probit regression

The purpose of this paper is to model the relationship between cognitive abilities and portfolio returns for portfolio managers and investors, with a focus on the dimensions of cognitive bias. The problem addressed in this study is to investigate the effectiveness of decision-making behavior among portfolio managers in the capital market of Iran, based on the dimensions of persistence and representation of cognitive bias in investors.

This study employs an applied research approach with a descriptive objective. Data collection was conducted using a series of tests, including the Cognitive Reflection Test (CRT), where one question (Linda's problem) was utilized to assess communication sophistication bias, and two additional questions were used to evaluate anchor bias. Gerter’s (1992) test was applied to measure long-term bias, while an imagery-based task (a guessing game involving a ball and bag) was administered to a sample of 30 managers from various companies. Additionally, a questionnaire containing 15 questions was given to 302 capital market professionals, including investment managers, analysts, and traders who worked in portfolio management companies and were selected as a statistical sample. The questionnaire included questions about decision-making power, investment activities, organizational position, yield of the past year, and investment strategies. To analyze the data and test the hypotheses, the chi-square test, Mann-Whitney test, and probit regression model were used.

The results showed that a one-unit increase in the decision-making criteria of portfolio management experts leads to a 0.27% increase in investment return. Additionally, the level of cognitive ability and decision-making skills of males and females showed a significant difference, and the level of cognitive ability and decision-making skills of basket operators with different efficiency levels also showed a considerable difference. Furthermore, the results indicated that 40.4% of the participants in the research have high cognitive ability. However, the criteria introduced for cognitive bias, such as representational bias and procrastination, harmed investment returns.

This study evaluated the dimensions of lingering bias, typology, reliance and adjustment, and sophistication in the decision-making of experts. The results showed that the criteria of cognitive ability and decision-making power of expert portfolio managers had a positive and significant effect on investment performance. An increase in the level of cognitive ability leads to an increase in performance by 0.53%, and a one-unit increase in the decision criteria of portfolio management experts leads to a 0.27% increase in investment return. However, the criteria introduced for cognitive bias, such as representational bias and procrastination, hurt investment returns.

This study examines the relationship between volatility and the probability of occurrence of expected extreme positive and negative returns in The thehran security market.‌‌ The purpose of this research is optimation portfolios via hold positive return and eleminate nagative return.‌‌ Two measures of volatility and the factor of expected shortfall are examined:‌‌ conditional volatility calculated using an EGARCH model and idiosyncratic volatility based on the Fama and French five-factor model and expected shortfall calculate with smi-parametric metod.‌‌ This paper is based on regresion probit‌ model and data use from 1382 to 7/1397.‌‌ A significantly positive relationship is observed between a firm’s idiosyncratic volatility and the probability of occurrence of an extreme return.‌‌ Other firm characteristics, including firm price, volume and Book-to-Market ratio, are also shown to be significantly related to firm extreme returns.‌‌ The relationship Between the age factor and exterem return is not found relation.‌‌ The effects of conditional and‌ expected shortfall are mixed.‌‌

Given the nature of banking industry activities, which mainly relate to the allocation of resources, credit risks are increasingly faced by this industry. Therefore, knowing the origin of credit risk and its estimation are always fundamental issues for this industry. In this regard, in order to solve this problem in Bank Sepah, this study aims to identify the features effective to credit risk of real customers of Bank Sepah as well as to design a model to predict the probability of credit risk default by employing genetic algorithm and probit regression models. The data of this research is gathered using the credit facilities paid to persons in 2016. Among all the credit facilities paid to real people in 2016, two samples each of size 3600 (for fitting the model) and two sample each of size 400 (in order to verify the model by the ROC curve) were randomly selected. Meanwhile, MATLAB software has been used to analyze the data. The result of the study shows that the genetic algorithm (GA) method has the ability to determine the variables in three difference levels based on the degree of importance. The results also show that area under the ROC curve in the GA method is equal to 0.92 but in the probit regression method, it is equal to 0.72 demonstrating the higher ability to predict the likelihood of facility failure in the GA method compared to the probit regression method. The results of the ROC curve verification show that the GA correctly predicts 91.8% of cases compared to 90.0% in probit regression.