مجله مدیریت دارایی و تامین مالی
سال یازدهم شماره 2 (پیاپی 41، تابستان 1402)

The purpose of this study is to investigate whether the increase in financing restrictions of companies has an effect on their financing strategies. Based on this, the strategies of company managers for financing were formulated in the form of their desire to finance internally instead of externally and through transactions with related parties and financing from debt instead of issuing shares in the capital market. In addition, this research, emphasizing the role of corporate governance and its effect on defined relationships, seeks to provide a reasonable answer in relation to the effect of corporate governance on reducing the adverse effect of financing restrictions on the financing strategies of companies. In this regard, the data of 150 companies listed in the Tehran Stock Exchange during 2015-2019 were selected, and according to the Kaplan/Zengales indices as a measure of supplier criteria governing the companies, research hypotheses were examined. The results of this study showed that financing restrictions have a negative and significant relationship with transactions with related parties and a positive and significant relationship with capital structure. In addition, corporate governance only has a moderating effect on the relationship between financing constraints and capital structure.

In this paper, the effect of a change in price limit on Iran’s stock market and its volatility was studied by applying two methods. One was Realized Variance (RV) before and after the price change was applied and the other was District Fourier Transform (DFT), which was applied to intraday price changes before and after changes occurred to the price limits so that the volatility could be studied at different frequencies. Using RV, it was found that the effects of a price limit change on all the markets and industries were not the same. As we witnessed, a change from a limit of ±10% to ±3% in the yellow market actually resulted in a more volatile market, while a change from a limit of ±10% to ±2% in the orange market did not result in a significantly more volatile market. The results of DFT showed that a tighter price limit increased the volatility; however, the effects were not the same at different frequencies. In conclusion, narrowing the price limit did not necessarily result in a decline in intraday volatility. Even in some cases, severe narrowing of the price limit could lead to an increase in the intraday volatility of stock prices.

Initial public offerings (IPO) is considered an important turning point in the investment literature. This type of stock not only removes the constraints of resources and capital limitations faced by companies and provides the necessary basis for providing liquidity to companies, but also offers a wide range of investment strategies to companies. Therefore, the main goal of the present study is to investigate the Granger causality relationship between the number of IPO and macroeconomic variables including industrial production, the interest rate on government debt, stock market, and stock market volatility using the Vector Auto Regression (VAR) model during the period 1991-2019 for the Tehran Stock Exchange. The results showed that the number of IPOs is the reason for the growth of industrial production and the fluctuations of the stock index. It was also found that the number of IPOs was the reason for the growth of the stock index and the fluctuations of the stock market and interest rate due to the government's debt. This means that macroeconomic variables such as industrial production, interest rates on government debt, and the stock market are sensitive to small impulses from the number of IPOs and can have macroeconomic consequences in Iran. Based on the results, it can be acknowledged that the financial resources obtained through the number of IPOs of the companies should not be spent at the expense of the government and it is better to spend the financial resources on the development of these companies and the stock market.

The present study compared the predictive performance of machine-learning models and statistical models for forecasting profit and operational cash flow by using a combination of accrual and cash variables. The research method encompassed 3 main stages: data set and variable selection, modeling, and estimation. The study focused on companies listed on the Tehran Stock Exchange (TSE), analyzing data from 184 companies over the period of 2012-2021. The findings indicated that accrual variables exhibited greater explanatory power than cash variables in predicting net profit and future operating cash flow. Furthermore, the comparison of machine-learning and statistical models for forecasting net profit and future operating cash flow revealed that the artificial intelligence approach exhibited superior capability. Specifically, symbolic regression among the machine-learning models and the probit model among the statistical models demonstrated higher performance. Additionally, the results indicated that certain statistical models outperformed some machine-learning models while, on average, machine-learning models outperformed statistical models.

The present study compared the predictive performance of machine-learning models and statistical models for forecasting profit and operational cash flow by using a combination of accrual and cash variables. The research method encompassed 3 main stages: data set and variable selection, modeling, and estimation. The study focused on companies listed on the Tehran Stock Exchange (TSE), analyzing data from 184 companies over the period of 2012-2021. The findings indicated that accrual variables exhibited greater explanatory power than cash variables in predicting net profit and future operating cash flow. Furthermore, the comparison of machine-learning and statistical models for forecasting net profit and future operating cash flow revealed that the artificial intelligence approach exhibited superior capability. Specifically, symbolic regression among the machine-learning models and the probit model among the statistical models demonstrated higher performance. Additionally, the results indicated that certain statistical models outperformed some machine-learning models while, on average, machine-learning models outperformed statistical models.

The purpose of this research was to identify the factors affecting financing of small and medium-sized enterprises in the Tehran Stock Exchange (TSE). The method of this study was to use the multiple regression model and panel data to test the hypotheses. The statistical population included 63 small and medium-sized companies admitted to the TSE, which were tested for the period of 2006 to 2021. The contribution was the use of market cap as a criterion for determining small and medium-sized companies. According to the findings, company size has a significant effect on internal financing. In addition, company size had a significant relationship with external financing through debt and share issuance. Also, there was a significant relationship between intangible assets and internal financing, while the ages of the small and medium sized enterprises did not have a significant relationship with external financing. It is suggested that small and medium-sized enterprises pay more attention to the significant variables for financing.