股本回报率
经济增加值
豪斯曼试验
经济
资产收益率
每股收益
证券交易所
会计
多重共线性
计量经济学
业务
面板数据
利润(经济学)
回归分析
统计
财务
数学
固定效应模型
微观经济学
作者
Jasvir S. Sura,Rajender Panchal,Anju Lather
出处
期刊:IIM Ranchi journal of management studies
[Emerald (MCB UP)]
日期:2022-09-29
卷期号:2 (1): 82-96
被引量:1
标识
DOI:10.1108/irjms-03-2022-0037
摘要
Purpose The main aim of this paper is to examine the claim that economic value added (EVA) advocates its superiority over the traditional accounting-based financial performance measures, i.e. profit after tax (PAT), earnings per share (EPS), return on assets (ROA), return on equity (ROE) and return on investment (ROI) in the Indian manufacturing sector and at the same time, give empirical facts. It also tests and examines the information content of various performance measures and their relationship with stock returns. Design/methodology/approach The paper uses the sample of 534 Indian manufacturing companies from the Bombay Stock Exchange (BSE) during the period 2000–2018. Multiple regression models are applied to examine the information content of EVA and traditional performance measures in explaining shareholders’ returns. Findings Relative information content tests revealed that traditional accounting-based measures such as EPS, ROE and ROA performed better than EVA in explaining the returns of Indian manufacturing companies. Incremental information content of EVA adds little contribution to information content above traditional performance measures. The claim of superiority of EVA over accounting-based measures in association with shareholder returns is proved invalid in Indian manufacturing companies. Originality/value This study concludes that EVA has no superiority over traditional accounting-based financial performance measures in explaining stock returns of Indian manufacturing companies. To achieve heftiness in outcomes, panel data are tested by using Breusch–Pagan–Godfrey (BPG) test for heteroskedasticity, Hausman’s test for fixed and random effect, variance inflation factor (VIF) test for multicollinearity and Durbin–Watson test for autocorrelation.
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