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IPO performance and earnings expectations: some French evidence

IPO performance and earnings expectations: some French evidence

analysts, those analysts

analysts, those analysts issue less biased forecasts. If we assume that analysts purposely bias their forecasts, this can mean that competition gives them an incentive to be more accurate. Another possible explanation is that it is harder to “fool” investors on big, well-known companies (that are generally the ones with more analyst following), and consequently that the high reputation costs incurred to manipulate their perspectives favorably cannot be recouped via increased brokerage activities. 6.3.2.2 Are analysts’ forecasts around IPOs more biased than forecasts for seasoned firms ? Table 4 - Panel C results suggest that, as we previously observed, analyst forecast errors for seasoned companies have a significantly negative mean, are less negative for the one-year horizon, and are more negative if we take “by-firm” results than for “all observations” results. These results are consistent with previous literature (see for instance Brown (1997) a) and with our findings in panels A and B. When we compare panels B and C in Table 4, we note that AFE are not smaller in absolute value 13 for non-IPO companies than for IPO firms, whichever method or period we consider.This suggests that when analysts issue reports about recent IPOs, their incentives to be accurate are as strong as their incentives to be optimistic. But this might also be due to the fact that the informational content of AFE is different for the two samples. It is worth noting that Rajan and Servaes (1997) find that analysts are more optimistic about IPO firms than for other companies. This difference between their results and ours may come from the fact that we did restrict our study to the very first forecast produced by each analyst, while Rajan and Servaes consider all the forecasts issued within one year of the IPO. This might also be explained by a change in analysts’ behavior around IPOs (Rajan and Servaes focus on the 1975-1987 period), or by differences between the French and American markets. 6.3.2.3 What are the determinants of Analyst Forecast Errors? 20

In the previous chapter, we saw that IPO AFE are not larger than seasoned companies AFE. Let us try to see if the determinants of Analyst Forecast errors are different for IPO vs. non-IPO companies. To investigate this, we run two different regressions. In the first regression, the dependent variable is the Analyst Forecast Error by firm / forecast year. The independent variables are Ipo, a dummy variable equal to 1 if the firm is in the IPO sample, 0 otherwise, Value, a variable that is equal to the average forecast in French Francs for the firm / forecast year considered, Log(market capitalization) that is a proxy for size, and Number of months, a variable equal to the average, for each firm / forecast year pair, of the number of months elapsed between forecast date and fiscal year end. The IPO variable is used to determine if there is an IPO effect in AFEs. We use the Value variable to see if AFEs are larger the larger the forecast, as Dechow, Hutton and Sloan (2000) suggest. Log(market capitalization) is our proxy for the size effect already mentioned. We use Number of months to control if AFEs are larger the larger the horizon of the forecasts. The results are presented in Table 5 - Panel A. Our size variable has a positive impact on AFE, which means that bigger firms have smaller average AFE in absolute value. Future earnings may be easier to predict for large companies, for which more information is available. On the contrary, the coefficient of the Number of months variable is negative: the further is the prediction from the realized earnings, the larger is the average forecast error. Those two coefficients are significantly different from 0 at a 1% level. On the contrary, we find no significant effect of the Value variable, which suggests that AFE are independent from the magnitude of the forecast. Finally, the coefficient for the Ipo variable is positive, but not significantly so. Even when we control for other variables, Analyst Forecast Errors are not larger for IPOs than for non-IPOs. 13 When we use the term “larger” forecast error, we always consider absolute values. Given the way forecasts errors are calculated, “larger” errors are actually (algebraically speaking) smaller. 21

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