درخت حوزه‌های تخصصی

So far, distress prediction models have been based on balanced, such sampling is not consistent with the reality of the statistical community of companies. If the data are balanced, the bias in sample selection may lead to an underestimation of typeI error and an overestimation of the typeII error of models. Although imbalanced data-based models are compatible with reality, they have a higher typeI error compared to balanced data-based models. The cost of typeI error is more important to Beneficiaries than the cost of typeII error. In this study, for reducing typeI error of imbalanced data-based models, random and clustering-based undersampling were used. Tested data included 760 companies since 2007-2007 with 4 different degrees and the results of the H1 to H3 test represented them. In all cases of the typeI error, typeII error of balanced data-based models were lower and more, respectively, compared to imbalanced data-based models; also, in most cases, the geometric mean of balanced data-based models was higher compared to imbalanced data-based models, respectively. The results of testing H4 to H6 show that in most cases, typeI error, typeII error and the geometric mean criterion of models based on modified imbalanced data were less, more, and more, respectiively compared to the models based on imbalanced data, in other words, applying Undersampling methods on imbalanced training data led to a decrease in typeI error and an increase in typeII error and geometric mean criteria. As a result using models based on modified imbalanced data is suggested to Beneficiaries

The publication of financial statements, a vital corporate communication tool, significantly influences behavioral biases in both individuals and organizations. While the importance of behavioral factors for investors is well recognized, the impact of financial statement releases on these biases in the Iranian capital market has yet to be thoroughly examined. This study investigates the effect of financial statement releases on two key biases: loss aversion and overconfidence among investors. By employing both quantitative and qualitative research methods, comprehensive data was collected from the Iranian stock market. For the analysis of loss aversion, a sample of 111 companies over six years (2017–2022) was selected using the systematic elimination method, while overconfidence bias was assessed at the market level. An event study method was used to measure the impact of financial statement releases on these biases. To analyze loss aversion, a regression model using the Dynamic Panel Data (GMM) method was applied, and for overconfidence, a Vector Autoregression (VAR) model was estimated during the financial statement release period at the market level. The findings show that investor’s exhibit heightened loss aversion during financial statement releases, and overconfidence bias was also evident, with investors attributing positive market outcomes to themselves and acting more aggressively in subsequent transactions. Based on the results, investors should consider behavioral bias patterns in their decision-making and incorporate technical analysis. Additionally, companies should ensure transparency and accuracy in their financial statements while remaining attentive to market behaviors and reacting promptly to changes.

Companies pay attention to the value level of financial flexibility in making decisions related to optimizing investments and applying their net working capital policies. This issue will make profitable investment opportunities for companies more efficient and enable companies to gain more efficiency, as well as apply more optimal policies to keep cash. The purpose of this research is to investigate the effect of financial flexibility value and financial constraint on the speed of adjustment of net working capital, as well as the effect of financial constraint on the relationship between the value of financial flexibility and the speed of adjustment of net working capital in companies listed on the Tehran Stock Exchange. The appropriate pattern recognition test in combined data indicates the use of the regression model of the research using the panel data method with the fixed and random effects approach for the panel and pooled data patterns to estimate the regression model. The statistical sample includes 100 companies accepted to the Tehran Stock Exchange during the period from 2005 to 2020. The findings indicate that the value of financial flexibility has a positive and significant effect on the speed of adjustment of net working capital in the models of partial adjustments and error correction. Financial constraint has a positive and significant effect on the speed of net working capital adjustment, and it also has a positive and significant effect on the relationship between the value of financial flexibility and the speed of net working capital adjustment.