Article Per Year (5 Year)
p-Index From 2020 - 2025
0
P-Index
This Author published in this journals
All Journal Journal of Accounting and Investment
Silvia Thiono, Silvia
Perguruan Tinggi Tridarma Bandar Lampung
Economics, Econometrics & Finance

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
PERBANDINGAN KEAKURATAN LABA PERMANEN, LABA AGREGAT, DAN ARUS KAS OPERASI UNTUK MEMPREDIKSI ARUS KAS OPERASI MASA DEPAN Evana, Einde; Thiono, Silvia
Journal of Accounting and Investment Vol 10, No 1: January 2009
Publisher : Universitas Muhammadiyah Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (431.619 KB)

Abstract

The goal of this research is to compare the accuracy of three prediction models which are permanent earnings, aggregate earnings and operational cash flow in prediction future operational cash flow. The hypotheses which are proposed in this research are H1: permanent earnings are more accurate than aggregate earnings in prediction future operational cash flow, and H2: operational cash flow is more accurate than aggregate earnings in prediction future operational cash flow. The researcher uses test instruments panel data regression to make efficient regression panel data model. The samples in this research are 29 companies from 148 manufactory companies which are registrant in Indonesian Stock Exchange. The dependent variable is operational cash flow, whereas independent variables are permanent earnings, aggregate earnings and operational cash flow. The accuracy comparison of prediction model is done by comparing the value of prediction error by using absolute percentage error (APE). The result of test by using Wilcoxon Signed Rank Test with real  rate 5% prove that permanent earnings is not different from the accuracy with aggregate earnings in prediction future operational cash flow. Empirical evidence also shows that operational cash flow is more accurate than aggregate earnings in prediction future operational cash flow.
Co-Authors Einde Evana, Einde