درخت حوزه‌های تخصصی

Our main aim was to identify and prioritize the obstacles to the implementation of decent work in the industry. The current research was applied in terms of purpose. It was also descriptive survey in terms of collecting data. Due to the large extent of industry sector actors, only one side of the main actors' triangle in the industrial sector in Yazd province was selected as the statistical sample.This side of triangle included the heads of unions and workers’ representatives in the industry sector. Thus the data was collected purposefully from this sample. To collect data, in the first step, obstacles to the decent work and its components were identified based on library studies and in-depth initial interviews. In the second step, though, using the Delphi-Fuzzy method and the opinion of some of the experts in the industry sector, the identified obstacles in the first step were analyzed and summarized. In the third step, a questionnaire was designed for ranking the identified obstacles based on the Fuzzy Best-Worst Method. The questionnaire was distributed among a sample of 130 members, of which 80 questionnaires were returned. The final findings of the current study indicated that from the workers’ point of view, the shortcoming in upstream laws, lack of a trust in dialogues, allocating welfare budget to unrelated areas, government-owned industries and lack of trust in other members (workers, employers, and government), were the most difficult obstacles in the implementation of decent work.

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.

Study of volatility has been considered by the academics and decision makers dur-ing two last decades. First since the volatility has been a risk criterion it has been used by many decision makers and activists in capital market. Over the years it has been of more importance because of the effect of volatility on economy and capital markets stability for stocks, bonds, and foreign exchange markets. This research first deals with the evaluation of 8 various models to forecasting volatility of stock index using daily data of Tehran stock exchange. The used models include simple ones such as random walk as well as more complex models like Arch and Garch group. Forecasting volatility index method is developed in this paper. This method is based a random walk using a fuzzy logic approach. This method is used to fore-casting volatility of Iran stock exchange index. The proposed method is assessed by comparing other methods such as Moving Average, Random walk… Results show that our proposed method is compatible with existent methods.