درخت حوزه‌های تخصصی

Technical analysis is constituted as an approach in the market analysis which is based on the study of pricing behavior and shares size in the past and price determination and its procedure in the future. Algorithmic transactions are growing rapidly in order to automate business strategies, given the arrival of computer-based technologies and the rapid processing of bulky information. Trading systems combine input information and ultimately identify the time of purchase and sale by forming one signal. In this paper, the training system is a kind of fuzzy inference system that combines fuzzified RSI and SO signals from technical analysis. The system’s trade rules database (selling, buying, and holding) would be calculated based on an optimization process using PSO. This optimization process should be repeated at certain intervals to keep the system up to date. This process is called the corrective property of systems. The findings on the overall index in the period 2001/3/21-2019/3/20 indicate that the system having optimized training on training data has an average daily return of /0027, risk-taking of /0065 and the daily sharp ratio of /42. Concerning the index of return and sharp ratio, the findings reveal that the system outperforms the signals and the market performance.

It is a well-established empirical fact that economic growth and development brings about major changes in sectorial output, employment, and consumption structure. In the context of economic development discussion, structural change refers to the change in key components of macroeconomic indicators, including national production and expenditure, exports and imports, population, and the sectorial composition of labor. Considering the importance of human capital for economic growth and development, the most important of the above factors is the labor market and the sectorial composition of employment. Another important dimension of economic development is economic well-being. In this study, we formulated and calculated two indicators called the Coefficient of Structural Change of Iran (CSCI) and the Index of Economic Well-Being of Iran (IEWBI), and then used the vector autoregressive (VAR) modeling method and the software EViews v.10 to analyze the impact of structural changes of Iranian economy on IEWBI. The results indicate the positive effect of structural changes and therefore development policies on the economic well-being of Iran when the weighting scheme of IEWBI was biased in favor of the consumption flow dimension, and the negative effect in other cases. JEL: C39, I31, O11.

Given the increasing importance of achieving low and stable inflation rate during the last decades, adopting the most suitable practices to implement monetary policies has always been of concern by monetary authorities of different countries. Inflation targeting (IT) regime is the most recent strategy to guide monetary policies that have been introduced following the occurrence of exchange rate targeting and monetary targeting problems. In this respect, in the present research, the performance of a number of IT countries versus that of the non-targeting ones was first investigated employing the difference in difference (DID) method. Then, considering monetary and oil shocks, IT performance in production gap in the economy of Iran was practically examined using smooth transition autoregressive regression (STAR) model. The Results indicated that implementation of IT policy was successful in applying the four main and most influential indicators in the production of treatment countries. In addition, it was revealed that variables such as IT, oil shock, and facility and exchange rates had a statistically negative effect on production shock in the economy of Iran. However, the monetary shock was found to have a statistically positive impact on production shock. Therefore, according to the findings regarding the successful performance of targeting countries, policymakers are recommended to implement IT policy during a long-term interval in order to stabilize it and also provide its prerequisites in the economy of Iran. JEL classification: E31, E58, E52.