Template-Type: ReDIF-Article 1.0 Author-Name: Martin Freedman Author-X-Name-First: Martin Author-X-Name-Last: Freedman Author-Name: Ora Freedman Author-X-Name-First: Ora Author-X-Name-Last: Freedman Author-Name: Jin Dong Park Author-X-Name-First: Jin Dong Author-X-Name-Last: Park Author-Name: A.J. Stagliano Author-X-Name-First: A.J. Author-X-Name-Last: Stagliano Title: Accounting by companies for the Kyoto Protocol in the EU Abstract: In attempting to achieve the goals of the Kyoto Protocol, the European Union agreed to reduce member countries' greenhouse gas emissions to 92% of their 1990 levels by the end of 2012. We assess here the carbon emissions for companies from those countries that were among the largest emitters of carbon in the EU. This evaluation includes emissions in 2008 and 2012 together with disclosures regarding environmental performance that these companies made in the two years. Upon analysing 141 companies from seven of the EU countries, we found that the firms that emitted the most carbon effluents in their home country and that voluntarily disclosed the greatest amount about their carbon emissions also were the ones that provided the most extensive disclosures concerning company environmental performance. These results appear to support legitimacy concepts as the basis for which voluntary reporting will be generated. Journal: Int. J. of Accounting and Finance Pages: 1-23 Issue: 1 Volume: 10 Year: 2020 Keywords: carbon disclosure; carbon emissions; Kyoto Protocol; sustainability accounting; environmental accounting. File-URL: http://www.inderscience.com/link.php?id=111224 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:intjaf:v:10:y:2020:i:1:p:1-23 Template-Type: ReDIF-Article 1.0 Author-Name: Norma Patricia Caro Author-X-Name-First: Norma Patricia Author-X-Name-Last: Caro Author-Name: Margarita Diaz Author-X-Name-First: Margarita Author-X-Name-Last: Diaz Author-Name: Fernando Garcia Author-X-Name-First: Fernando Author-X-Name-Last: Garcia Author-Name: Marcela Porporato Author-X-Name-First: Marcela Author-X-Name-Last: Porporato Title: Mixed logistic model with two independent random coefficients for financial crisis prediction: Argentinean companies Abstract: The paper develops a mixed logistic financial distress prediction model with two independent random coefficients and validates it for public Argentinean companies. This study complements existing literature on bankruptcy prediction in emerging economies advancing the application of contemporary econometric methods (Caro et al., 2013). Anticipating bankruptcy risks increases portfolios' profitability. Emerging economies and frontier markets differ from developed economies in political, cultural, social and institutional terms. Given those differences, investors and lenders need specific bankruptcy and financial distress prediction models. The model developed achieves an excellent performance using financial statements from firms listed in the Buenos Aires Stock Exchange during 1993-2000 with ratios accepted in the literature (Altman, 1993; Jones and Hensher, 2004). Results show that profitability, assets turnover and cash flow from operations reduce the likelihood of financial distress while leverage increases it for companies operating in a frontier market such as Argentina. Journal: Int. J. of Accounting and Finance Pages: 40-63 Issue: 1 Volume: 10 Year: 2020 Keywords: mixed logistic model; financial statements; financial ratios; financial distress; bankruptcy prediction; Latin America; Argentina; accounting. File-URL: http://www.inderscience.com/link.php?id=111228 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:intjaf:v:10:y:2020:i:1:p:40-63 Template-Type: ReDIF-Article 1.0 Author-Name: Pramod Kumar Naik Author-X-Name-First: Pramod Kumar Author-X-Name-Last: Naik Title: Determinants of banks' debt: dynamic panel evidence from Indian public sector banks Abstract: This study aims at examining the major determinants of debt financing of Indian public sector banks. To achieve the study objective, we form a balanced panel by extracting data of 26 public sector banks (PSBs) of India over 12 years from 2005 to 2016. The study employs the pooled OLS, and both the static and the dynamic panel data techniques, such as the random-effects model and system GMM model for the empirical analysis. The analysis reveals that the bank's debt financing is significantly determined by bank size, tangibility, liquidity, and financial strength. It shows that bank size, liquidity, and tangibility are positively related to banks' debt, whereas financial strength and economic growth are negatively related to the banks' debt level. It is also found that the debt level is consistent over time; however, the speed of adjustment is around 92% per annum. This implies that the PSBs adjust their actual debt level towards their optimal debt level at a faster rate. Journal: Int. J. of Accounting and Finance Pages: 24-39 Issue: 1 Volume: 10 Year: 2020 Keywords: debt financing; dynamic panel data; system GMM; public sector banks; PSBs; India. File-URL: http://www.inderscience.com/link.php?id=111231 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:intjaf:v:10:y:2020:i:1:p:24-39 Template-Type: ReDIF-Article 1.0 Author-Name: Ayca Akyatan Author-X-Name-First: Ayca Author-X-Name-Last: Akyatan Author-Name: Mustafa Koray Cetin Author-X-Name-First: Mustafa Koray Author-X-Name-Last: Cetin Title: Return prediction with time varying betas: a research in BIST Abstract: In the present study, dynamic versions of beta, which is the risk measure of investment instruments, have been employed to predict daily return of 30 random portfolios made of 154 stocks transacted in BIST ALL between dates 02.01.2003 and 29.08.2013. BIST 100 Index has been employed as the market portfolio. The predictions have been made with rolling regression and MGARCH methods. The performance of return predictions of dynamic betas has been compared to the performance of return predictions of traditional beta. Dynamic betas have been estimated with rolling regression, MGARCH DVECH, MGARCH DBEKK, MGARCH CCC and MGARCH DCC. In the study, it has been identified that the return prediction made with dynamic betas performed better than the predictions made with traditional beta. However, the return predictions made with CCC betas have been superior to other dynamic betas in terms of beating the performance of traditional beta. Journal: Int. J. of Accounting and Finance Pages: 64-86 Issue: 1 Volume: 10 Year: 2020 Keywords: risk; return prediction; conditional covariance; rolling regression; MGARCH; dynamic beta; dynamic conditional correlation; DCC; constant conditional correlation; CCC; diagonal BEKK; DBEKK; BIST. File-URL: http://www.inderscience.com/link.php?id=111240 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:intjaf:v:10:y:2020:i:1:p:64-86