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Corporate Social Irresponsibility and Credit Risk Prediction: A Machine Learning Approach

  1. Journals
  2. Economics, Finance, Business & Management
  3. Credit and Capital Markets – Kredit und Kapital
  4. Vol. 53, Issue 4
  5. pp. 513–554

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Fauser, D., Gruener, A. Corporate Social Irresponsibility and Credit Risk Prediction: A Machine Learning Approach. Credit and Capital Markets – Kredit und Kapital, 53(4), 513-554. https://doi.org/10.3790/ccm.53.4.513
Fauser, Daniel V. and Gruener, Andreas "Corporate Social Irresponsibility and Credit Risk Prediction: A Machine Learning Approach" Credit and Capital Markets – Kredit und Kapital 53.4, 2020, 513-554. https://doi.org/10.3790/ccm.53.4.513
Fauser, Daniel V./Gruener, Andreas (2020): Corporate Social Irresponsibility and Credit Risk Prediction: A Machine Learning Approach, in: Credit and Capital Markets – Kredit und Kapital, vol. 53, iss. 4, 513-554, [online] https://doi.org/10.3790/ccm.53.4.513
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Corporate Social Irresponsibility and Credit Risk Prediction: A Machine Learning Approach

Fauser, Daniel V. | Gruener, Andreas

Credit and Capital Markets – Kredit und Kapital, Vol. 53 (2020), Iss. 4 : pp. 513–554

1 Citations (CrossRef)

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Article Details

Publisher Name
Duncker & Humblot
DOI
https://doi.org/10.3790/ccm.53.4.513
Language
English
Pages
42
Keywords

Author Details

Dr. des. Daniel V. Fauser, School of Finance, University of St. Gallen, Guisanstrasse 1a, 9010 St. Gallen (corresponding author).

Prof. Dr. Andreas Gruener, School of Finance, University of St. Gallen, Switzerland.

Cited By

  1. Environmental, social, and governance (ESG) and artificial intelligence in finance: State-of-the-art and research takeaways

    Lim, Tristan

    Artificial Intelligence Review, Vol. 57 (2024), Iss. 4

    https://doi.org/10.1007/s10462-024-10708-3 [Citations: 6]

References

  1. Agarwal, V./Taffler, R. (2008): Comparing the performance of market-based and accounting-based bankruptcy prediction models. Journal of Banking and Finance, Vol. 32(8): 1541–1551.  Google Scholar
  2. Alaka, H. A./Oyedele, L. O./Owolabi, H. A./Kumar, V./Ajayi, S. O./Akinade, O. O./Bilal, M. (2018): Systematic review of bankruptcy prediction models: Towards a framework for tool selection. Expert Systems with Applications, Vol. 94, 164–184.  Google Scholar
  3. Altman, E. I. (1968): Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, Vol. 23(4), 589–609.  Google Scholar
  4. Altman, E. I./Marco, G./Varetto, F. (1994): Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking & Finance, Vol. 18(3), 505–529.  Google Scholar
  5. Ashbaugh-Skaife, H./Collins, D. W./LaFond, R. (2006): The effects of corporate governance on firms’ credit ratings. Journal of Accounting and Economics, Vol. 42(1–2), 203–243.  Google Scholar
  6. Attig, N./Ghoul, S. E./Guedhami, O./Suh, J. (2013): Corporate Social Responsibility and Credit Ratings. Journal of Business Ethics, Vol. 117(4), 679–694.  Google Scholar
  7. Avramov, D./Chordia, T./Jostova, G./Philipov, A. (2009): Credit ratings and the cross-section of stock returns. Journal of Financial Markets, Vol. 12(3), 469–499.  Google Scholar
  8. Barboza, F./Kimura, H./Altman, E. (2017): Machine learning models and bankruptcy prediction. Expert Systems with Applications, Vol. 83, 405–417.  Google Scholar
  9. Basheer, I. A./Hajmeer, M. (2000): Artificial neural networks: Fundamentals, computing, design, and application. Journal of Microbiological Methods, Vol. 43(1), 3–31.  Google Scholar
  10. Bennell, J. A./Crabbe, D./Thomas, S./Gwilym, O. A. (2006): Modelling sovereign credit ratings: Neural networks versus ordered probit. Expert Systems with Applications, Vol. 30(3), 415–425.  Google Scholar
  11. Bharath, S. T./Shumway, T. (2008): Forecasting Default with the Merton Distance to Default Model. The Review of Financial Studies, Vol. 21(3), 1339–1369.  Google Scholar
  12. Boritz, J. E./Kennedy, D. B. (1995): Effectiveness of neural network types for prediction of business failure. Expert Systems With Applications, Vol. 9(4), 503–512.  Google Scholar
  13. Bose, I./Mahapatra, R. K. (2001): Business data mining – A machine learning perspective. Information and Management, Vol. 39(3), 211–225.  Google Scholar
  14. Breiman, L. (2001): Random forests. Machine Learning, Vol. 45(1), 5–32.  Google Scholar
  15. Breiman, L./Friedman J. H./Olshen, R. A./Stone, C. J. (1984): Classification and regression trees. Wadsworth International Group, Monterey, CA.  Google Scholar
  16. Campbell, J. Y./Hilscher, J./Szilagyi, J. (2008): In Search of Distress Risk. The Journal of Finance, Vol. 63(6), 2899–2939.  Google Scholar
  17. Chang, C.-C./Lin, C.-J. (2011): LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, Vol. 2(3), 1–27.  Google Scholar
  18. Chava, S. (2014): Environmental externalities and cost of capital. Management Science, Vol. 60(9), 2223–2247.  Google Scholar
  19. Chen, M.-Y. (2011): Bankruptcy prediction in firms with statistical and intelligent techniques and a comparison of evolutionary computation approaches. Computers & Mathematics with Applications, Vol. 62(12), 4514–4524.  Google Scholar
  20. Cleofas-Sánchez, L./García, V./Marqués, A. I./Sánchez, J. S. (2016): Financial distress prediction using the hybrid associative memory with translation. Applied Soft Computing Journal, Vol. 44, 144–152.  Google Scholar
  21. Dichev, I. D. (1998): Is the Risk of Bankruptcy a Systematic Risk? The Journal of Finance, 53(3):1131–1147. Diebold, F. X. and Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business and Economic Statistics, Vol. 13(3), 253–263.  Google Scholar
  22. Dorfleitner, G./Grebler, J./Utz, S. (2020): The Impact of Corporate Social and Environmental Performance on Credit Rating Prediction: North America versus Europe. Journal of Risk, forthcoming.  Google Scholar
  23. Drago, D./Carnevale, C./Gallo, R. (2019): Do corporate social responsibility ratings affect credit default swap spreads? Corporate Social Responsibility and Environmental Management, Vol. 26(3), 644–652.  Google Scholar
  24. Ericsson, J./Renault, O. (2006): Liquidity and credit risk. The Journal of Finance, Vol. 61(5), 2219–2250.  Google Scholar
  25. Fan, R.-E./Chang, K.-W./Hsieh, C.-J./Wang, X.-R./Lin, C.-J. (2008). LIBLINEAR: A Library for Large Linear Classification. The Journal of Machine Learning Research, Vol. 9, 1871–1874.  Google Scholar
  26. Géron, A. (2017): Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media, Inc., Sebastopol, CA, 1st edition.  Google Scholar
  27. Goss, A./Roberts, G. S. (2011): The impact of corporate social responsibility on the cost of bank loans. Journal of Banking and Finance, Vol. 35(7), 1794–1810.  Google Scholar
  28. Graham, J. R./Harvey, C. R. (2001): The theory and practice of corporate finance: Evidence from the field. Journal of Financial Economics, Vol. 60(2–3), 187–243.  Google Scholar
  29. Gu, S./Kelly, B./Xiu, D. (2020): Empirical Asset Pricing via Machine Learning. The Review of Financial Studies, Vol. 33(5), 2223–2273.  Google Scholar
  30. Harvey, D./Leybourne, S./Newbold, P. (1997): Testing the equality of prediction mean squared errors. International Journal of Forecasting, Vol. 13(2), 281–291.  Google Scholar
  31. Hastie, T./Tibshirani, R./Friedman, J. (2009): The Elements of Statistical Learning. Springer-Verlag, New York, 2nd edition.  Google Scholar
  32. Henisz, W. J./McGlinch, J. (2019): ESG, Material Credit Events, and Credit Risk. Journal of Applied Corporate Finance, Vol. 31(2), 105–117.  Google Scholar
  33. Hsu, F. J./Chen, Y.-C. (2015): Is a firms financial risk associated with corporate social responsibility? Management Decision, Vol. 53(9), 2175–2199.  Google Scholar
  34. Jiang, F./Jiang, Y./Zhi, H./Dong, Y./Li, H./Ma, S./Wang, Y./Dong, Q./Shen, H./Wang, Y. (2017): Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, Vol. 2(4), 230–243.  Google Scholar
  35. Jiraporn, P./Jiraporn, N./Boeprasert, A./Chang, K. (2014): Does corporate social responsibility (CSR) improve credit ratings? Evidence from geographic identification. Financial Management, Vol. 43(3), 505–531.  Google Scholar
  36. Jo, H./Na, H. (2012): Does CSR Reduce Firm Risk? Evidence from Controversial Industry Sectors. Journal of Business Ethics, Vol. 110(4), 441–456.  Google Scholar
  37. Kealhofer, S. (2003): Quantifying credit risk I: Default prediction. Financial Analysts Journal, Vol. 59(1), 30–44.  Google Scholar
  38. Khandani, A. E./Kim, A. J./Lo, A. W. (2010): Consumer credit-risk models via machine-learning algorithms. Journal of Banking and Finance, Vol. 34(11), 2767–2787.  Google Scholar
  39. Kiesel, F./Lücke, F. (2019): ESG in credit ratings and the impact on financial markets. Financial Markets, Institutions & Instruments, Vol. 28(3), 263–290.  Google Scholar
  40. Kingma, D. P./Ba, J. L. (2015): Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations, ICLR 2015.  Google Scholar
  41. Kisgen, D. J. (2006): Credit ratings and capital structure. Journal of Finance, Vol. 61(3), 1035–1072.  Google Scholar
  42. Kölbel, J. F./Busch, T./Jancso, L. M. (2017): How Media Coverage of Corporate Social Irresponsibility Increases Financial Risk. Strategic Management Journal, Vol. 38(11), 2266–2284.  Google Scholar
  43. Kourou, K./Exarchos, T. P./Exarchos, K. P./Karamouzis, M. V./Fotiadis, D. I. (2015): Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 13, 8–17.  Google Scholar
  44. Lee, D. D./Faff, R. W. (2009): Corporate Sustainability Performance and Idiosyncratic Risk: A Global Perspective. Financial Review, Vol. 44(2), 213–237.  Google Scholar
  45. Leland, H. E. (1994): Corporate Debt Value, Bond Covenants, and Optimal Capital Structure. The Journal of Finance, Vol. 49(4), 1213–1252.  Google Scholar
  46. McCullagh, P. (1980): Regression Models for Ordinal Data. Journal of the Royal Statistical Society. Series B (Methodological), Vol. 42(2), 109–142.  Google Scholar
  47. Menz, K.-M. M. (2010): Corporate Social Responsibility: Is it Rewarded by the Corporate Bond Market? A Critical Note. Journal of Business Ethics, Vol. 96(1), 117–134.  Google Scholar
  48. Merton, R. C. (1973): Theory of Rational Option Pricing. The Bell Journal of Economics and Management Science, Vol. 4(1), 141–183.  Google Scholar
  49. Merton, R. C. (1974): On the Pricing of Corporate Debt: the Risk Structure of Interest Rates. The Journal of Finance, Vol. 29(2), 449–470.  Google Scholar
  50. Ohlson, J. A. (1980): Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, Vol. 18(1), 109–131.  Google Scholar
  51. Oikonomou, I./Brooks, C./Pavelin, S. (2014): The Effects of Corporate Social Performance on the Cost of Corporate Debt and Credit Ratings. The Financial Review, Vol. 49(1), 49–75.  Google Scholar
  52. Piramuthu, S. (2006): On preprocessing data for financial credit risk evaluation. Expert Systems with Applications, Vol. 30(3), 489–497.  Google Scholar
  53. PRI (2018): ESG in Credit Risk and Ratings Forums: Investor Survey Results. Technical report, PRI Association, London.  Google Scholar
  54. Rasekhschaffe, K. C./Jones, R. C. (2019): Machine Learning for Stock Selection. Financial Analysts Journal, Vol. 75(3), 70–88.  Google Scholar
  55. Sassen, R./Hinze, A.-K./Hardeck, I. (2016): Impact of ESG Factors on Firm Risk in Europe. Journal of Business Economics, Vol. 86(8),867–904.  Google Scholar
  56. Sebastiani, F. (2002): Machine Learning in Automated Text Categorization. ACM Computing Surveys, Vol. 34(1), 1–47.  Google Scholar
  57. Shanker, M. S./Hu, M. Y./Hung, M. S. (1996): Effect of data standardization on neural network training. Omega, Vol. 24(4), 385–397.  Google Scholar
  58. Stellner, C./Klein, C./Zwergel, B. (2015): Corporate social responsibility and Eurozone corporate bonds: The moderating role of country sustainability. Journal of Banking and Finance, 59, 538–549.  Google Scholar
  59. Strike, V. M./Gao, J./Bansal, P. (2006): Being good while being bad: Social responsibility and the international diversification of US firms. Journal of International Business Studies, Vol. 37(6), 850–862.  Google Scholar
  60. Sun, W./Cui, K. (2014): Linking corporate social responsibility to firm default risk. European Management Journal, Vol. 32(2), 275–287.  Google Scholar
  61. Svetnik, V./Liaw, A./Tong, C./Culberson, J. C./Sheridan, R. P./Feuston, B. P. (2003): Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling. Journal of Chemical Information and Computer Sciences, Vol. 43(6), 1947–1958.  Google Scholar
  62. Vassalou, M./Xing, Y. (2004): Default Risk in Equity Returns. The Journal of Finance, Vol. 59(2), 831–868.  Google Scholar
  63. Waddock, S. A./Graves, S. B. (1997): The corporate social performance-financial performance link. Strategic Management Journal, Vol. 18(4), 303–319.  Google Scholar
  64. Wu, L./Yang, Y. (2014): Nonnegative Elastic Net and application in index tracking. Applied Mathematics and Computation, 227, 541–552.  Google Scholar
  65. Yeh, C. C./Chi, D. J./Lin, Y. R. (2014): Going-concern prediction using hybrid random forests and rough set approach. Information Sciences, 254, 98–110.  Google Scholar
  66. Zou, J., Huss/M., Abid, A./Mohammadi, P./Torkamani, A./Telenti, A. (2019): A primer on deep learning in genomics. Nature Genetics, Vol. 51(1), 12–18.  Google Scholar
  67. Agarwal, V./Taffler, R. (2008): Comparing the performance of market-based and accounting-based bankruptcy prediction models. Journal of Banking and Finance, Vol. 32(8): 1541–1551.  Google Scholar
  68. Alaka, H. A./Oyedele, L. O./Owolabi, H. A./Kumar, V./Ajayi, S. O./Akinade, O. O./Bilal, M. (2018): Systematic review of bankruptcy prediction models: Towards a framework for tool selection. Expert Systems with Applications, Vol. 94, 164–184.  Google Scholar
  69. Altman, E. I. (1968): Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, Vol. 23(4), 589–609.  Google Scholar
  70. Altman, E. I./Marco, G./Varetto, F. (1994): Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking & Finance, Vol. 18(3), 505–529.  Google Scholar
  71. Ashbaugh-Skaife, H./Collins, D. W./LaFond, R. (2006): The effects of corporate governance on firms’ credit ratings. Journal of Accounting and Economics, Vol. 42(1–2), 203–243.  Google Scholar
  72. Attig, N./Ghoul, S. E./Guedhami, O./Suh, J. (2013): Corporate Social Responsibility and Credit Ratings. Journal of Business Ethics, Vol. 117(4), 679–694.  Google Scholar
  73. Avramov, D./Chordia, T./Jostova, G./Philipov, A. (2009): Credit ratings and the cross-section of stock returns. Journal of Financial Markets, Vol. 12(3), 469–499.  Google Scholar
  74. Barboza, F./Kimura, H./Altman, E. (2017): Machine learning models and bankruptcy prediction. Expert Systems with Applications, Vol. 83, 405–417.  Google Scholar
  75. Basheer, I. A./Hajmeer, M. (2000): Artificial neural networks: Fundamentals, computing, design, and application. Journal of Microbiological Methods, Vol. 43(1), 3–31.  Google Scholar
  76. Bennell, J. A./Crabbe, D./Thomas, S./Gwilym, O. A. (2006): Modelling sovereign credit ratings: Neural networks versus ordered probit. Expert Systems with Applications, Vol. 30(3), 415–425.  Google Scholar
  77. Bharath, S. T./Shumway, T. (2008): Forecasting Default with the Merton Distance to Default Model. The Review of Financial Studies, Vol. 21(3), 1339–1369.  Google Scholar
  78. Boritz, J. E./Kennedy, D. B. (1995): Effectiveness of neural network types for prediction of business failure. Expert Systems With Applications, Vol. 9(4), 503–512.  Google Scholar
  79. Bose, I./Mahapatra, R. K. (2001): Business data mining – A machine learning perspective. Information and Management, Vol. 39(3), 211–225.  Google Scholar
  80. Breiman, L. (2001): Random forests. Machine Learning, Vol. 45(1), 5–32.  Google Scholar
  81. Breiman, L./Friedman J. H./Olshen, R. A./Stone, C. J. (1984): Classification and regression trees. Wadsworth International Group, Monterey, CA.  Google Scholar
  82. Campbell, J. Y./Hilscher, J./Szilagyi, J. (2008): In Search of Distress Risk. The Journal of Finance, Vol. 63(6), 2899–2939.  Google Scholar
  83. Chang, C.-C./Lin, C.-J. (2011): LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, Vol. 2(3), 1–27.  Google Scholar
  84. Chava, S. (2014): Environmental externalities and cost of capital. Management Science, Vol. 60(9), 2223–2247.  Google Scholar
  85. Chen, M.-Y. (2011): Bankruptcy prediction in firms with statistical and intelligent techniques and a comparison of evolutionary computation approaches. Computers & Mathematics with Applications, Vol. 62(12), 4514–4524.  Google Scholar
  86. Cleofas-Sánchez, L./García, V./Marqués, A. I./Sánchez, J. S. (2016): Financial distress prediction using the hybrid associative memory with translation. Applied Soft Computing Journal, Vol. 44, 144–152.  Google Scholar
  87. Dichev, I. D. (1998): Is the Risk of Bankruptcy a Systematic Risk? The Journal of Finance, 53(3):1131–1147. Diebold, F. X. and Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business and Economic Statistics, Vol. 13(3), 253–263.  Google Scholar
  88. Drago, D./Carnevale, C./Gallo, R. (2019): Do corporate social responsibility ratings affect credit default swap spreads? Corporate Social Responsibility and Environmental Management, Vol. 26(3), 644–652.  Google Scholar
  89. Ericsson, J./Renault, O. (2006): Liquidity and credit risk. The Journal of Finance, Vol. 61(5), 2219–2250.  Google Scholar
  90. Fan, R.-E./Chang, K.-W./Hsieh, C.-J./Wang, X.-R./Lin, C.-J. (2008). LIBLINEAR: A Library for Large Linear Classification. The Journal of Machine Learning Research, Vol. 9, 1871–1874.  Google Scholar
  91. Géron, A. (2017): Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media, Inc., Sebastopol, CA, 1st edition.  Google Scholar
  92. Goss, A./Roberts, G. S. (2011): The impact of corporate social responsibility on the cost of bank loans. Journal of Banking and Finance, Vol. 35(7), 1794–1810.  Google Scholar
  93. Graham, J. R./Harvey, C. R. (2001): The theory and practice of corporate finance: Evidence from the field. Journal of Financial Economics, Vol. 60(2–3), 187–243.  Google Scholar
  94. Gu, S./Kelly, B./Xiu, D. (2020): Empirical Asset Pricing via Machine Learning. The Review of Financial Studies, Vol. 33(5), 2223–2273.  Google Scholar
  95. Harvey, D./Leybourne, S./Newbold, P. (1997): Testing the equality of prediction mean squared errors. International Journal of Forecasting, Vol. 13(2), 281–291.  Google Scholar
  96. Hastie, T./Tibshirani, R./Friedman, J. (2009): The Elements of Statistical Learning. Springer-Verlag, New York, 2nd edition.  Google Scholar
  97. Henisz, W. J./McGlinch, J. (2019): ESG, Material Credit Events, and Credit Risk. Journal of Applied Corporate Finance, Vol. 31(2), 105–117.  Google Scholar
  98. Hsu, F. J./Chen, Y.-C. (2015): Is a firms financial risk associated with corporate social responsibility? Management Decision, Vol. 53(9), 2175–2199.  Google Scholar
  99. Jiang, F./Jiang, Y./Zhi, H./Dong, Y./Li, H./Ma, S./Wang, Y./Dong, Q./Shen, H./Wang, Y. (2017): Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, Vol. 2(4), 230–243.  Google Scholar
  100. Jiraporn, P./Jiraporn, N./Boeprasert, A./Chang, K. (2014): Does corporate social responsibility (CSR) improve credit ratings? Evidence from geographic identification. Financial Management, Vol. 43(3), 505–531.  Google Scholar
  101. Jo, H./Na, H. (2012): Does CSR Reduce Firm Risk? Evidence from Controversial Industry Sectors. Journal of Business Ethics, Vol. 110(4), 441–456.  Google Scholar
  102. Kealhofer, S. (2003): Quantifying credit risk I: Default prediction. Financial Analysts Journal, Vol. 59(1), 30–44.  Google Scholar
  103. Khandani, A. E./Kim, A. J./Lo, A. W. (2010): Consumer credit-risk models via machine-learning algorithms. Journal of Banking and Finance, Vol. 34(11), 2767–2787.  Google Scholar
  104. Kiesel, F./Lücke, F. (2019): ESG in credit ratings and the impact on financial markets. Financial Markets, Institutions & Instruments, Vol. 28(3), 263–290.  Google Scholar
  105. Kingma, D. P./Ba, J. L. (2015): Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations, ICLR 2015.  Google Scholar
  106. Kisgen, D. J. (2006): Credit ratings and capital structure. Journal of Finance, Vol. 61(3), 1035–1072.  Google Scholar
  107. Kölbel, J. F./Busch, T./Jancso, L. M. (2017): How Media Coverage of Corporate Social Irresponsibility Increases Financial Risk. Strategic Management Journal, Vol. 38(11), 2266–2284.  Google Scholar
  108. Kourou, K./Exarchos, T. P./Exarchos, K. P./Karamouzis, M. V./Fotiadis, D. I. (2015): Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 13, 8–17.  Google Scholar
  109. Lee, D. D./Faff, R. W. (2009): Corporate Sustainability Performance and Idiosyncratic Risk: A Global Perspective. Financial Review, Vol. 44(2), 213–237.  Google Scholar
  110. Leland, H. E. (1994): Corporate Debt Value, Bond Covenants, and Optimal Capital Structure. The Journal of Finance, Vol. 49(4), 1213–1252.  Google Scholar
  111. McCullagh, P. (1980): Regression Models for Ordinal Data. Journal of the Royal Statistical Society. Series B (Methodological), Vol. 42(2), 109–142.  Google Scholar
  112. Menz, K.-M. M. (2010): Corporate Social Responsibility: Is it Rewarded by the Corporate Bond Market? A Critical Note. Journal of Business Ethics, Vol. 96(1), 117–134.  Google Scholar
  113. Merton, R. C. (1973): Theory of Rational Option Pricing. The Bell Journal of Economics and Management Science, Vol. 4(1), 141–183.  Google Scholar
  114. Merton, R. C. (1974): On the Pricing of Corporate Debt: the Risk Structure of Interest Rates. The Journal of Finance, Vol. 29(2), 449–470.  Google Scholar
  115. Ohlson, J. A. (1980): Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, Vol. 18(1), 109–131.  Google Scholar
  116. Oikonomou, I./Brooks, C./Pavelin, S. (2014): The Effects of Corporate Social Performance on the Cost of Corporate Debt and Credit Ratings. The Financial Review, Vol. 49(1), 49–75.  Google Scholar
  117. Piramuthu, S. (2006): On preprocessing data for financial credit risk evaluation. Expert Systems with Applications, Vol. 30(3), 489–497.  Google Scholar
  118. PRI (2018): ESG in Credit Risk and Ratings Forums: Investor Survey Results. Technical report, PRI Association, London.  Google Scholar
  119. Rasekhschaffe, K. C./Jones, R. C. (2019): Machine Learning for Stock Selection. Financial Analysts Journal, Vol. 75(3), 70–88.  Google Scholar
  120. Sassen, R./Hinze, A.-K./Hardeck, I. (2016): Impact of ESG Factors on Firm Risk in Europe. Journal of Business Economics, Vol. 86(8),867–904.  Google Scholar
  121. Sebastiani, F. (2002): Machine Learning in Automated Text Categorization. ACM Computing Surveys, Vol. 34(1), 1–47.  Google Scholar
  122. Shanker, M. S./Hu, M. Y./Hung, M. S. (1996): Effect of data standardization on neural network training. Omega, Vol. 24(4), 385–397.  Google Scholar
  123. Stellner, C./Klein, C./Zwergel, B. (2015): Corporate social responsibility and Eurozone corporate bonds: The moderating role of country sustainability. Journal of Banking and Finance, 59, 538–549.  Google Scholar
  124. Strike, V. M./Gao, J./Bansal, P. (2006): Being good while being bad: Social responsibility and the international diversification of US firms. Journal of International Business Studies, Vol. 37(6), 850–862.  Google Scholar
  125. Sun, W./Cui, K. (2014): Linking corporate social responsibility to firm default risk. European Management Journal, Vol. 32(2), 275–287.  Google Scholar
  126. Svetnik, V./Liaw, A./Tong, C./Culberson, J. C./Sheridan, R. P./Feuston, B. P. (2003): Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling. Journal of Chemical Information and Computer Sciences, Vol. 43(6), 1947–1958.  Google Scholar
  127. Vassalou, M./Xing, Y. (2004): Default Risk in Equity Returns. The Journal of Finance, Vol. 59(2), 831–868.  Google Scholar
  128. Waddock, S. A./Graves, S. B. (1997): The corporate social performance-financial performance link. Strategic Management Journal, Vol. 18(4), 303–319.  Google Scholar
  129. Wu, L./Yang, Y. (2014): Nonnegative Elastic Net and application in index tracking. Applied Mathematics and Computation, 227, 541–552.  Google Scholar
  130. Yeh, C. C./Chi, D. J./Lin, Y. R. (2014): Going-concern prediction using hybrid random forests and rough set approach. Information Sciences, 254, 98–110.  Google Scholar
  131. Zou, J., Huss/M., Abid, A./Mohammadi, P./Torkamani, A./Telenti, A. (2019): A primer on deep learning in genomics. Nature Genetics, Vol. 51(1), 12–18.  Google Scholar
  132. Dorfleitner, G./Grebler, J./Utz, S. (2020): The Impact of Corporate Social and Environmental Performance on Credit Rating Prediction: North America versus Europe. Journal of Risk, forthcoming.  Google Scholar

Abstract

This paper examines the prediction accuracy of various machine learning (ML) algorithms for firm credit risk. It marks the first attempt to leverage data on corporate social irresponsibility (CSI) to better predict credit risk in an ML context. Even though the literature on default and credit risk is vast, the potential explanatory power of CSI for firm credit risk prediction remains unexplored. Previous research has shown that CSI may jeopardize firm survival and thus potentially comes into play in predicting credit risk. We find that prediction accuracy varies considerably between algorithms, with advanced machine learning algorithms (e.?g. random forests) outperforming traditional ones (e.?g. linear regression). Random forest regression achieves an out-of-sample prediction accuracy of 89.75% for adjusted R2 due to the ability of capturing non-linearity and complex interaction effects in the data. We further show that including information on CSI in firm credit risk prediction does not consistently increase prediction accuracy. One possible interpretation of this result is that CSI does not (yet) seem to be systematically reflected in credit ratings, despite prior literature indicating that CSI increases credit risk. Our study contributes to improving firm credit risk predictions using a machine learning design and to exploring how CSI is reflected in credit risk ratings.

Table of Contents

Section Title Page Action Price
Daniel V. Fauser / Andreas Gruener: Corporate Social Irresponsibility and Credit Risk Prediction: A Machine Learning Approach 1
Abstract 1
I. Introduction 1
II. Literature Review 5
1. Machine Learning Applications 5
2. Corporate Social Irresponsibility and Credit Risk 6
III. Research Design 9
1. Predictors for Corporate Social Irresponsibility 9
2. Additional Accounting and Market Related Predictors 1
3. Proxy for Credit Risk 1
4. Sample Development 1
5. Prediction Using a Selection of Machine Learning Algorithms 1
a) Linear Regression 1
b) Elastic Net 1
c) Ridge 1
d) Ordinal Ridge 1
e) Support Vector Regression 1
f) Decision Tree Regression 1
g) Random Forest Regression 1
h) AdaBoost Regression 1
i) Gradient Boost Regression 1
j) K-neaerest Neighbors Regression 1
k) Multi-layer Perceptron Regression 1
IV. Descriptive Statistics 1
V. Results 2
1. Main Results 2
2. Additional Analyses – Regression Design 2
3. Predicting Investment Grade vs. Non-Investment Grade – Classification Design 2
VI. Contributions to Research and Practical Implications 3
VII. Concluding Remarks 3
References 3
Appendix 3

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