درخت حوزه‌های تخصصی

With the onset of the financing crisis in the real sector of the economy and the intensification of shortcomings in the banking system of Iran in recent years, the issue of capital raising has been seriously considered by economic and banking experts to improve the health and stability of banks and their credit provision. What has been critical in this regard is the effects of capital raisings on the liquidity creation and credit provision of the banks. Therefore, using data of 29 banks during the period 2008 to 2017, and the econometric method of dynamic panel data, the relationship between the capital raising and the liquidity creation and credit providing has been studied. For this purpose, in two steps, first, the effect of capital raising in general on the liquidity creation and credit of banks, and in the next step, the impact of different methods of capital raising on these factors has been studied. The results of the estimations show that an increase in total capital will lead to a rise in liquidity creation in banks. In examining the effect of various methods of capital raising on liquidity creation, however, only the method of cash contribution and retained earnings can have a positive effect on liquidity creation, and other techniques show adverse effects. Also, based on the results of the proposed models, banks' capital raising has harmed their credit provision. It means that banks have allocated the resources from capital raisings to things other than providing loans to customers.

In this research, we use a pair trading strategy to make a profit in an emerging market. This is a statistical arbitrage strategy used for similar assets with dissimilar valuations. In the present study, smooth transition heteroskedastic models are used with the second-order logistic function for producing thresholds as trading entry and exit signals. For generating upper and lower bounds, we apply the rolling window approach and one-step-ahead quantile forecasting. Markov chain Monte Carlo sampling method is used for optimizing the parameters. Also, passive strategy in the out-of-sample period is used to compare the profits. The population consists of 36 daily stock returns in Tehran Stock Exchange. Then, we select ten pairs from these stocks and use Minimum Square Distance method, and five pairs from one industrial sector. Finally, we see strategy1 and 2 have positive returns in the out-of-sample period, and they produce higher returns than passive strategy.

One of the significant problems of banks and investors in Iran is the lack of precise awareness about the financial performance of each bank and the roadmap for improving the conditions. Besides, the undesirable status of the financial performance of banks becomes evident only when the improvement of conditions is complicated. In this paper, a data mining-based early warning system (EWS) model has been presented to capture the financial performance of banks. To design this model, the CHAID decision tree has been used. Using this model, the banks have been classified as poor, medium, and good regarding financial performance, and the roadmap to achieving the desirable status has been determined. For this purpose, 13 Iranian banks have been investigated within the years 2003-2017. Eventually, the results obtained from the decision tree have been compared with the findings achieved from the CAMELS model. Based on the designed decision tree, 8 profiles have been extracted; 2 representing good, 3 medium, and 3 poor financial performance. Based on these profiles, according to the latest reports published by the studied banks, eight banks have a mediocre financial performance while five banks suffer poor financial performance. According to these profiles, four variables of the asset to shareholders’ equity ratio, the shareholders’ equity to loans ratio, the long-term debt to equity ratio, and liquidity coverage ratio were identified as the most relevant variables associated with the financial performance of banks.