Financial Distress Prediction

Examining the theoretical foundations of earnings management shows that companies have stronger incentive to use earnings management at the pre-bankruptcy stage. Consequently, accounting-based determinants retrieved from financial statements may be biased factors for financial distress. In this paper, we investigate whether taking into account real earnings management improves specification of accounting-based financial distress prediction models. We test whether the inclusion of such attributes in bankruptcy prediction models improves their predictive ability. We use a sample of listed manufacturing companies in the Iran Stock Exchange during 2008 - 2017. Our findings suggest that the inclusion of earnings management significantly increases the predictive ability of accounting-based financial distress prediction models. Our results show that the real earnings management can provide predictive signals concerning a financial distress and that an abnormal cash flow which proxies for real earnings management can play a relevant role in early warnings of financial distress. These results are of interest to market participants, auditors, regulating authorities, banks and other financial institutions that are interested in financial distress assessment

Due to their inherent nature in the economy, banks have a fundamental and significant responsibility for capital formation. Therefore, evaluating their performance can help decision makers find the optimal solution and prevent financial distress. The purpose of this study is to evaluate the performance and forecast financial distress of banks listed on the Tehran Stock Exchange, based on CAMELS indicators and ِ Data Envelopment Analysis model.  First, using the data of 17 banks in the fiscal year 2018, 5 levels of determining the health of banks, in the form of differences between the performance of these banks in terms of capital adequacy, quality of assets, quality of management, Earning and liquidity and  sensitivity to market risk, It was found. And the studied banks were divided into two groups: healthy and helpless, based on CAMELS indices. Then, according to the effects of financial distress on banks, financial distress was predicted by Data Envelopment Analysis model slacks-based on measure of efficiency (SBM) and with a different approach. The results show that 61% of the predictions were correct by DEA technique and 39% of them were incorrect. Also, the results of this study showed that CAMELS financial ratios can be a good assessor for banks' financial distress.