درخت حوزه‌های تخصصی

Financing decisions such as capital structure have gained much attention in literature of financial development over the last decade. Capital structure deviations from its optimal level can occur for various reasons. According to the dynamic trade-off theory, continuous adjustment of capital structure to maximize company value is essential. If companies adjust their capital structure quickly towards the target leverage, past financing activities and historical market conditions will only have short-term effects on the current capital structure. Conversely, if companies adjust their capital structure slowly, the opposite is true. We examine the relationship between organizational and performance characteristics and capital structure, as well as the speed of adjustment, in financial and non-financial firms listed on the Tehran Stock Exchange and compare these effects between the two categories of companies. The statistical population consists of companies listed on the Tehran Stock Exchange from 2017 to 2022. The results show that the speed of capital structure adjustment is lower in the financial sector compared to the non-financial sector. Additionally, three performance variables—profitability, growth opportunities, and liquidity—are statistically significant and impact capital structure and its adjustment speed in both financial and non-financial sectors. However, the growth opportunities variable has a different effect direction in the financial sector compared to the non-financial sector, while the age variable does not have a significant effect. Regarding organizational characteristics—complexity, institutional ownership, and size—only organizational complexity in the financial sector is significant at a 95% confidence level, while all organizational characteristics are significant in the non-financial sector

The increased number of restatements in recent years has increased the wor-ries about the quality of financial reporting among the beneficiary groups. The pres-ence of prior period adjustments and, subsequently, the financial restatements have a negative impact on the relatedness and reliability of the financial state-ments. The present study is aimed to present an appropriate criterion for predict-ing the financial restatements based on the Beneish model and its indices in companies admitted to the Tehran Stock & Exchange between 2009 and 2020. For this purpose, a total of 265 companies were selected considering the limitations. Also, the model estimation was per-formed using Beneish's primary model, a meta-heuristic neural network model, and optimization through genetic programming. As indicated by the obtained results based on the confusion matrix, the efficiency of the pro-posed model derived from the enhanced Beneish model with a genetic algo-rithm had a total prediction accuracy of 73.21%, which was the highest prediction power compared to the Beneish Model .

One of the main demands of investors (depositors and shareholders) of banks is transparency. However, in addition to the requirements for meeting this demand, measuring how to meet it has become challenging. So far, researchers have proposed different qualitative criteria for transparency. In this study, while introducing the correlation coefficient between book and market value at risk (VaRs) as a criterion of transparency, we seek to examine the stability of this criterion in different economic conditions. For this purpose, first, by using the e-garch model, the value at risk was estimated based on the balance sheet (book) information and also the market information of the banks' shares, then by calculating the correlation coefficients between book and market VaR’s under normal conditions, we predict book and market VaR’s using vector auto-regressive (VAR) models, along with defining three stress scenarios (Mild - Severe - hyper stress). We examined the significance of the difference between the calculated correlation coefficients in the three stress test modes. We thus tested the stability of the correlation coefficient of the defined scenarios. The findings showed that except for the correlation caused by the unemployment rate factor in mild and hyper-stress scenarios, in other cases, no evidence of H0 rejection was found, indicating the stability of the correlation coefficient between book and market VaRs as a measure of transparency.

In today's world, financial markets, as the main arteries of any country's economic system, have created an attractive environment for investors, and therefore it is necessary to identify the behavior of investors in this space and variables affecting prices and stock returns in these markets. The purpose of this study is to use the method of random dominance analysis and electrification technique as a new method in financial research to investigate the effect of calendar anomalies on the index returns of banks listed on the Tehran Stock Exchange in the period 2016-2020. Findings show that according to the three main hypotheses that there is a significant difference between the returns of the banking industry index on Saturdays to Wednesdays, in the first, second, third and fourth weeks of each month and in the months of April to March based on accuracy Market efficiency assumptions were formulated, all three hypotheses were rejected. It was also found that Wednesdays and the second weeks of each month are the best time to invest in the banking sector. In addition, June is the best and March is the most unsuitable month of the year to invest in this field.