Sasan Babaie
Abstract
Compared with net earnings, the components of earnings are more informative in companies whose components have different qualities of persistence and volatility. We examine the issue of whether net earnings together with their components have more information content than only net earnings. We construct ...
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Compared with net earnings, the components of earnings are more informative in companies whose components have different qualities of persistence and volatility. We examine the issue of whether net earnings together with their components have more information content than only net earnings. We construct a model to describe the effect of components volatility and their persistence through disaggregation of earnings value relevance and predictability. The analyses in our study are based on 600 firm-year observations in Tehran Stock Exchange (TSE) for the period 2005- 2019. Data are derived from RAHAVARD NOVIN Iranian software and firms' financial statements. The statistical tests for data analyses are the difference of means test (t-test) and regression analyses. The results of the current study indicate that as the persistence and volatility of selected components of earnings (sales, employee expenses, other selling, general and administrative expenses, and income taxes) increase, earnings disaggregation can improve earnings predictability. Furthermore, when the volatility of employee expenses increases, disaggregated earnings can improve earnings value relevance. As the value relevance of net earnings has been declined over the past decades, the results of the current study suggest that earnings disaggregation plays a major role in improving earnings value relevance and their predictability.
Roya mirzaei; Amir Abbas Sahebgharani; Nazanin Hashemi
Abstract
Prediction of stock returns is always one of the most important discussions of financial markets, which has led to introducing of various models to pricing financial assets, one of the most important of these models is to measure the surplus returns by Fama & French model was introduced in ...
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Prediction of stock returns is always one of the most important discussions of financial markets, which has led to introducing of various models to pricing financial assets, one of the most important of these models is to measure the surplus returns by Fama & French model was introduced in the form of a 5-factor model which, in spite of its satisfaction with the model, is still in conflict with many anomalies in the market, which the model can not explain, in the same way The purpose of this paper is to examine the strength of Five Factor Model of Fama & French (2015) for explaining volatility as a market anomaly.The sample consists of 168 companies listed in Tehran Stock Exchange. Portfolio Analysis is the approach of this paper for testing explanatory power of the Five Factor Model. Results show that profitability and investment factors couldn’t explain excess returns. This conclusion contradicts the model of Fama and French (2016).