افشین باغفلکی

Based on IFRS laws, British companies have started providing their reporting systems according to International Standards Requirements regarding disclosing their financial derivatives since January 2005. In 2013, Iran revised its Accounting Standard No. 15 to include the derivative instruments. The present study aims at investigating the effect of this revision on financial derivatives and instruments, and the effect of earning management on the relationship between the level of financial derivatives and instruments and risk-adjusted discount rates. From generalized least squares regression panel data, it was found that based on the first hypothesis, the companies which disclose their financial instruments based on No. 15 internal standard have a lower risk- adjusted discount rate, implying an increase in profit and a price rise in the markets. The findings also confirmed the second hypothesis, attesting to the effect of earning management on the relation-ship between financial derivatives and instruments disclosure and excess return. Findings of the research third hypothesis represent that there is a direct meaningful relationship between disclosure level of financial instruments and company value. So, it can be concluded that instruments` disclosures and financial derivatives can decrease risk-adjusted discount rate and increase companies` values in terms of standard number 15.

Many studies have been conducted in the field of bankruptcy prediction; But in most of them only financial ratios are used. However, in Iran, many non-financial factors affect bankruptcy. The main purpose of this study is to develop a mathematical model in which financial and non-financial indicators such as management and economics factors are used to predict bankruptcy. In this study, 44 variables that had the greatest impact on bankruptcy forecast were selected and with confirmatory factor analysis, a questionnaire was developed and sent to experts in the fields of management, accounting and economics to rank the impact of these variables. The statistical sample of the study includes 200 bankrupt and non-bankrupt companies listed in the period 2009-2018. After collecting the questionnaires using the OLS regression estimation method, the variables that had a factor load of less than 0.5 were eliminated and in the final model 9 main variables. The research model identified 95% of bankrupt companies and 93% of non-bankrupt companies with 95.4% confidence. Then, for verification, two hypotheses were developed and the model of this research was compared with two existing models. The ability to distinguish bankrupt companies from non-bankrupt ones by our proposed model was 6% more accurate than the Pourheidari et al. model, and 9.4% more accurate than Altman’s model.