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Selecting Directors Using Machine Learning

Citations

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Cited by:

  1. Colak, Gonul & Fu, Mengchuan & Hasan, Iftekhar, 2022. "On modeling IPO failure risk," Economic Modelling, Elsevier, vol. 109(C).
  2. Colak, Gonul & Fu, Mengchuan & Hasan, Iftekhar, 2020. "Why are some Chinese firms failing in the US capital markets? A machine learning approach," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
  3. Michalski, Lachlan & Low, Rand Kwong Yew, 2024. "Determinants of corporate credit ratings: Does ESG matter?," International Review of Financial Analysis, Elsevier, vol. 94(C).
  4. Alona Bilokha & Mingying Cheng & Mengchuan Fu & Iftekhar Hasan, 2025. "Understanding CSR champions: a machine learning approach," Annals of Operations Research, Springer, vol. 347(1), pages 761-774, April.
  5. Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
  6. Falco J. Bargagli-Stoffi & Jan Niederreiter & Massimo Riccaboni, 2020. "Supervised learning for the prediction of firm dynamics," Papers 2009.06413, arXiv.org.
  7. Matthew Harding & Gabriel F. R. Vasconcelos, 2022. "Managers versus Machines: Do Algorithms Replicate Human Intuition in Credit Ratings?," Papers 2202.04218, arXiv.org.
  8. Coraggio, Luca & Pagano, Marco & Scognamiglio, Annalisa & Tåg, Joacim, 2025. "JAQ of all trades: Job mismatch, firm productivity and managerial quality," Journal of Financial Economics, Elsevier, vol. 164(C).
  9. Khadivar, Hamed & Fardnia, Pedram & Walker, Thomas, 2025. "Flying safe: The impact of corporate governance on aviation safety," Journal of Air Transport Management, Elsevier, vol. 124(C).
  10. Shun Chen & Lingling Guo & Lei Ge, 2024. "Increasing the Hong Kong Stock Market Predictability: A Temporal Convolutional Network Approach," Computational Economics, Springer;Society for Computational Economics, vol. 64(5), pages 2853-2878, November.
  11. Bo Cowgill, 2019. "Bias and Productivity in Humans and Machines," Upjohn Working Papers 19-309, W.E. Upjohn Institute for Employment Research.
  12. Hu, Nan & Yin, Xuebao & Yao, Yuhang, 2025. "A novel HAR-type realized volatility forecasting model using graph neural network," International Review of Financial Analysis, Elsevier, vol. 98(C).
  13. Yang, Jie & Niu, Yanfang & Shi, Wenlei & Zhu, Kanghuan, 2025. "Predicting ESG disclosure quality through board secretaries' characteristics: A machine learning approach," Research in International Business and Finance, Elsevier, vol. 76(C).
  14. Wang, Meijun & Liu, Chan & Tan, Zhanglu & Ye, Zihan & Lv, Hanbing, 2025. "Synergistic pathways towards greening and digital transformations of Chinese primary energy producers: Reassessing the microeconomic effects of environmental regulation," Energy, Elsevier, vol. 325(C).
  15. Gao, Wei & Ju, Ming & Yang, Tongyang, 2023. "Severe weather and peer-to-peer farmers’ loan default predictions: Evidence from machine learning analysis," Finance Research Letters, Elsevier, vol. 58(PA).
  16. Mathieu Aubry & Roman Kräussl & Gustavo Manso & Christophe Spaenjers, 2023. "Biased Auctioneers," Journal of Finance, American Finance Association, vol. 78(2), pages 795-833, April.
  17. Goodell, John W. & Muckley, Cal B. & Neelakantan, Parvati & Ryan, Darragh & Yu, Pei-Shan, 2025. "AI culture ‘profiling’ and anti-money laundering: Efficacy vs ethics," International Review of Financial Analysis, Elsevier, vol. 101(C).
  18. Santiago Mejia, 2023. "The Normative and Cultural Dimension of Work: Technological Unemployment as a Cultural Threat to a Meaningful Life," Journal of Business Ethics, Springer, vol. 185(4), pages 847-864, July.
  19. von Meyerinck, Felix & Romer, Jonas & Schmid, Markus, 2025. "CEO turnover and director reputation," Journal of Financial Economics, Elsevier, vol. 163(C).
  20. Vasiliy Andreevich Laptev & Daria Rinatovna Feyzrakhmanova, 2021. "Digitalization of Institutions of Corporate Law: Current Trends and Future Prospects," Laws, MDPI, vol. 10(4), pages 1-19, December.
  21. Christensen, Peter & Francisco, Paul & Myers, Erica & Shao, Hansen & Souza, Mateus, 2024. "Energy efficiency can deliver for climate policy: Evidence from machine learning-based targeting," Journal of Public Economics, Elsevier, vol. 234(C).
  22. Hanauer, Matthias X. & Kalsbach, Tobias, 2023. "Machine learning and the cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 55(C).
  23. Paul Geertsema & Helen Lu, 2023. "Relative Valuation with Machine Learning," Journal of Accounting Research, Wiley Blackwell, vol. 61(1), pages 329-376, March.
  24. Zhao, Yuchen & Bi, Xiaogang & Ma, Qing-Ping, 2025. "Predicting mergers & acquisitions: A machine learning-based approach," International Review of Financial Analysis, Elsevier, vol. 99(C).
  25. Gao, Feng & Chi, Hong & Shao, Xueyan, 2021. "Forecasting residential electricity consumption using a hybrid machine learning model with online search data," Applied Energy, Elsevier, vol. 300(C).
  26. Giannetti, Mariassunta & Wang, Tracy Yue, 2020. "Public Attention to Gender Equality and the Demand for Female Directors," CEPR Discussion Papers 14503, C.E.P.R. Discussion Papers.
  27. Gormley, Todd A. & Gupta, Vishal K. & Matsa, David A. & Mortal, Sandra C. & Yang, Lukai, 2023. "The Big Three and board gender diversity: The effectiveness of shareholder voice," Journal of Financial Economics, Elsevier, vol. 149(2), pages 323-348.
  28. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
  29. Shumiao Ouyang & Hayong Yun & Xingjian Zheng, 2024. "AI as Decision-Maker: Ethics and Risk Preferences of LLMs," Papers 2406.01168, arXiv.org, revised Jun 2025.
  30. Manal Ahdadou & Abdellah Aajly & Mohamed Tahrouch, 2024. "Unlocking the potential of augmented intelligence: a discussion on its role in boardroom decision-making," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 21(3), pages 433-446, September.
  31. Michael Gofman & Zhao Jin, 2024. "Artificial Intelligence, Education, and Entrepreneurship," Journal of Finance, American Finance Association, vol. 79(1), pages 631-667, February.
  32. Hanauer, Matthias X. & Kononova, Marina & Rapp, Marc Steffen, 2022. "Boosting agnostic fundamental analysis: Using machine learning to identify mispricing in European stock markets," Finance Research Letters, Elsevier, vol. 48(C).
  33. Amini, Shahram & Elmore, Ryan & Öztekin, Özde & Strauss, Jack, 2021. "Can machines learn capital structure dynamics?," Journal of Corporate Finance, Elsevier, vol. 70(C).
  34. Liu, Tingting & Lu, Zhongjin (Gene) & Shu, Tao & Wei, Fengrong, 2022. "Unique bidder-target relatedness and synergies creation in mergers and acquisitions," Journal of Corporate Finance, Elsevier, vol. 73(C).
  35. Steven Balsam & So Yean Kwack, 2022. "The impact of connections between the CEO and top executives on appointment, turnover and firm value," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 49(5-6), pages 882-933, May.
  36. McGinnity, Frances & Quinn, Emma & McCullough, Evie & Enright, Shannen, 2021. "Measures to combat racial discrimination and promote diversity in the labour market: A review of evidence," Research Series, Economic and Social Research Institute (ESRI), number SUSTAT110.
  37. Ilya Ivaninskiy & Irina Ivashkovskaya, 2022. "Are blockchain-based digital transformation and ecosystem-based business models mutually reinforcing? The principal-agent conflict perspective," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 12(4), pages 643-670, December.
  38. Zhao, Shuang & Zhang, Liqun & Peng, Lin & Zhou, Haiyan & Hu, Feng, 2024. "Enterprise pollution reduction through digital transformation? Evidence from Chinese manufacturing enterprises," Technology in Society, Elsevier, vol. 77(C).
  39. Gang Kou & Yang Lu, 2025. "FinTech: a literature review of emerging financial technologies and applications," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-34, December.
  40. Li, Ang & Liu, Mark & Sheather, Simon, 2023. "Predicting stock splits using ensemble machine learning and SMOTE oversampling," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
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