نادر رضایی

Negative relation between unemployment and inflation is known as the Philips model, which states, when inflation is high, unemployment gently decries and when unemployment is low, salaries will increase rapidly. While economic planners of the country tend to impart this situation highly, it would fall down, because frequent increase of inflation with the hope of keeping unemployment low permanently, ultimately will cause rise in expected inflation of organizations and lead to change in their recruitment decisions. In a section of this model, it has also analyzed, relation between income, investment, and consumption. Regular Philips model with derivative deals with first and second level of calculations, however this model has faced many weaknesses and deficiencies in economic cycle of Iran. Regarding its implementation in the economy of Iran, in this research, through substitution of Caputo fractional-order derivatives, we reach out “Philips model of Fractional-order”. Moreover, by numerical calculation of resulted equations and using Maple software, we over reach to Philips curve of Fractional-order in multiple various orders. According to achieved results, this model highly depends on derivation orders and different outcomes will be acquired through various derivation orders. Acquired model in the economy of Iran (situational format in Sugar and Sugar cube Industries) has been studied through phasic technic which proves meaningfulness of relations between variables of the model.

Stock market is affected by news and information. If the stock market is not efficient, the reaction of stock price to news and information will place the stock market in overreaction and under-reaction states. Many models have been already presented by using different tools and techniques to forecast the stock market behavior. In this study, the reaction of stock price in the stock market was modeled by the behavioral finance approach. The population of this study included the companies listed on the Tehran Stock Exchange. In order to forecast the stock price, the final price data of the end December, March, June, and September 2006-2015 and the stock prices of 2014 and 2015 were analyzed as the sample. In this study, Bayes' rule was used to estimate the probability of the model change. Through this rule, the probability of an event can be calculated by conditioning the occurrence or lack of occurrence of another event. The results of model estimation showed that there is the probability of being placed in high-fluctuated regimes (overreaction) and low- fluctuated (under-reaction of stock price despite the shocks entered to the stock market. In modelling with the 4-month final prices, it was proved that the real stock price had no difference from the market price.