Double Ensemble Approaches to Predicting Firms’ Credit Rating

Several rating agencies such as Standard & Poor\u27s (S&P), Moody\u27s and Fitch Ratings have evaluated firms’ credit rating. Since lots of fees are required by the agencies and sometimes the timely default risk of the firms is not reflected, it can be helpful for stakeholders if the credit ratings can be predicted before the agencies publish them. However, it is not easy to make an accurate prediction of credit rating since it covers a variety of range. Therefore, this study proposes two double ensemble approaches, 1) bagging-boosting and 2) boosting-bagging, to improve the prediction accuracy. To that end, we first conducted feature selection, using Chi-Square and Gain-Ratio attribute evaluators, with 3 classification algorithms (i.e., decision tree (DT), artificial neural network (ANN), and Naïve Bayesian (NB)) to select relevant features and a base classifier of ensemble models. And then, we integrated bagging and boosting methods by applying boosting method to bagging method (bagging-boosting), and bagging method to boosting method (boosting-bagging). Finally, we compared the prediction accuracy of our proposed model to benchmark models. The experimental results showed that our proposed models outperformed the benchmark models

Forecasting the environmental, social and governance rating of firms by using corporate financial performance variables: A rough sets approach

[EN] The environmental, social, and governance (ESG) rating of firms is a useful tool for stakeholders and investment decision-makers. This paper develops a rough set model to relate ESG scores to popular corporate financial performance measures. This methodology permits handling with information in an uncertain, ambiguous, and imperfect context. A large database was gathered, including ESG scores, as well as industry sector and financial variables for publicly traded European companies during the period 2013-2018. We carried out 500 simulations of the rough set model for different values in the discretization parameter and different grouping scenarios of firms regarding ESG scores. The results suggest that the variables considered are useful in the prediction of ESG rank when firms are clustered in three or four equally balanced groups. However, the prediction power vanishes when a larger number of groups is computed. This would suggest that industry sector and financial variables serve to find big differences across firms regarding ESG, but the significance of the model drops when small differences in ESG performance are scrutinized.García García, F.; González-Bueno, J.; Guijarro, F.; Oliver-Muncharaz, J. (2020). Forecasting the environmental, social and governance rating of firms by using corporate financial performance variables: A rough sets approach. Sustainability. 12(8):1-18. https://doi.org/10.3390/su12083324S118128García-Rodríguez, F. J., García-Rodríguez, J. L., Castilla-Gutiérrez, C., & Major, S. A. (2013). Corporate Social Responsibility of Oil Companies in Developing Countries: From Altruism to Business Strategy. Corporate Social Responsibility and Environmental Management, 20(6), 371-384. doi:10.1002/csr.1320García, González-Bueno, Oliver, & Riley. (2019). Selecting Socially Responsible Portfolios: A Fuzzy Multicriteria Approach. Sustainability, 11(9), 2496. doi:10.3390/su11092496Arribas, I., Espinós-Vañó, M. D., García, F., & Tamošiūnienė, R. (2019). Negative screening and sustainable portfolio diversification. Entrepreneurship and Sustainability Issues, 6(4), 1566-1586. doi:10.9770/jesi.2019.6.4(2)Martínez-Ferrero, J., Gallego-Álvarez, I., & García-Sánchez, I. M. (2015). A Bidirectional Analysis of Earnings Management and Corporate Social Responsibility: The Moderating Effect of Stakeholder and Investor Protection. Australian Accounting Review, 25(4), 359-371. doi:10.1111/auar.12075Garriga, E., & Melé, D. (2004). Corporate Social Responsibility Theories: Mapping the Territory. Journal of Business Ethics, 53(1/2), 51-71. doi:10.1023/b:busi.0000039399.90587.34Jensen, M. C. (2002). Value Maximization, Stakeholder Theory, and the Corporate Objective Function. Business Ethics Quarterly, 12(2), 235-256. doi:10.2307/3857812Charlo, M. J., Moya, I., & Muñoz, A. M. (2017). Sustainable Development in Spanish Listed Companies: A Strategic Approach. Corporate Social Responsibility and Environmental Management, 24(3), 222-234. doi:10.1002/csr.1403Hillman, A. J., & Keim, G. D. (2001). Shareholder value, stakeholder management, and social issues: what’s the bottom line? Strategic Management Journal, 22(2), 125-139. doi:10.1002/1097-0266(200101)22:23.0.co;2-hMio, C., Venturelli, A., & Leopizzi, R. (2015). Management by objectives and corporate social responsibility disclosure. Accounting, Auditing & Accountability Journal, 28(3), 325-364. doi:10.1108/aaaj-09-2013-1480Venturelli, A., Caputo, F., Leopizzi, R., Mastroleo, G., & Mio, C. (2017). How can CSR identity be evaluated? A pilot study using a Fuzzy Expert System. Journal of Cleaner Production, 141, 1000-1010. doi:10.1016/j.jclepro.2016.09.172Lii, Y.-S., Wu, K.-W., & Ding, M.-C. (2011). Doing Good Does Good? Sustainable Marketing of CSR and Consumer Evaluations. 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AN EMPIRICAL EXAMINATION OF THE RELATIONSHIP BETWEEN EMISSION REDUCTION AND FIRM PERFORMANCE. Business Strategy and the Environment, 5(1), 30-37. doi:10.1002/(sici)1099-0836(199603)5:13.0.co;2-qHang, M., Geyer-Klingeberg, J., & Rathgeber, A. W. (2018). It is merely a matter of time: A meta-analysis of the causality between environmental performance and financial performance. Business Strategy and the Environment, 28(2), 257-273. doi:10.1002/bse.2215McWilliams, A., & Siegel, D. (2001). Corporate Social Responsibility: a Theory of the Firm Perspective. Academy of Management Review, 26(1), 117-127. doi:10.5465/amr.2001.4011987Luo, X., & Bhattacharya, C. B. (2006). Corporate Social Responsibility, Customer Satisfaction, and Market Value. Journal of Marketing, 70(4), 1-18. doi:10.1509/jmkg.70.4.001Seifert, B., Morris, S. A., & Bartkus, B. R. (2004). Having, Giving, and Getting: Slack Resources, Corporate Philanthropy, and Firm Financial Performance. 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How Well Do Social Ratings Actually Measure Corporate Social Responsibility? Journal of Economics & Management Strategy, 18(1), 125-169. doi:10.1111/j.1530-9134.2009.00210.xGangi, F., & Varrone, N. (2018). Screening activities by socially responsible funds: A matter of agency? Journal of Cleaner Production, 197, 842-855. doi:10.1016/j.jclepro.2018.06.228Utz, S., & Wimmer, M. (2014). Are they any good at all? A financial and ethical analysis of socially responsible mutual funds. Journal of Asset Management, 15(1), 72-82. doi:10.1057/jam.2014.8Roberts, P. W., & Dowling, G. R. (2002). Corporate reputation and sustained superior financial performance. Strategic Management Journal, 23(12), 1077-1093. doi:10.1002/smj.274Julian, S. D., & Ofori-dankwa, J. C. (2013). Financial resource availability and corporate social responsibility expenditures in a sub-Saharan economy: The institutional difference hypothesis. Strategic Management Journal, 34(11), 1314-1330. doi:10.1002/smj.2070Garcia-Castro, R., Ariño, M. A., & Canela, M. A. (2009). Does Social Performance Really Lead to Financial Performance? Accounting for Endogeneity. Journal of Business Ethics, 92(1), 107-126. doi:10.1007/s10551-009-0143-8Dupire, M., & M’Zali, B. (2016). CSR Strategies in Response to Competitive Pressures. Journal of Business Ethics, 148(3), 603-623. doi:10.1007/s10551-015-2981-xLin, W. L., Law, S. H., Ho, J. A., & Sambasivan, M. (2019). The causality direction of the corporate social responsibility – Corporate financial performance Nexus: Application of Panel Vector Autoregression approach. The North American Journal of Economics and Finance, 48, 401-418. doi:10.1016/j.najef.2019.03.004Van Beurden, P., & Gössling, T. (2008). The Worth of Values – A Literature Review on the Relation Between Corporate Social and Financial Performance. Journal of Business Ethics, 82(2), 407-424. doi:10.1007/s10551-008-9894-xJankalová, M., & Jankal, R. (2017). The assessment of corporate social responsibility: approaches analysis. Entrepreneurship and Sustainability Issues, 4(4), 441-459. doi:10.9770/jesi.2017.4.4(4)Marom, I. Y. (2006). Toward a Unified Theory of the CSP–CFP Link. Journal of Business Ethics, 67(2), 191-200. doi:10.1007/s10551-006-9023-7Chen, C.-M., & Delmas, M. (2010). Measuring Corporate Social Performance: An Efficiency Perspective. Production and Operations Management, 20(6), 789-804. doi:10.1111/j.1937-5956.2010.01202.xMuñoz-Torres, M. J., Fernández-Izquierdo, M. Á., Rivera-Lirio, J. M., & Escrig-Olmedo, E. (2018). Can environmental, social, and governance rating agencies favor business models that promote a more sustainable development? Corporate Social Responsibility and Environmental Management, 26(2), 439-452. doi:10.1002/csr.1695Guijarro, F., & Poyatos, J. (2018). Designing a Sustainable Development Goal Index through a Goal Programming Model: The Case of EU-28 Countries. Sustainability, 10(9), 3167. doi:10.3390/su10093167Guijarro, F. (2019). A Multicriteria Model for the Assessment of Countries’ Environmental Performance. International Journal of Environmental Research and Public Health, 16(16), 2868. doi:10.3390/ijerph16162868Escrig-Olmedo, E., Fernández-Izquierdo, M., Ferrero-Ferrero, I., Rivera-Lirio, J., & Muñoz-Torres, M. (2019). Rating the Raters: Evaluating how ESG Rating Agencies Integrate Sustainability Principles. Sustainability, 11(3), 915. doi:10.3390/su11030915Mattingly, J. E. (2015). Corporate Social Performance: A Review of Empirical Research Examining the Corporation–Society Relationship Using Kinder, Lydenberg, Domini Social Ratings Data. Business & Society, 56(6), 796-839. doi:10.1177/0007650315585761Landi, G., & Sciarelli, M. (2019). Towards a more ethical market: the impact of ESG rating on corporate financial performance. Social Responsibility Journal, 15(1), 11-27. doi:10.1108/srj-11-2017-0254Martínez-Ferrero, J., & Frías-Aceituno, J. V. (2013). Relationship Between Sustainable Development and Financial Performance: International Empirical Research. Business Strategy and the Environment, 24(1), 20-39. doi:10.1002/bse.1803Grewatsch, S., & Kleindienst, I. (2015). When Does It Pay to be Good? Moderators and Mediators in the Corporate Sustainability–Corporate Financial Performance Relationship: A Critical Review. Journal of Business Ethics, 145(2), 383-416. doi:10.1007/s10551-015-2852-5López, M. V., Garcia, A., & Rodriguez, L. (2007). Sustainable Development and Corporate Performance: A Study Based on the Dow Jones Sustainability Index. Journal of Business Ethics, 75(3), 285-300. doi:10.1007/s10551-006-9253-8Kang, C., Germann, F., & Grewal, R. (2016). Washing Away Your Sins? Corporate Social Responsibility, Corporate Social Irresponsibility, and Firm Performance. Journal of Marketing, 80(2), 59-79. doi:10.1509/jm.15.0324WADDOCK, S. A., & GRAVES, S. B. (1997). THE CORPORATE SOCIAL PERFORMANCE-FINANCIAL PERFORMANCE LINK. Strategic Management Journal, 18(4), 303-319. doi:10.1002/(sici)1097-0266(199704)18:43.0.co;2-gChin, M. K., Hambrick, D. C., & Treviño, L. K. (2013). Political Ideologies of CEOs. Administrative Science Quarterly, 58(2), 197-232. doi:10.1177/0001839213486984Chung, S. (Andy), Pyo, H., & Guiral, A. (2019). Who Is the Beneficiary of Slack on Corporate Financial Performance and Corporate Philanthropy? Evidence from South Korea. Sustainability, 11(1), 252. doi:10.3390/su11010252El Ghoul, S., Guedhami, O., Kwok, C. C. Y., & Mishra, D. R. (2011). Does corporate social responsibility affect the cost of capital? Journal of Banking & Finance, 35(9), 2388-2406. doi:10.1016/j.jbankfin.2011.02.007Mishra, S., & Modi, S. B. (2012). Positive and Negative Corporate Social Responsibility, Financial Leverage, and Idiosyncratic Risk. 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Social Capital, Trust, and Firm Performance: The Value of Corporate Social Responsibility during the Financial Crisis. The Journal of Finance, 72(4), 1785-1824. doi:10.1111/jofi.12505Bouslah, K., Kryzanowski, L., & M’Zali, B. (2016). Social Performance and Firm Risk: Impact of the Financial Crisis. Journal of Business Ethics, 149(3), 643-669. doi:10.1007/s10551-016-3017-xKoh, P.-S., Qian, C., & Wang, H. (2013). Firm litigation risk and the insurance value of corporate social performance. Strategic Management Journal, 35(10), 1464-1482. doi:10.1002/smj.2171Drempetic, S., Klein, C., & Zwergel, B. (2019). The Influence of Firm Size on the ESG Score: Corporate Sustainability Ratings Under Review. Journal of Business Ethics, 167(2), 333-360. doi:10.1007/s10551-019-04164-1ADAMS, C. A., HILL, W.-Y., & ROBERTS, C. B. (1998). CORPORATE SOCIAL REPORTING PRACTICES IN WESTERN EUROPE: LEGITIMATING CORPORATE BEHAVIOUR? 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Prediction of financial strength ratings using machine learning and conventional techniques

Financial strength ratings (FSRs) have become more significant particularly since the recent financial crisis of 2007–2009 where rating agencies failed to forecast defaults and the downgrade of some banks. The aim of this paper is to predict Capital Intelligence banks’ financial strength ratings (FSRs) group membership using machine learning and conventional techniques. Here the authors use five different statistical techniques, namely CHAID, CART, multilayer-perceptron neural networks, discriminant analysis and logistic regression. They also use three different evaluation criteria namely average correct classification rate, misclassification cost and gains charts. The data are collected from Bankscope database for the Middle Eastern commercial banks by reference to the first decade of the 21st century. The findings show that when predicting bank FSRs during the period 2007–2009, discriminant analysis is surprisingly superior to all other techniques used in this paper. When only machine learning techniques are used, CHAID outperform other techniques. In addition, the findings highlight that when a random sample is used to predict bank FSRs, CART outperform all other techniques. The evaluation criteria have confirmed the findings and both CART and discriminant analysis are superior to other techniques in predicting bank FSRs. This has implications for Middle Eastern banks, as the authors would suggest that improving their bank FSR can improve their presence in the market

Gene expression programming for Efficient Time-series Financial Forecasting

Stock market prediction is of immense interest to trading companies and buyers due to high profit margins. The majority of successful buying or selling activities occur close to stock price turning trends. This makes the prediction of stock indices and analysis a crucial factor in the determination that whether the stocks will increase or decrease the next day. Additionally, precise prediction of the measure of increase or decrease of stock prices also plays an important role in buying/selling activities. This research presents two core aspects of stock-market prediction. Firstly, it presents a Networkbased Fuzzy Inference System (ANFIS) methodology to integrate the capabilities of neural networks with that of fuzzy logic. A specialised extension to this technique is known as the genetic programming (GP) and gene expression programming (GEP) to explore and investigate the outcome of the GEP criteria on the stock market price prediction. The research presented in this thesis aims at the modelling and prediction of short-tomedium term stock value fluctuations in the market via genetically tuned stock market parameters. The technique uses hierarchically defined GP and gene-expressionprogramming (GEP) techniques to tune algebraic functions representing the fittest equation for stock market activities. The technology achieves novelty by proposing a fractional adaptive mutation rate Elitism (GEP-FAMR) technique to initiate a balance between varied mutation rates between varied-fitness chromosomes thereby improving prediction accuracy and fitness improvement rate. The methodology is evaluated against five stock market companies with each having its own trading circumstances during the past 20+ years. The proposed GEP/GP methodologies were evaluated based on variable window/population sizes, selection methods, and Elitism, Rank and Roulette selection methods. The Elitism-based approach showed promising results with a low error-rate in the resultant pattern matching with an overall accuracy of 95.96% for short-term 5-day and 95.35% for medium-term 56-day trading periods. The contribution of this research to theory is that it presented a novel evolutionary methodology with modified selection operators for the prediction of stock exchange data via Gene expression programming. The methodology dynamically adapts the mutation rate of different fitness groups in each generation to ensure a diversification II balance between high and low fitness solutions. The GEP-FAMR approach was preferred to Neural and Fuzzy approaches because it can address well-reported problems of over-fitting, algorithmic black-boxing, and data-snooping issues via GP and GEP algorithmsSaudi Cultural Burea

Empirical essays on risk disclosures, multi-level governance, credit ratings, and bank value: evidence from MENA banks

This thesis contains four essays that examine the relationships among risk disclosures, multi-level governance, credit ratings, and bank value in the Middle East and North Africa (MENA) banks. These essays concentrate on four closely linked risk disclosures, and governance topics that quantitatively investigate the antecedents and informativeness of risk disclosures by banks from 14 countries in MENA region over the 2006–2013 inclusive period. The first essay aims at investigating the impact of multi-layer governance mechanisms on the level of risk disclosures by banks. The essay result suggests a variation between MENA banks in the level of risk disclosures with a significant improvement from 2006 to 2013. Specifically, the findings are three-fold. First, the results suggest that Sharia Supervisory Board (SSB) is positively associated with the level of risk disclosures by banks. Second and at the bank-level, the essay finds that ownership (governmental ownership and family ownership) and board (board size and non-executive directors) structures have a positive effect on the level of risk disclosures by banks, whilst CEO duality is negative, but insignificantly related to bank risk disclosures. At the country-level, the evidence suggests that control of corruption has a positive effect on the level of bank risk disclosures, whilst political stability and absence of violence have a negative, but insignificant association with the level of bank risk disclosures. In the second essay, the thesis investigates the relationships among national governance quality (NGQM), Islamic governance quality (ISGQ), including other bank-level governance mechanisms, and risk management and disclosure practices (RMDPs); and consequently ascertains whether NGQM has a moderating influence on the ISGQ -RMDPs nexus. The findings are four-fold. Firstly, this study finds that RMDPs are higher in banks from countries with higher NGQM. Secondly, this essay shows that RMDPs are higher in banks with better Islamic governance. Thirdly, the study finds that board size and non-executive directors have a positive effect on the level of RMDPs. Finally, this study finds evidence that suggests that NGQM has a moderating effect on the Islamic governance quality-RMDPs nexus. The third essay explores whether RMDPs have a predictive effect (informativeness) on banks’ credit ratings (BCRs); and consequently ascertains whether governance structures can moderate such an association. The findings suggest that RMDPs have a predictive effect on BCRs. The study finds that the quality of the BCR is higher in banks that have higher risk disclosures, board size, government ownership, board independence, women directors and established SSB. On the other hand, the results indicate that the BCR quality is lower in banks that have higher foreign ownership, and CEO role duality. Furthermore, the findings suggest that governance structures moderate the relation between RMDPs and BCRs. The final essay examines the extent to which RMDPs and multi-level governance can explain observable changes in bank value in a number of ways. First, this essay seeks to examine whether RMDPs can influence the value of banks. The second objective is to examine how NGQM may affect the bank value. Finally, this essay explores the relationship between operating in better- or poorly-governed countries and the market value of banks. The results confirm the substantial role of risk disclosures and multi-level governance in improving bank valuation in MENA. More specifically, the results indicate that market valuation is higher in banks with bigger foreign ownership, board size, board independence, Islamic governance, and NGQM. The results also show a significant negative relationship between CEO power and bank value. The research’s empirical findings are largely in line with the predictions of the multi-theoretical framework that incorporates insights from agency, signalling, legitimacy, institutional, and resource dependence theories. The study findings are robust to alternative firm- and country-level controls, alternative multi-level governance mechanisms, risk disclosure proxies, alternative estimation techniques, and endogeneity problems. In doing so, this study extends, as well as contributes to the banking and governance literature in a number of ways. First, to the best of the researcher’s knowledge, this thesis provides a first-time cross-country evidence on the level of risk disclosures in MENA countries, especially following the 2007/08 financial crisis in the banking industry. Second, this thesis offers first-time evidence on the informativeness of Islamic governance quality and risk disclosures from equity and debt markets. Third, this thesis offers evidence and extends prior research on the influence of multi-level governance on bank value, and credit ratings, using a multi-theoretical framework. Fourth, the study offers first-time evidence on the effect of national governance quality on banks’ risk disclosures, credit ratings, and bank value