خلاصة:
In this paper، we used Iranian stock market data (TEPIX) to identify the bull and bear phases and to analyze their characteristics during the period of 1991-2017 using a non-parametric approach. Having determined bull and bear phases، we calculated the following five indices (durations، amplitudes، cumulative movements، excess movements and ratio of big expansions and contractions) using a non-parametric approach. The results showed that there are some common facts about the cycles that average duration and amplitudes of the bull market are longer than that of the bear market which are also true in Iran Stock Market. However، the excess index of the bull market is not larger than that of the bear market in Iran. We also found that bull phases are longer and more intense (larger amplitude) than bear phases and the rate of the growth index in the bull periods is higher than the rate of its slowdown in bear periods
ملخص الجهاز:
For this purpose, using a non-parametric approach, the bull and bear market cycles were identified and five indices were calculated (average cycle duration, range of fluctuation, accumulated movements, excess index and ratio of boom and recession).
Many researchers have tried to determine the turning points of business cycles (Bry and Boschan, 1971; King and Placer, 1994; Watson, 1994; Land and Timmermann, 2004 and Harding and Pagan, 2006), and some have also used these methods to determine the turning points of different stock markets in order to examine the characteristics of these cycles and their significance (Biscarelli and Garcia, 2003; Gonzalez, Paul, Shi and Wilson, 2007; Claessens, Kose and Trons, 2012; Barro, 2012; Gunay, 2014; Zang and Beck, 2015; Pandy, Patnaik and Shah, 2017).
Many researchers have tried to determine the turning points of business cycles (Bry and Boschan, 1971; King and Placer, 1994; Watson, 1994; Land and Timmermann, 2004 and Harding and Pagan, 2006), and some have also used these methods to determine the turning points of different stock markets in order to examine the characteristics of these cycles and their significance (Biscarelli and Garcia, 2003; Gonzalez, Paul, Shi and Wilson, 2007; Claessens, Kose and Trons, 2012; Barro, 2012; Gunay, 2014; Zang and Beck, 2015; Pandy, Patnaik and Shah, 2017).
After coding, using the research data (monthly total price index from Farvardin 1370 to Tir 1396), we identify the turning points, then we proceed to calculate the average of the desired indicators (time period, excess index, accumulated movements, range of oscillation, and ratio of major booms and busts).
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