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.
Review of Quantitative Finance and Accounting – Springer Journals
Published: Jul 14, 2016
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