Presenting a fuzzy model for fuzzy portfolio optimization with the mean absolute deviation risk function

The main purpose of this paper is portfolio optimization with the use of fuzzy method based on the mean absolute deviation risk function in firms listed in Tehran Stock Market. In the present research, for the purpose of fuzzy portfolio optimization the stock portfolio Value at Risk criterion and for calculation of this value the parametric method and for fuzzy optimization also the Hybrid intelligent algorithms (genetic algorithms and neural networks) have been used. For selecting the portfolio with 15 during the research time span (2005-2011) fuzzy optimization based on the following six criteria were used including Asymmetric Value at Risk, Symmetric Value at Risk , Interval Value at Risk (interval of 5%-95%), Interval Value at Risk (interval of 10%-90%), and Normal Value at Risk. Since the calculated probability ratio statistic Kupiec based on fuzzy optimization for the 6 above mentioned models is larger than the obtained critical value from chi-square distribution at the confidence level of 95%, the research hypothesis stating that the application of fuzzy optimization method improves the efficiency of portfolio in the actual world problems with lack of certainty was confirmed. Also, the results of the Kupiec probability ratio statistic indicate that the model of value at risk based on the mean absolute deviation risk function (MVAR) is more successful and have less failure comparing to other models, hence; the research hypothesis stating that fuzzy variables have a higher ability in modeling asymmetric uncertainties in financial domains is also confirmed

Presenting a fuzzy model for fuzzy portfolio optimization with the mean absolute deviation risk function

The main purpose of this paper is portfolio optimization with the use of fuzzy method based on the mean absolute deviation risk function in firms listed in Tehran Stock Market. In the present research, for the purpose of fuzzy portfolio optimization the stock portfolio Value at Risk criterion and for calculation of this value the parametric method and for fuzzy optimization also the Hybrid intelligent algorithms (genetic algorithms and neural networks) have been used. For selecting the portfolio with 15 during the research time span (2005-2011) fuzzy optimization based on the following six criteria were used including Asymmetric Value at Risk, Symmetric Value at Risk , Interval Value at Risk (interval of 5%-95%), Interval Value at Risk (interval of 10%-90%), and Normal Value at Risk. Since the calculated probability ratio statistic Kupiec based on fuzzy optimization for the 6 above mentioned models is larger than the obtained critical value from chi-square distribution at the confidence level of 95%, the research hypothesis stating that the application of fuzzy optimization method improves the efficiency of portfolio in the actual world problems with lack of certainty was confirmed. Also, the results of the Kupiec probability ratio statistic indicate that the model of value at risk based on the mean absolute deviation risk function (MVAR) is more successful and have less failure comparing to other models, hence; the research hypothesis stating that fuzzy variables have a higher ability in modeling asymmetric uncertainties in financial domains is also confirmed

Presenting a fuzzy model for fuzzy portfolio optimization with the mean absolute deviation risk function

The main purpose of this paper is portfolio optimization with the use of fuzzy method based on the mean absolute deviation risk function in firms listed in Tehran Stock Market. In the present research, for the purpose of fuzzy portfolio optimization the stock portfolio Value at Risk criterion and for calculation of this value the parametric method and for fuzzy optimization also the Hybrid intelligent algorithms (genetic algorithms and neural networks) have been used. For selecting the portfolio with 15 during the research time span (2005-2011) fuzzy optimization based on the following six criteria were used including Asymmetric Value at Risk, Symmetric Value at Risk , Interval Value at Risk (interval of 5%-95%), Interval Value at Risk (interval of 10%-90%), and Normal Value at Risk. Since the calculated probability ratio statistic Kupiec based on fuzzy optimization for the 6 above mentioned models is larger than the obtained critical value from chi-square distribution at the confidence level of 95%, the research hypothesis stating that the application of fuzzy optimization method improves the efficiency of portfolio in the actual world problems with lack of certainty was confirmed. Also, the results of the Kupiec probability ratio statistic indicate that the model of value at risk based on the mean absolute deviation risk function (MVAR) is more successful and have less failure comparing to other models, hence; the research hypothesis stating that fuzzy variables have a higher ability in modeling asymmetric uncertainties in financial domains is also confirmed

Effectiveness of Health Promoting Lifestyle Training in Health Hardiness, Self-compassion, and Behavioral Emotion Regulation in Patients with Type 2 Diabetes

Introduction and purpose: Patients with diabetes face many problems, especially in the field of health hardiness, self-compassion, and behavioral emotion regulation. As a result, the present research aimed to investigate the effectiveness of health-promoting lifestyle training in health hardiness, self-compassion, and behavioral emotion regulation in patients with type 2 diabetes. Methods: This study was conducted based on a semi-experimental pretest and posttest design with a control group. The statistical research population was patients with type 2 diabetes who were members of Ahvaz Diabetes Association in the winter of 2021. After reviewing the inclusion criteria, 50 people were selected as a sample with the available sampling method and randomly assigned to two equal groups (n=25 in each group). The experimental group underwent eight 90 minutes sessions of  Alijani et al. health-promoting lifestyle training (2015), and the control group remained on the waiting list for training. Data were collected with the demographic information form, revised health hardiness inventory (Gebhardt et al., 2001), self-compassion scale (Neff, 2016), and behavioral emotion regulation questionnaire (Kraaij and Garnefski, 2019) and analyzed in SPSS software (version 19) using chi-square, independent t-test, and multivariate analysis of covariance. Results: The results demonstrated that the experimental and control groups did not significantly differ in terms of gender, education level, and age (P>0.05). The other findings pointed out that health-promoting lifestyle training led to increased health hardiness (F=575.597, P<0.001), self-compassion (F=283.882, P<0.001) and behavioral emotion regulation (F=633.967, P<0.001) in patients with type 2 diabetes (P<0.001). Conclusion: As evidenced by the results of the present study, health professionals and therapists can use the health-promoting lifestyle training method along with other training methods to improve the psychological characteristics of patients with type 2 diabetes, especially increasing health hardiness, self-compassion, and behavioral emotion regulation