چکیده:
This paper has provided "out of sample" evidence of stock returns predictability in Tehran Stock Exchange. 68 qualified companies over the period from 2002 to 2015 were selected and for five different "forms of returns", five superior predictive models have been designed by applying "General to specific" approach of modeling technique. Then "out of sample" analysis, based on rolling regressions, has been used to test the validation of the designed models. The result showed that all designed models have sufficient "out of sample" validity and the aggregate returns have a higher predictability level.
خلاصه ماشینی:
Investigating Predictability of Different "Forms of Return" in Tehran Stock Exchange: Some Rolling Regressions-based Evidence 3 Azam Mohtadi1&2, Rezvan Hejazi1, Sayed Ali Hosseini1, Mansoor Momeny 1.
com Introduction Researchers for various reasons, such as finance-behavioral theories or absence of full market efficiency, have considered stock returns to be predictable, and for many years (for example, Dow, 1920) have attempted to identify the nature and elements of its formation.
We design, compare, test, and rank models with the "out of sample" analysis based on rolling regressions technique to prepare a comprehensive analysis of stock returns predictability in Iranian texture.
Comprehensiveness of the present research about the analysis of several forms of returns and the use of rolling regressions in models validation are considered as our contribution to the literature.
To answer the second question, we apply "out of sample" approach and test the hypothesis below for each kind of returns : H0: Based on the pattern of rolling regressions in the "out of sample" validation technique, for "each form of the returns", the errors of the "historical mean model" are less than or equal to the errors of designed model.
Then, the most suitable predictive model was designed using the mentioned approach, for each form of returns and finally, extracted models were validated using an "out of sample" approach based on rolling regressions and ranked based on the forecast accuracy and quality.
The result of this research showed that the "principal component analysis" technique could be used in data reduction of effective variables on stock returns in Iran's capital market.