Equity Values and Prediction of Earnings with Disaggregation of Earnings in India

Abstract

This article examined the relative performance of aggregated and disaggregated earnings for valuation of equity and prediction of earnings in India. We measured three levels of earnings disaggregation: aggregate earnings, total accruals and cash flows, and four major constituents of accruals, then we estimated pooled as well as individual industry-wise regressions. We adopted Barth, Beaver, Hand and Landsman’s (1999, Review of Accounting Studies, 4(3, 4), 205–229; 2005, Journal of Accounting, Auditing & Finance, 20(4), 311–345) linear information structure grounded on generalized version of Ohlson (1999) model. We compared our results with the studies based on developed market. Our findings say that aggregated earnings and its disaggregated components are value relevant, and the adjusted R-squares of every next disaggregated systems are higher than aggregated systems, but in varying range across industries. We also find that the investors are not capable of judging total accruals and cash flows separately for investment decisions in this emerging market.

The Effect of Net Income and Other Comprehensive Income on Future’s Comprehensive Income With Attribution of Comprehensive Income as Moderating Variable

“…Previous research has shown that predictive power is better when aggregate is decomposed into its constituent items (Hodgson & Russell, 2014;Banks et al, 2018); while Kubota et al, (2011) concluded that OCI components are more relevant in valuation and prediction than OCI in the aggregate. The study conducted by Kumari & Mishra (2018), Hafij Ullah Mohammad (2013) also concluded, that the prediction of future earnings will increase when earnings are decomposed into accrual earnings and operating cash flows compared to aggregate earnings. Meanwhile, Willett (2016) research results show that when CI is decomposed into NI and OCI, it is proven to be more able to increase the relevance value of OCI.…”