شرکت های قیاس پذیر و دقت ارزش گذاری سهام

I investigate the relationship between the amount of information provided by a firm's comparables (i.e., firms in the same line of business as the firm being valued) and the precision of the firm's equity valuation. When investors have more information, previous studies argue that investors can make a more precise estimate of a firm's true equity value and this implies a lower (excess) stock return volatility around corporate events such as earnings announcements. I develop a simple model that shows a negative relationship between the amount of information provided by a firm's comparables and the firm's stock return volatility. Using alternative measures of information provided by comparables and different definitions of comparables, I consistently find a negative and significant relationship between these information measures and stock return volatility, ceteris paribus.

Why are some stocks more precisely valued than others? Some scholars posit that when investors have more information about a stock, they can make a more precise estimate of its true value. For example, Atiase (1985) argues that there is more information on large firms and implies that this should increase the precision of their stock valuation. In support of this argument, he finds that large firms have a lower stock price reaction to earnings announcements than small firms. More information means the pre-announcement price of the stock more accurately forecasts the information contained in the announcement, ceteris paribus. Therefore, the announcement is less surprising and the stock price reaction to the announcement is reduced. Other information proxies besides size have been considered in the literature. For example, Barry and Brown (1984) propose the firm's period-of-listing (POL) as a proxy for the amount of information on the stock. They focus on the relationship between information and stock returns and find that a longer POL is associated with lower returns after controlling for size, beta and interactive effects.1 Their results suggest an inverse relationship between POL and the magnitude of stock price reactions to corporate announcements, ceteris paribus. The number of analysts following a firm has also been posited as a proxy for the amount of information on a stock (i.e., differential information across securities). For instance, Brennan et al. (1993) find that stocks followed by many analysts react more quickly to common information than stocks followed by fewer analysts. I propose a new proxy for differential information that explains cross-sectional differences in the magnitude of security price reactions to corporate announcements beyond that previously suggested in the literature. This proxy follows from the regular use of comparables (i.e., firms in the same industry as the firm being valued) in stock valuation. For example, in a survey of investment firms, Carter and Van Auken (1990) report on the popularity of comparables' multiples in valuation. This technique can be as simple as multiplying the comparables' average price–earnings (PE) ratio times the firm's earnings to get an estimate of the firm's stock value. Besides the survey data cited above, everyday discussions of valuation in the popular press and valuation books attest to the popularity of using comparables' multiples in valuation.2 Moreover, Kaplan and Ruback (1995) find that, in their sample, the use of comparables' multiples is about as accurate as discounted cash flow (DCF) analysis. Kim and Ritter (1999) examine the usefulness of multiples for IPO valuation and report that PE multiples with forecasted earnings provide more accurate valuations than multiples using trailing earnings. Comparables are also useful in DCF analysis, however. For example, the betas of comparables can be used to estimate a firm's cost of capital (Fuller and Kerr, 1981). Therefore, comparables are useful with a variety of valuation techniques.3 The importance of comparables in valuation is also revealed in studies that document a contagion effect, where an announcement affecting the value of one firm in an industry is shown to affect the value of other (comparable/competitor) firms in the same industry. For example, Lang and Stulz (1992) find that bankruptcy announcements reduce the stock value of the firm announcing bankruptcy and the value of its competitors. Fenn and Cole (1994) report that the announcement of an insurance company writing down the value of its bond portfolio decreases its stock value and the value of its competitors. As a result of the support for contagion effects and the prominent role of comparables in valuation, I argue that comparables comprise a significant portion of the information investors use to value a stock. One simple measure of how much information comparables provide is the number of comparables. Ceteris paribus, more comparables suggests there is more information on the stock. Using the number of comparables as a differential information proxy is analogous to using the number of analysts and both measures may be needed to capture the various facets of differential information. The correlations from my sample, however, show that the number of analysts is more highly correlated with variables that represent more information on the stock (such as size and period-of-listing) than the number of comparables and tends to have a larger negative correlation with measures of the stock's intrinsic risk (such as the firm's debt ratio and the volatility of its return on assets). Therefore, the number of analysts may be at least partly reflecting the effect of other proxies for differential information or estimates of the stock's intrinsic risk. In fact, Bhushan (1989) argues that analysts may be attracted to high volatility stocks. To determine if more comparables imply more information, I examine the relationship between the number of comparables and the magnitude of stock price reactions to earnings announcements. I use the excess stock return volatility for the 21-day period surrounding the earnings announcement as the measure of the magnitude of stock price reactions to these announcements.4 As with Atiase's (1985) finding that large firms have a smaller reaction, there should be a negative relationship between the number of comparables and the stock price reaction to earnings announcements if more comparables imply more information. I also develop a simple model in Appendix A that shows, ceteris paribus, a negative relationship between the number of comparables and excess stock return volatility (resulting from the greater information more comparables provide). The negative relationship illustrated in the model is in a general one-period framework and is not restricted to earnings announcement tests. Therefore, I perform additional tests on the relationship between the number of comparables and volatility measured each month (i.e., the monthly volatility tests). To mitigate the possibility of finding a negative relationship between the number of comparables and volatility merely because firms with more comparables tend to have lower intrinsic risk, I include control variables for each sample firm's business risk, financial risk and its industry structure. Moreover, because previous studies (e.g., Karpoff, 1987) report a positive relationship between volatility and trading volume, I include trading volume as an additional control variable. I also include the information proxies discussed above (i.e., size, POL and number of analysts) to see if the number of comparables provides additional information beyond that contained in these established proxies for differential information.5 Besides these primary control variables, I include some other control variables to mitigate further the possibility that the number of comparables is correlated with some other factor(s) that reduces volatility. I include the firm's dividend yield as an additional control variable because firms with higher dividend yields may have less risk; of course, a high dividend yield does not cause the firm's risk to be lower but less risky firms may choose a high dividend yield and this lower risk may not be captured by the main control variables. In the earnings announcements tests, I also control for the analysts' earnings forecast error, the standard deviation of the analysts' forecasts, the firm's market-to-book ratio and the existence of previous earnings announcements in the same quarter by a firm's comparables. Of course, despite these efforts, I cannot rule out the possibility that there is some other factor I should control for but this is a common problem among empirical studies and I have more control variables than previous studies in this area.6 After accounting for the possible confounding effects discussed above, I consistently find a significantly negative relationship between the number of comparables and the volatility around earnings announcements. I also find a significantly negative relationship between the number of comparables and volatility measured each month, ceteris paribus. In contrast, the number of analysts is generally positively related to the volatility around earnings announcements and to the volatility measured each month. The other primary control variables are generally significant and have a sign consistent with financial theory or the findings of previous studies. I define comparable firms as those with the same primary 4-digit SIC code. To test the robustness of the results to an alternative industry definition, I use the Value Line (Investment Survey) industry definition. To gauge the robustness of the results to a different measure of the amount of information provided by comparable firms, I develop an alternative (to the number of comparables) measure that recognizes that some comparables are more similar to the firm being valued than others and may provide more information. In other words, it is possible that one comparable firm that is highly similar to the firm being valued can provide more information than several comparables that are relatively dissimilar to the firm being valued. To account for this possibility, I develop an index of the amount of information provided by comparables called the degree of comparability (DOC) in Appendix A. When the comparables are highly similar to the firm being valued the DOC is higher and this implies that more information is provided by the comparables. The model I present in Appendix A shows a negative relationship between volatility and DOC, ceteris paribus. I consistently find a highly significant negative relationship between the volatility around earnings announcements and the number of comparables based on the Value Line industry definitions, ceteris paribus; the DOC measure has a similarly negative relationship (using the SIC code or the Value Line industry definitions). I also find a significantly negative relationship between the volatility measured each month and DOC (again, using the SIC code or the Value Line industry definitions) and with the number of comparables based on the Value Line industry definitions. The results in this paper are important for four reasons. First, this paper's explanation for cross-sectional differences in volatility beyond that provided in the literature has important implications for why some stocks' options are more valuable than others. Second, because some studies argue that differential information affects expected returns (e.g., Barry and Brown, 1984; Merton, 1987), the results in this paper may help explain differences in expected returns across securities (even though my focus is on volatility). Third, the number of analysts has been a popular proxy for differential information and the results in this study suggest that, after controlling for other factors (including the number of comparables or DOC), this variable may not be a good proxy. Finally, though the usefulness of comparable firms in valuation has been recognized, their significance has not been fully investigated; the results in this paper highlight the importance of comparables in valuation. In Section 2, I discuss the data and methods. In Section 3, I analyze the empirical results and the summary and conclusions are presented in Section 4.

Previous studies suggest that greater information about a stock increases the market's precision in valuing the stock and this implies a lower stock price reaction to events such as earnings announcements, ceteris paribus. I argue that comparables comprise a significant portion of the information investors use to value a firm because of their widespread use in valuation and the well-documented existence of contagion effects. I use the number of comparables as a simple measure of differential information and posit that more comparables imply more information and lower stock price reactions to earnings announcements (defined as the excess stock return volatility around earnings announcements), ceteris paribus. Comparable firms are those in the same 4-digit SIC code as the firm being valued. The intuition is similar to that used with another popular proxy for differential information, the number of analysts following a firm. The number of analysts, however, has two notable problems as a proxy for differential information. First, the correlations from my sample show that the number of analysts may be reflecting the effect of other proxies for differential information (such as size and period-of-listing) and estimates of the stock's intrinsic risk. Second, the number of analysts can be affected by the volatility around earnings announcements; for example, Bhushan (1989) shows that analysts may be attracted to high volatility stocks. I address both problems by including other proxies for differential information and estimates of the stock's intrinsic risk (e.g., period-of-listing, size, trading volume, measures of business and financial risk and a measure of concentration in the firm's industry). I also use an iterated three-stage least squares method, where the number of analysts is affected by volatility and other factors posited by Bhushan. After accounting for the other variables affecting the stock price reaction to earnings announcements and the endogeneity of the number of analysts, I find that the number of analysts is consistently positively related to the volatility around earnings announcements. In contrast, the number of comparables is consistently negatively related to the volatility around earnings announcements. Because the number of comparables depends on how an industry is defined and the number of comparables is just one way to measure the amount of information provided by comparables, I test the results using the Value Line industry definitions to compute the number of comparables. I also construct a separate measure of the amount of information provided by comparables that I call the degree of comparability. The more similar the comparables are to the firm being valued, the greater the degree of comparability and the more information they provide. I also develop a simple model that shows a negative relationship between excess stock return volatility (i.e., volatility) and the degree of comparability; the model also shows a negative relationship between volatility and the number of comparables. Because the model is in a general one-period framework and is not restricted to earnings announcement tests, I perform additional tests on the relationship between the number of comparables (and the degree of comparability) and excess stock return volatility measured each month. Using alternative definitions of comparables (SIC code and Value Line) and different measures of the amount of information provided by comparables (number of comparables and degree of comparability), I consistently find a highly significant negative relationship between a firm's volatility (measured each month and around earnings announcements) and the amount of information provided by a firm's comparables, ceteris paribus. These results help answer a fundamental question in finance: Why are some stocks more volatile than others? Answering this question helps explain why some stock's options are more valuable than others and possibly why some stocks have higher expected rates of returns. Comparable firms appear to be an important part of the answer.