Investigating the Effect of Price Acceleration in Tehran Stock Exchange based on Risk and Under Reaction

Document Type : Research Paper

Authors

1 Department of Management, Faculty of Economics and Social Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran

2 Department of Management, Shahid Chamran University of Ahvaz, Ahvaz, Iran

10.22051/jfm.2023.43538.2814

Abstract

The purpose of this research is to identify the effect of price acceleration in Tehran Stock Exchange. For this purpose, the data of 60 companies available in the stock exchange during a 10-year period (2012-2021) has been used. The regression model of the research has been tested using the time series data method. The hypotheses of this research have been examined in two boom and recession periods. In investigating the problem of risk, Fama and French's five-factor model was used to explain the effect of price acceleration, which shows that this model cannot explain the effect of price acceleration, and this result is still valid in the boom and recession periods, although it has a higher explanatory power in the boom period. Will have. In examining the second hypothesis, the five-factor model was expanded to measure the ability of the profit acceleration factor to explain the price acceleration effect by adding this factor to the model. The results of the hypothesis test show that the 6-factor model does not have sufficient ability to explain the effect of price acceleration, and this result has been confirmed in the investigation of an event, such as a downward trend of profit acceleration has been observed, in which the reaction is less than the limit to profit news. It is not visible. These results are still valid in two periods of boom and recession, although only in the j6k12 strategy in two periods of boom and recession, the second hypothesis was confirmed and it was observed that in this strategy, profit acceleration is a suitable behavioral factor to explain the effect of price acceleration, because by adding this factor According to the model, the model has an explanatory power of up to 59%.

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References

 
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History

  • Receive Date: 25 April 2023
  • Revise Date: 13 September 2023
  • Accept Date: 15 November 2023

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