Journal of Computer Science and Engineering (IJCSE), e-ISSN, 0976-5166.
69. Massaoudi, M., S. S. Refaat, I. Chihi, M. Trabelsi, F. S. Oueslati, and H. Abu-Rub(2021), “A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short- Term Load Forecasting,” Energy, 214, p. 118874.
70. Mishra, A. K. and S. Paliwal(2023), “Mitigating cyber threats through integration of feature selection and stacking ensemble learning: the LGBM and random forest intrusion detection perspective.” Cluster Computing, 26(4), pp. 2339-2350.
71. Morgan, N. A., D. W. Vorhies, and C. H. Mason (2009), “Market orientation, marketing capa- bilities, and firm performance,” Strategic Management Journal, 30(8), pp.909-920.
72. Nasiboglu, R. and E. Nasibov(2023), “WABL method as a universal defuzzifier in the fuzzy gradient boosting regression model,” Expert Systems with Applications, 212, p.118771.
73. Nhat-Duc, H. and T. Van-Duc(2023), “Comparison of histogram-based gradient boosting clas- sification machine, random Forest, and deep convolutional neural network for pavement raveling severity classification,” Automation in Construction, 148, p.104767.
74. Park W. J., S. S. Shin, H. J. Kim, J. N. Yu, and J. H. Kim(2022), “An analysis of value invest- ment by industry through deep learning models,” Thesis book for the academic con- ference of the Korean Society of Communi- cations, pp.914-915.
75. Patel, J., S. Shah, P. Thakkar, and K. Kotecha (2015), “Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems with Applications, 42(1), pp.259-268.
76. Pavlyshenko, B. M.(2019), “Machine-learning models for sales time series forecasting,” Data, 4 (1), p.15.
77. Pechlivanidis, E., D. Ginoglou, and P. Barmpoutis (2022), “Can intangible assets predict future performance? A deep learning approach,” International Journal of Accounting & Information Management, 30(1), pp.61-72.
78. Polak, P., C. Nelischer, H. Guo, and D. C. Robertson (2020), “Intelligent finance and treasury management: what we can expect,” Ai and Society, 35, pp.715-726.
79. Qiu, S. C., J. Jiang, X. Liu, M. H. Chen, and X. Yuan(2021), “Can corporate social responsibility protect firm value during the COVID-19 pandemic?,” International Journal of Hospitality Management, 93, p.102759.
80. Rai, N., Y. Zhang, B. G. Ram, L. Schumacher, R. K. Yellavajjala, S. Bajwa, and X. Sun(2023), “Applications of deep learning in precision weed management: A review,” Computers and Electronics in Agriculture, 206, p.107698.
81. Ranjitha, P. and M. Spandana(2021), “Predictive analysis for big mart sales using machine learning algorithms,” 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), pp.1416-1421.
82. Rezaei, H., H. Faaljou, and G. Mansourfar(2021), “Stock price prediction using deep learning and frequency decomposition,” Expert Systems with Applications, 169, p.114332.
83. Sadhukhan, B., S. Chakraborty, and S. Mukherjee (2023), “Predicting the magnitude of an impending earthquake using deep learning techniques,” Earth Science Informatics, 16 (1), pp.803-823.
84. Saeed, U., S. U. Jan, Y. D. Lee, and I. Koo(2021), “Fault diagnosis based on extremely randomized trees in wireless sensor networks,” Reliability Engineering and System Safety, 205, p.107284.
85. Sanjeetha, R., A. Raj, K. Saivenu, M. I. Ahmed, B. Sathvik, and A. Kanavalli(2021), “Detection and mitigation of botnet based DDoS attacks using catboost machine learning algorithm in SDN environment,” International Journal of Advanced Technology and Engineering Exploration, 8(76), p.445.
86. Sharma, A., and B. Singh(2020), “AE-LGBM: Sequence-based novel approach to detect interacting protein pairs via ensemble of autoencoder and LightGBM,” Computers in Biology and Medicine, 125, p.103964.
87. Sheykhmousa, M., M. Mahdianpari, H. Ghanbari, F. Mohammadimanesh, P. Ghamisi, and S. Homayouni(2020), “Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, pp.6308-6325.
88. Sills, M. R., M. Ozkaynak, and H. Jang(2021), “Predicting hospitalization of pediatric asthma patients in emergency departments using machine learning,” International Journal of Medical Informatics, 151, p.104468.
89. Sitaula, C. and T. B. Shahi(2022), “Monkeypox virus detection using pre-trained deep learning- based approaches,” Journal of Medical Systems, 46(11), p.78.
90. Speiser, J. L., M. E. Miller, J. Tooze, and E. Ip (2019), “A comparison of random forest variable selection methods for classification prediction modeling,” Expert Systems with Applications, 134, pp.93-101.
91. Sung T. E., M. S. Kim, C. H. Lee, J. H. Choi, Y. J. Jang, and J. H. Lee, (2021), “Technology valuation and estimation of evaluation variables based on deep learning,” Paper of the Korean Society of Contents, 21(10), pp. 48-58.
92. Syed, D., H. Abu-Rub, A. Ghrayeb, and S. S. Refaat (2021), “Household-level energy forecasting in smart buildings using a novel hybrid deep learning model,” IEEE Access, 9, pp. 33498-33511.
93. Taha, A. A., and S. J. Malebary(2020), “An intelligent approach to credit card fraud detection using an optimized light gradient boosting machine,” IEEE Access, 8, pp.25579-25587.
94. Traczynski, J.(2017). “Firm default prediction: A Bayesian model-averaging approach.,” Journal of Financial and Quantitative Analysis, 52 (3), pp.1211-1245
95. Tsoumakas, G(2019), “A survey of machine learning techniques for food sales prediction,” Artificial Intelligence Review, 52(1), pp.441-447.
96. Tyralis, H., G. Papacharalampous, and A. Langousis (2019), “A brief review of random forests for water scientists and practitioners and their recent history in water resources,” Water, 11(5), p.910.
97. Velthoen, J., C. Dombry, J. J. Cai, and S. Engelke (2023), “Gradient boosting for extreme quantile regression,” Extremes, pp.1-29.
98. Wahid, N., A. Zaidi, G. Dhiman, M. Manwal, D. Soni, and R. R. Maaliw(2023), “Identification of Coronary Artery Disease using Extra Tree Classification,” 2023 International Con- ference on Inventive Computation Technologies (ICICT), IEEE, pp.787-792.
99. Wang, J., C. Rao, M. Goh, and X. Xiao(2023), “Risk assessment of coronary heart disease based on cloud-random forest,” Artificial Intelligence Review, 56(1), pp.203-232.
100. Wang, S., J. Wang, H. Lu, and W. Zhao(2021), “A novel combined model for wind speed prediction –Combination of linear model, shallow neural networks, and deep learning approaches,” Energy, 234, p.121275.
101. Wang, X., L. Tan, and J. Fan(2023), “Performance Evaluation of Mangrove Species Classification Based on Multi-Source Remote Sensing Data Using Extremely Randomized Trees in Fucheng Town, Leizhou City, Guangdong Province,” Remote Sensing, 15(5), p.1386.
102. Wang, Y(2022), “Personality type prediction using decision tree, gbdt, and cat boost,” 2022 International Conference on Big Data, Infor- mation and Computer Network (BDICN), IEEE. pp.552-558.
103. Wang, Y., Y. Liu, J. Zhao, and Q. Zhang(2023), “Low-Complexity Fast CU Classification Decision Method Based on LGBM Classifier,” Electronics, 12(11), p.2488.
104. Wei, X., C. Rao, X. Xiao, L. Chen, and M. Goh(2023), “Risk assessment of cardiovascular disease based on SOLSSA-CatBoost model,” Expert Systems with Applications, 219, p.119648.
105. Wisesa, O., A. Adriansyah, and O. I. Khalaf(2020), “Prediction analysis sales for corporate services telecommunications company using gradient boost algorithm,” 2020 2nd International Conference on Broadband Communications, Wireless Sensors and Powering (BCWSP), IEEE, pp.101-106.
106. Wong, W. C., J. A. Batten, S. B. Mohamed-Arshad, S. Nordin, and A. A. Adzis(2021), “ Does ESG certification add firm value?. Finance Research Letters, 39, p.101593.
107. Xi, B., E. Li, Y. Fissha, J. Zhou, and P. Segarra (2023), “LGBM-based modeling scenarios to compressive strength of recycled aggregate concrete with SHAP analysis,” Mechanics of Advanced Materials and Structures, pp. 1-16.
108. Yoon, J.(2021), “Forecasting of real GDP growth using machine learning models: Gradient boosting and random forest approach,” Com- putational Economics, 57(1), pp.247-265.
109. Yu, B., C. Li, N. Mirza, and M. Umar(2022), “Forecasting credit ratings of decarbonized firms: Comparative assessment of machine learning models,” Technological Forecasting and Social Change, 174, p.121255.
110. Yu, P., and X. Yan(2020), “Stock price prediction based on deep neural networks,” Neural Computing and Applications, 32, pp.1609- 1628.
111. Zhang, W., Y. He, L. Wang, S. Liu, and X. Meng, (2023), “Landslide Susceptibility mapping using random forest and extreme gradient boosting: A case study of Fengjie,” Chongqing. Geological Journal, 58(6), pp.2372-2387.
112. Zhao, B., J. Feng, X. Wu, and S. Yan(2017), “A survey on deep learning-based fine-grained object classification and semantic segmentation,” International Journal of Automation and Computing, 14(2), pp.119-135.
113. Zhu, H., W. Ge, and Z. Liu(2019), “Deep learning- based classification of weld surface defects,” Applied Sciences, 9(16), p.3312.

∙ The author Kyung Gu Kang is currently the chairman of Commerce Holdings Inc. and SUNGJIN OCEAN & ENERGY Inc. and a research professor at the Institute of Industrial Policy. He obtained a master's degree in international finance from New York State University, a master's degree in economics from Korea University, and a doctorate in business administration from Seoul School of Integrated Sciences & Technologies, aSSIST. His primary research areas are ESG management, corporate value, management strategy, and machine learning/deep learning.

∙ The author Hyung Jong Na is currently a professor of accounting and taxation at Semyung University. He obtained bachelor's, master's, and doctorate degrees from Kyung Hee University. After that, he worked as a research professor at Kyung Hee University and Sungkyunkwan University. His main research fields are tax policy, corporate value, and ESG, and recently, the focus has been on prosperous research using text mining and machine learning/deep learning.

∙ The author Cheong Yeul Park is an Associate Professor of Seoul Business School at aSSIST University. He obtained his bachelor's, master's, and doctoral degrees in psychology from Chung-Ang University. After completing his doctoral degree, he worked as a postdoctoral researcher at Hankuk University of Foreign Studies. He later served as a research professor in the Department of Psychology at Korea University. His primary research interests include positive psychology, work-life balance, and organizational engagement.

 
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