The main purpose of this research is the study of association between various measures of firm performance based on earnings and cash flows and stock returns. This research is an applied research, and its design is semi-empirical, which is done by the method of post-event (past information). The statistical population of the research includes all companies listed in Tehran Stock Exchange (TSE), and its period is nine consecutive years, from 2003 to 2011. Simple and multiple regressions are applied in order to test the hypotheses. Results of the research represent that earning based measures are more related to stock returns than cash flow based measures. Furthermore, earning based measures depict the company performance better than cash flow measures in some companies with higher accruals. But in companies with lower accruals, the company performance cannot be depicted properly neither by earning based nor cash flow based measures.
Purpose: The core purpose of this paper empirically study of the initial public offerings (IPOs) of companies accepted in oil and chemical industries. The paper attempts to answer the question of is there any abnormal return from IPOs in listed companies in Tehran Stock Exchange (TSE).Design/methodology/approach: This research is an applied research, and its design is empirical, which is done by the method of post-event (past information). For the purpose of the study the t-statistic, regression and variance analyses are applied to examine the hypotheses. We use in the analyses a sample of 29 newly accepted Iranian oil and chemical companies listed on TSE for the period of 2001 to 2012. This paper has studied abnormal return and three abnormal phenomena have been considered in capital market. These phenomena consist: (1) underpricing or overpricing of the firm's stock, (2) lower or higher stock return of the firms and (3) Particular period in market for stock transactions volume.Findings: The results support the hypothesis that there is a positive abnormal return to investing in the newly accepted oil and chemical firms for stockholders. It also shown the firm size is the only factor that can affect the stock abnormal return. With considering significance level, investors have to give attention sequentially to other variables such as stock ownership centralization, going public time and stock offering volume.
Purpose: the main purpose of this research is to examine the effective factors in abnormal error of earnings forecast. Design/methodology/approach: this research is an applied research, and its design is semi-empirical, which is done by the method of post-event (past information). The statistical population of the research includes all companies listed in Tehran Stock Exchange (TSE), and its period is four years, from 2010 to 2013. To examine the hypotheses, correlation analysis, and variance analysis test are used. Findings: the results of this research indicate there is a meaningful relationship between the type of industry, and the effective factors in abnormal error of earnings forecast. The mining, chemical materials, petrochemical and pharmaceutical industries have high abnormal error of earnings forecast, while metal industries, agriculture, and animal husbandry have low abnormal error of earnings forecast. Also we find in this research that there is a meaningful relation between the firm size, and the firm age in stock exchange. Finally, simultaneous effect of the three factors on abnormal error of earnings forecast was examined.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.