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IPO performance and earnings expectations: some French evidence

IPO performance and earnings expectations: some French evidence

We know of no previous

We know of no previous evidence on the sign and magnitude of this type of forecast errors. However, the work of Michaely and Womack (1999) suggests that the analyst who works for the lead underwriter and who is in charge of forecasting earnings for the firm will have a strong incentive to boost his predictions in order to give a positive image of the firm to potential investors 12 . As far as the forecast horizon is concerned, we expect long-term (2 or 3 years) predictions to be less important than the short-term (1 year) ones in terms of accuracy constraint. Indeed, when earnings announcements are issued for the third year following an IPO, it is very likely that investors have forgotten the 3-year predictions issued three years before in the pre-IPO prospectus. Thus we expect that the analyst’s (and the underwriter’s) reputation will be less affected by a forecast error at a 3-year horizon than for the first year end following the IPO. We compute Prospectus Forecast Errors (PFE) as the difference between actual and forecasted earnings, divided by the share price at IPO date, in line with the method used in previous literature: PFE = (actual EPS – prospectus EPS forecast) / stock price at IPO date The results we obtain are presented in Table 4 - Panel A. They support our hypotheses. First, the overall mean (-2.08%) is significantly negative at a 1% level, indicating prospectus optimism about future earnings. Second, when we split the results by forecast period, we notice that forecasts for periods 2 and 3 are significantly more biased than for period 1 (-2.54% and - 6.30% versus -0.36%, respectively). This suggests that analysts have a stronger incentive to provide accurate predictions for shorter horizon forecasts. 6.3.2 Analyst Forecast Errors (AFE) 12 Note that, given the fact that those forecasts are also used in cash-flow models to estimate the value of the firm, the analyst also has an obligation, for legal or at least credibility reasons, to prove that the firm value corresponding to the IPO price is equal to or slightly smaller than his estimated value of the firm. 18

Rajan and Servaes (1997) show that analysts are over-optimistic around IPOs when they predict future Earnings per Share or long-term growth. They also find that over-optimism is stronger for IPOs than for other firms. We replicate and extend some of their tests. The measure of Analyst Forecast Errors (AFE) is similar to the one used previously for prospectus errors for comparison purposes: AFE = (actual EPS – analyst’s EPS forecast) / stock price at prediction date The questions we address are the following: (1) Are analyst forecasts around IPOs biased? (2) Are they more biased than forecasts for non-IPO firms? (3) What are the determinants of these errors? (4) Do these errors depend on the affiliation of the analyst who issued the prediction? 6.3.2.1 Are analyst forecasts around IPOs biased? The results in Table 4 - Panel B suggest that the answer to that question is yes. Whichever method we use (“all observations” or “by-firm”), we find a significantly negative all-period average analyst forecast error. We also note that the distribution is approximately centered in zero and left-skewed: the number of bad surprises is approximately the same as the number of good surprises. When we consider the different horizons separately, we notice as we expected that the longer the horizon, the larger the error. As we suggested previously, the temptation for analysts to give a positive image of an IPO firm around the IPO is mitigated by the threat to lower their reputation. This is particularly true for relatively short horizons, which are presumably more important in investors’ formation of beliefs. We also note that the bias in analyst forecasts is always larger in the “by-firm” table than in the “all observations” table. This means that when firms are followed by a larger number of 19

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