Is the accuracy of stock value forecasting relevant to industry factors or firm-specific factors? An empirical study of the Ohlson model

Is the accuracy of stock value forecasting relevant to industry factors or firm-specific factors?... This study develops a methodology to incorporate industry and firm-specific factors into the Ohlson (Contemp Account Res 11:661–687, 1995). Residual income valuation model (RIM) and applies a time- series approach instead of the cross-sectional regression models used by existing studies. This method provides neglected valuation information when analysts’ earnings forecasts are used. The results suggest that it improves the accuracy of stock value forecasting. The inclusion of the two factors increases the forecasting ability of RIM. Furthermore, the relative importance of the two factors varies across industries. Firm-specific factors are relevant to the accuracy of stock value forecasting for three large industries (finance and insurance, electronics, building, construction and materials), whereas industry factors play a dominant role in determining the accuracy for small industries (automobile and paper). These results imply that either industry or firm-specific factors serve as crucial determinants in stock value forecasting for the five industries. In practice, investors can consider one or both of the two factors when implementing RIM to forecast stock value. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Review of Quantitative Finance and Accounting Springer Journals

Is the accuracy of stock value forecasting relevant to industry factors or firm-specific factors? An empirical study of the Ohlson model

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Publisher
Springer US
Copyright
Copyright © 2016 by Springer Science+Business Media New York
Subject
Finance; Corporate Finance; Accounting/Auditing; Econometrics; Operation Research/Decision Theory
ISSN
0924-865X
eISSN
1573-7179
D.O.I.
10.1007/s11156-016-0587-8
Publisher site
See Article on Publisher Site

Abstract

This study develops a methodology to incorporate industry and firm-specific factors into the Ohlson (Contemp Account Res 11:661–687, 1995). Residual income valuation model (RIM) and applies a time- series approach instead of the cross-sectional regression models used by existing studies. This method provides neglected valuation information when analysts’ earnings forecasts are used. The results suggest that it improves the accuracy of stock value forecasting. The inclusion of the two factors increases the forecasting ability of RIM. Furthermore, the relative importance of the two factors varies across industries. Firm-specific factors are relevant to the accuracy of stock value forecasting for three large industries (finance and insurance, electronics, building, construction and materials), whereas industry factors play a dominant role in determining the accuracy for small industries (automobile and paper). These results imply that either industry or firm-specific factors serve as crucial determinants in stock value forecasting for the five industries. In practice, investors can consider one or both of the two factors when implementing RIM to forecast stock value.

Journal

Review of Quantitative Finance and AccountingSpringer Journals

Published: Jul 14, 2016

References

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