This study constructs a proposed model to investigate the link between environmental,social,and governance(ESG)disclosures and ESG scores for publicly traded companies in the Borsa Istanbul Sustainability(XUSRD)index....This study constructs a proposed model to investigate the link between environmental,social,and governance(ESG)disclosures and ESG scores for publicly traded companies in the Borsa Istanbul Sustainability(XUSRD)index.In this context,this study considers 66 companies,examining recently structured ESG disclosures for 2022 that were published for the first time as novel data and applying a multilayer perceptron(MLP)artificial neural network algorithm.The relevant results are fourfold.(1)The MLP algorithm has explanatory power(i.e.,R^(2))of 79% in estimating companies’ESG scores.(2)Common,environment,social,and governance pillars have respective weights of 21.04%,44.87%,30.34%,and 3.74% in total ESG scores.(3)The absolute and relative significance of each ESG reporting principle for companies’ESG scores varies.(4)According to absolute and relative significance,the most effective ESG principle is the common principle,followed by social and environmental principles,whereas governance principles have less significance.Overall,the results demonstrate that applying a linear approach to complete deficient ESG disclosures is inefficient for increasing companies’ESG scores;instead,companies should focus on the ESG principles that have the highest relative significance.The findings of this study contribute to the literature by defining the most significant ESG principles for stimulating the ESG scores of companies in the XUSRD index.展开更多
Today＇s global trends need to be clearly explained to enable the efficient functioning of capital markets for the purpose of the country＇s economic development. Promotion of a strong internal audit function plays a ...Today＇s global trends need to be clearly explained to enable the efficient functioning of capital markets for the purpose of the country＇s economic development. Promotion of a strong internal audit function plays a key role in assisting the board to discharge its governance responsibilities. The internal audit needs to exert its important function for refining corporate governance procedures, improving internal control, and strengthening risk management. The rules concerning internal auditing issued by the Banking Regulation and Supervision Agency, Capital Markets Board of Turkey, and other public societies have contributed to the development of internal auditing in Turkey. The Capital Markets Board of Turkey published “Communiqu6 Serial： IV, Noi 56 on Identification and Application of Corporate Governance Principles” （Official Gazette dated December 30, 2011, No. 28158）. In Part 4.2.4 of these principles, it was stated that “The board of directors supervise the efficiency of risk management and internal control systems at least once a year. Information about existence, operation, and efficiency of internal control and internal audit is given by annual report”. The objective of this study is to analyze the structure of internal audit function-related information on the annual reports of companies that are included in the Borsa Istanbul. Annual reports of 192 manufacturing companies listed on Borsa Istanbul were examined by content analysis method.展开更多
In this study,the hourly directions of eight banking stocks in Borsa Istanbul were predicted using linear-based,deep-learning(LSTM)and ensemble learning(Light-GBM)models.These models were trained with four different f...In this study,the hourly directions of eight banking stocks in Borsa Istanbul were predicted using linear-based,deep-learning(LSTM)and ensemble learning(Light-GBM)models.These models were trained with four different feature sets and their performances were evaluated in terms of accuracy and F-measure metrics.While the first experiments directly used the own stock features as the model inputs,the second experiments utilized reduced stock features through Variational AutoEncoders(VAE).In the last experiments,in order to grasp the effects of the other banking stocks on individual stock performance,the features belonging to other stocks were also given as inputs to our models.While combining other stock features was done for both own(named as allstock_own)and VAE-reduced(named as allstock_VAE)stock features,the expanded dimensions of the feature sets were reduced by Recursive Feature Elimination.As the highest success rate increased up to 0.685 with allstock_own and LSTM with attention model,the combination of allstock_VAE and LSTM with the attention model obtained an accuracy rate of 0.675.Although the classification results achieved with both feature types was close,allstock_VAE achieved these results using nearly 16.67%less features compared to allstock_own.When all experimental results were examined,it was found out that the models trained with allstock_own and allstock_VAE achieved higher accuracy rates than those using individual stock features.It was also concluded that the results obtained with the VAE-reduced stock features were similar to those obtained by own stock features.展开更多
文摘This study constructs a proposed model to investigate the link between environmental,social,and governance(ESG)disclosures and ESG scores for publicly traded companies in the Borsa Istanbul Sustainability(XUSRD)index.In this context,this study considers 66 companies,examining recently structured ESG disclosures for 2022 that were published for the first time as novel data and applying a multilayer perceptron(MLP)artificial neural network algorithm.The relevant results are fourfold.(1)The MLP algorithm has explanatory power(i.e.,R^(2))of 79% in estimating companies’ESG scores.(2)Common,environment,social,and governance pillars have respective weights of 21.04%,44.87%,30.34%,and 3.74% in total ESG scores.(3)The absolute and relative significance of each ESG reporting principle for companies’ESG scores varies.(4)According to absolute and relative significance,the most effective ESG principle is the common principle,followed by social and environmental principles,whereas governance principles have less significance.Overall,the results demonstrate that applying a linear approach to complete deficient ESG disclosures is inefficient for increasing companies’ESG scores;instead,companies should focus on the ESG principles that have the highest relative significance.The findings of this study contribute to the literature by defining the most significant ESG principles for stimulating the ESG scores of companies in the XUSRD index.
文摘Today＇s global trends need to be clearly explained to enable the efficient functioning of capital markets for the purpose of the country＇s economic development. Promotion of a strong internal audit function plays a key role in assisting the board to discharge its governance responsibilities. The internal audit needs to exert its important function for refining corporate governance procedures, improving internal control, and strengthening risk management. The rules concerning internal auditing issued by the Banking Regulation and Supervision Agency, Capital Markets Board of Turkey, and other public societies have contributed to the development of internal auditing in Turkey. The Capital Markets Board of Turkey published “Communiqu6 Serial： IV, Noi 56 on Identification and Application of Corporate Governance Principles” （Official Gazette dated December 30, 2011, No. 28158）. In Part 4.2.4 of these principles, it was stated that “The board of directors supervise the efficiency of risk management and internal control systems at least once a year. Information about existence, operation, and efficiency of internal control and internal audit is given by annual report”. The objective of this study is to analyze the structure of internal audit function-related information on the annual reports of companies that are included in the Borsa Istanbul. Annual reports of 192 manufacturing companies listed on Borsa Istanbul were examined by content analysis method.
文摘In this study,the hourly directions of eight banking stocks in Borsa Istanbul were predicted using linear-based,deep-learning(LSTM)and ensemble learning(Light-GBM)models.These models were trained with four different feature sets and their performances were evaluated in terms of accuracy and F-measure metrics.While the first experiments directly used the own stock features as the model inputs,the second experiments utilized reduced stock features through Variational AutoEncoders(VAE).In the last experiments,in order to grasp the effects of the other banking stocks on individual stock performance,the features belonging to other stocks were also given as inputs to our models.While combining other stock features was done for both own(named as allstock_own)and VAE-reduced(named as allstock_VAE)stock features,the expanded dimensions of the feature sets were reduced by Recursive Feature Elimination.As the highest success rate increased up to 0.685 with allstock_own and LSTM with attention model,the combination of allstock_VAE and LSTM with the attention model obtained an accuracy rate of 0.675.Although the classification results achieved with both feature types was close,allstock_VAE achieved these results using nearly 16.67%less features compared to allstock_own.When all experimental results were examined,it was found out that the models trained with allstock_own and allstock_VAE achieved higher accuracy rates than those using individual stock features.It was also concluded that the results obtained with the VAE-reduced stock features were similar to those obtained by own stock features.
