A New Methodology for Identifying the Sources of Cost Stickiness and Investigating Their Effects on Earnings Forecast Accuracy

Autor: Ali Shirzad, Mohammad Javad Saei, Farzaneh Nassir Zadeh
Rok vydání: 2020
Předmět:
Zdroj: SSRN Electronic Journal.
ISSN: 1556-5068
DOI: 10.2139/ssrn.3701125
Popis: The main aim of this study is to present a new methodology for separating the sources of cost stickiness. In previous research, various factors have been shown to affect cost stickiness. These factors are rooted in the industry and firm-specific characteristics or specific events, which may occur each year at national or international scales. Overall, they could be classified into three groups: 1. Year-specific events and features, 2. Industry-specific and 3. Firm-specific characteristics. In this study, in the first step, a new methodology is presented to separate the sources of cost stickiness, including a novel method for calculating cost stickiness for each firm-year. In the second step, we investigated the effect of each firm-year stickiness and each source of stickiness on the earnings forecast accuracy (EFA). To investigate the validity, the results compared with Anderson et al. (2007). The statistical population of the study consisted of all companies listed on the Tehran Stock Exchange, from which 1080 observations in 2014-2018 period were selected and reviewed. Our results indicated that EFA has a negative and significant relationship with total stickiness, stickiness of each year and each company, but no significant relationship was found with stickiness of each industry. In addition, the results of using the proposed method are consistent with Anderson et al.’s (2007) model and even more significant to that. The findings suggest that the events of each year and the intra-organizational events of each company have a greater impact on cost behavior. Hence, it is necessary for managers and financial analysts to take into account each source of cost stickiness, especially year-specific events and firm-specific characteristics, and consider their effects in earnings forecast to improve their EFA.
Databáze: OpenAIRE