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2015 | OriginalPaper | Buchkapitel

87. Alternative Equity Valuation Models

verfasst von : Hong-Yi Chen, Cheng-Few Lee, Wei K. Shih

Erschienen in: Handbook of Financial Econometrics and Statistics

Verlag: Springer New York

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Abstract

This chapter examines alternative equity valuation models and their ability to forecast future stock prices. Equity valuation models included Ohlson’s (1995) Model, Feltham and Ohlson’s (1995) Model, and Warren and Shelton’s (1971) Model. Five research hypotheses are developed to examine whether different estimation techniques, earnings measures, and combined forecasting methods can improve the ability to predict future stock prices. We find that the simultaneous equation estimation procedure can produce more accurate future stock price forecasts than the traditional single equation estimation method in terms of smaller prediction errors. In addition, the combined forecast method can further reduce the prediction errors by using combination of individual forecasts. Empirical evidence also shows that investors can use comprehensive earnings to more accurately forecast future stock prices in these valuation models.
We use simultaneous equation estimation technique to investigate the stock price forecast ability of Ohlson’s Model, Feltham and Ohlson’s Model, and Warren and Shelton’s (1971) Model. Moreover, we use the combined forecasting methods proposed by Granger and Newbold (1973) and Granger and Ramanathan (1984) to form combined stock price forecasts from individual models. Finally, we examine whether comprehensive earnings can provide incremental price-relevant information beyond net income.

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Fußnoten
1
Previous literatures in advocating comprehensive income reporting include Robinson (1991), Johnson et al. (1995), Beresford et al. (1996), and Smith and Reither (1996).
 
2
Previous literatures in advocating current operating performance concepts of reporting income include Kiger and Williams (1977), Black (1993), Brief and Peasnell (1996), and Holthausen and Watts (2001).
 
3
International evidence of the debate of comprehensive income reporting in countries such as UK and New Zealand can be found in O’Hanlon and Pope (1999), Cahan et al. (2000), Brimble and Hodgson (2007), and Lin (2006).
 
4
In the post-SFAS 130 periods, Compustat has not yet completely disclosed all components in comprehensive income. Currently, Compustat only reports some of the items related to comprehensive income and these data are only complete after year 2001. Given that our empirical tests require sufficient time series to conduct forecasting, we employ the measurement methodology in Cheng et al. (1993) and Dhaliwal et al. (1999) to estimate comprehensive income.
 
5
Borrowing from Nissim and Penman (2001), the marginal tax rate is the top statutory federal tax rate plus 2 % average state tax rate. For our sample periods, the top statutory federal tax rate was 46 % in 1979–1986, 40 % in 1987, 34 % in 1988–1992, 35 % in 1993–1999, 40 % in 2000–2002, and 35 % in 2003–2008.
 
6
The long-term growth forecast generally represents an expected increase in operating earnings over the company’s next full business cycle. Usually, these forecasts refer to a period of between 3 and 5 years. Thomson Financial recommends the median value for long-term growth forecast rather than the mean. The median value is less affected by outlier forecasts.
 
7
Since the linear information dynamics contains lagged dependent variables, the OLS estimation is inconsistent. We proceed our estimation by the IV estimation and panel GMM proposed by Anderson and Hsiao (1981) and Arellano and Bond (1991), respectively. The panel GMM is more efficient than the IV estimator because of additional lags of dependent variable as instruments. The results from the two estimation methods are similar and we reported the results from panel GMM estimator.
 
8
The single equation estimation is conducted by the panel GMM estimator as in Table 87.2A and B. Since our system of simultaneous equation specification of information dynamics involves endogenous regressors from other equations, we use the more efficient error-component three-stage least square (3SLS) estimator proposed by Baltagi (1981) to conduct the estimation. Essentially, the 3SLS is a combination of the two-stage least square (2SLS) estimator and the seemingly unrelated regression (SUR) estimator. 3SLS considers both the simultaneous equation bias and the cross equation correlation of the errors.
 
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Metadaten
Titel
Alternative Equity Valuation Models
verfasst von
Hong-Yi Chen
Cheng-Few Lee
Wei K. Shih
Copyright-Jahr
2015
Verlag
Springer New York
DOI
https://doi.org/10.1007/978-1-4614-7750-1_87