درخت حوزه‌های تخصصی

The main criterion in investment decisions is to maximize the investors utility. Traditional capital asset pricing models cannot be used when asset returns do not follow a normal distribution. For this reason, we use capital asset pricing model with independent and identically asymmetric power distributed (CAPM-IIAPD) and capital asset pricing model with asymmetric independent and identically asymmetric exponential power distributed with two tail parameters(CAPM-AIEPD) to estimate return and risk. When the assumption of normality is violated, the first and second moments lose their efficiency in optimization and we need to use the third and fourth moments. For the first time, we propose independent and identically asymmetric exponential power distributed with two tail parameters. Then, we use higher moments optimization with unequal weights to optimize portfolios. The results indicate that capital asset pricing model with independent and identically asymmetric power distributed (CAPM-IIAPD) is better than asymmetric independent and identically asymmetric exponential power distributed with two tail parameters(CAPM-AIEPD) to estimate return and risk. Adjusted Sharp ratio in portfolio optimization in second moments are higher than others. Adjusted returns to risk in third and fourth moments in the CAPM-IIAPD model significantly differ from the CAPM-AIEPD model and have a better performance.

For many investors, it is important to predict the future trend of abnormal stock returns. Thus, in this research, the abnormal stock returns of the listed companies in Tehran Stock Exchange were tested since 2008- 2017 using three hypotheses. The first and second hypotheses examined the non-linearity and non-randomness of the abnormal stock returns ′ trend around the release date of annual financial statements, respectively. While, the third hypothesis tested the potential of the chaos model in explaining future abnormal returns based on the past abnormal returns around the release date of the annual financial statements. For this pur-pose, BDS, Teraesvirta Neural Network, and White Neural Network tests were used to investigate its non-linearity. In addition, Lyapunov exponent, correlation dimension, Dickey-Fuller, and Hurst exponent tests were used for testing non-randomness and the fitness of AR, SETAR, and LSTAR models to determine the optimal model in explaining the abnormal returns utilizing R software. Results of these tests represented a non-linear and non-random process and chaos in the abnormal stock returns, implying the predictability of abnormal stock returns. Also, among three used chaos models, the LSTAR model had lower error and more predictability than the other two models.

both academic and auditing firms have been searching for ways to detect corporate fraud. The main objective of this study was to present a model to detect financial reporting fraud by companies listed on Tehran Stock Exchange (TSE) using genetic algorithm. For this purpose, consistent with theoretical foundations, 21 variables were selected to predict fraud in financial reporting that finally, using statistical tests, 9 variables including SALE/EMP, RECT/SALE, LT/CEQ, INVT/SALE, SALE/TA, NI/CEQ, NI/SALE, LT/XINT, and AT/LT were selected as the potential financial reporting fraud indexes. Then, using genetic algorithm, the final model of fraud detection in financial reporting was presented. The statistical population of this study included 66 companies including 33 fraudulent and 33 non-fraudulent companies from 2011 to 2016. The results showed that the presented model with the accuracy of 91.5% can detect fraudulent companies. These findings extend financial statement fraud research and can be used by practitioners and regulators to improve fraud risk models.