firms in the bubble period, suggesting media generally not only provided more coverage but also had a more optimistic view, whether rational or not, about internet firms in the bubble period However, Figures 1-c, 1-e, 2-c and 2-e reveal that post peak, there was a dramatic shift in media sentiment. Internet IPO firms have more negative net news than their matching sample after the bubble burst. This indicates that media reported relatively more bad news than good news for the internet firms than they did for the control firms in the post-bubble period, suggesting media had a more pessimistic view whether rational or not, about internet firms in the post-bubble period Finally, note the following asymmetry: the relative pessimism on internet firms over non-internet firms in the post-bubble period was higher than the relative optimism on internet firms over non-internet firms in the bubble period Table 2 documents results from formal statistical tests that corroborate our observations from the above informal ocular tests. Panel A shows prior to market peak defined as March 24, 2000, an internet firm receives on average 1.04 pieces of news reports per day and 21.04 per month. Among these news items, 0.39 per day and 7.98 per month are good news- more than twice the amount of good news a non- internet firm receives. The difference is statistically significant. The net news in the bubble period averages 0.10 per day and 2. 17 per month for an internet firm, significantly higher than the 0.07 per day and 1.42 per month coverage for a non-internet firm After the market peak, with an average of 0.65 news items per day and 14.01 news items per month an internet firm still receives over twice the media attention than a non-internet firm. However the media coverage is more negative about internet firms during this sub-period. The net news is-0. 12 per day and 2.28 per month for an internet firm, compared to-0.01 per day and-0 19 per month for a non-internet firm Panel B of Table 2 shows the same pattern of media coverage under a different measure of peak the day a firms price peaked in the period 1996 to 2000. So Table 2 leads us to conclude that during the bubble period the media coverage was more positive about internet IPOs than it was about non-internet IPOs, but during the post-bubble period, the media coverage was more negative about internet IPOs than it was about non-internet IPos 14
14 firms in the bubble period, suggesting media generally not only provided more coverage but also had a more optimistic view, whether rational or not, about internet firms in the bubble period. However, Figures 1-c, 1-e, 2-c and 2-e reveal that post peak, there was a dramatic shift in media sentiment. Internet IPO firms have more negative net news than their matching sample after the bubble burst. This indicates that media reported relatively more bad news than good news for the internet firms than they did for the control firms in the post-bubble period, suggesting media had a more pessimistic view, whether rational or not, about internet firms in the post-bubble period. Finally, note the following asymmetry: the relative pessimism on internet firms over non-internet firms in the post-bubble period was higher than the relative optimism on internet firms over non-internet firms in the bubble period. Table 2 documents results from formal statistical tests that corroborate our observations from the above informal ocular tests. Panel A shows prior to market peak defined as March 24, 2000, an internet firm receives on average 1.04 pieces of news reports per day and 21.04 per month. Among these news items, 0.39 per day and 7.98 per month are good news – more than twice the amount of good news a noninternet firm receives. The difference is statistically significant. The net news in the bubble period averages 0.10 per day and 2.17 per month for an internet firm, significantly higher than the 0.07 per day and 1.42 per month coverage for a non-internet firm. After the market peak, with an average of 0.65 news items per day and 14.01 news items per month, an internet firm still receives over twice the media attention than a non-internet firm. However, the media coverage is more negative about internet firms during this sub-period. The net news is -0.12 per day and - 2.28 per month for an internet firm, compared to -0.01 per day and -0.19 per month for a non-internet firm. Panel B of Table 2 shows the same pattern of media coverage under a different measure of peak: the day a firm’s price peaked in the period 1996 to 2000. So Table 2 leads us to conclude that during the bubble period the media coverage was more positive about internet IPOs than it was about non-internet IPOs, but during the post-bubble period, the media coverage was more negative about internet IPOs than it was about non-internet IPOs
Since large offers tend to attract more public attention, we further break down our sample into two sub-samples, large and small IPOs, based on the median gross proceeds of the combined two samples. In two alternative and separate classifications, we break down our sample into tech and non-tech IPOs, as well as VC-backed and non VC-backed IPOs. Our previous observations of the difference in media coverage between the two samples hold, regardless of the size of the offer, the technological nature of the firm, and whether or not the issue is backed by venture capitalists Finally, we confirm the asymmetry we had noticed before: the relative pessimism on internet firms over non-internet firms in the post-bubble period(net news was-0. 12 per day for internet firms and-001 per day for non-internet firms) was higher than the relative optimism on internet firms over non-internet firms in the bubble period(net news was 0.10 per day for internet firms and 0.07 per day for non-internet firms) B Conditional Media Coverage We now explore conditional news coverage between the two groups of firms during our sample period. Specifically, do the media report more good news than bad news when the previous period experiences a price increase, and do the media report more bad news than good news when the previous period was a price decrease? If yes, this would be consistent with the positive feedback hypothesis discussed in Shiller(2000) Graphic illustrations of this test are given in Figures 3-a to 3-h in both calendar time and event time, and in aggregate and per-firm basis. We notice that the optimism of the media, captured by the net news items per month, moves along with market capitalization for both internet and non-internet firms in calendar time(Figure 3-b)and in event time(Figure 3-f). This effect is about the same for internet firms and non-internet firms in the pre-peak period, but it is much stronger for internet firms in the post-peak period. The post-peak difference between the two samples suggests the media are more pessimistic about falling prices for internet firms than they are about falling prices for non-internet firms in this period Not only is media sentiment positively linked with price levels, but also is its interest. Notice that media coverage, as captured by the total news per month, moves along with market capitalization for both
15 Since large offers tend to attract more public attention, we further break down our sample into two sub-samples, large and small IPOs, based on the median gross proceeds of the combined two samples. In two alternative and separate classifications, we break down our sample into tech and non-tech IPOs, as well as VC-backed and non VC-backed IPOs. Our previous observations of the difference in media coverage between the two samples hold, regardless of the size of the offer, the technological nature of the firm, and whether or not the issue is backed by venture capitalists. Finally, we confirm the asymmetry we had noticed before: the relative pessimism on internet firms over non-internet firms in the post-bubble period (net news was -0.12 per day for internet firms and -0.01 per day for non-internet firms) was higher than the relative optimism on internet firms over non-internet firms in the bubble period (net news was 0.10 per day for internet firms and 0.07 per day for non-internet firms.) B. Conditional Media Coverage We now explore conditional news coverage between the two groups of firms during our sample period. Specifically, do the media report more good news than bad news when the previous period experiences a price increase, and do the media report more bad news than good news when the previous period was a price decrease? If yes, this would be consistent with the positive feedback hypothesis discussed in Shiller (2000). Graphic illustrations of this test are given in Figures 3-a to 3-h in both calendar time and event time, and in aggregate and per-firm basis. We notice that the optimism of the media, captured by the net news items per month, moves along with market capitalization for both internet and non-internet firms in calendar time (Figure 3-b) and in event time (Figure 3-f). This effect is about the same for internet firms and non-internet firms in the pre-peak period, but it is much stronger for internet firms in the post-peak period. The post-peak difference between the two samples suggests the media are more pessimistic about falling prices for internet firms than they are about falling prices for non-internet firms in this period. Not only is media sentiment positively linked with price levels, but also is its interest. Notice that media coverage, as captured by the total news per month, moves along with market capitalization for both
internet and non-internet firms in calendar time(Figure 3-a)and in event time(Figure 3-e). The effect is stronger for internet firms both in the pre-peak period as well as in the post-peak period Finally, observe that the net news per firm spiked before March 24, 2000(Figure 3-d), or before the firm reached its maximum value( Figure 3-h), suggesting that media sentiment turned before the market peaked. We explore this tantalizing result formally in the next section, where we ask whether media sentiment Granger-causes returns The findings from Figures 3-a to 3-h are corroborated formally in Table 3. We report the results based on two arbitrarily selected cutoff points about the degree of price movement: price increases or decreases more than 0% and 1% from previous day for daily study, and 0% and 10% from previous month for monthly analysis. Alternative cutoff points do not change our results qualitatively. Using abnormal returns instead of raw returns yields virtually identical results and hence these results are not reported Panel A of Table 3 reveals that if prices increased in the previous period, net news was positive this period for both internet stocks(0.07 per day)and for non-internet stocks(0.05 per day ). If prices decreased in the previous period, net news was negative this period for internet stocks (-0.06 per day ), but still positive this period for non-internet stocks(0.01 per day ). Examining alternative cutoff points for previous price movements yields the same pattern. This means that Shiller's(2000)positive feedback hypothesis works especially for internet shares The analysis of the bubble and the post-bubble stages in Panels B and C of Table 3 leads to more interesting results. It seems that only one leg of the positive feedback hypothesis worked in the bubble stage, and another leg of the positive feedback hypothesis worked in the post-bubble stage. In the bubble stage, if prices increased in the previous period, net news was positive this period, but if prices decreased in the previous period, net news was not negative this period. In the post-bubble stage, if prices decreased in the previous period, net news was negative this period, but if prices increased in the previous period, net news was not positive this period Interestingly, during the bubble period, this asymmetry was both economically and statistically stronger for internet stocks when we examine net news per month. In the post-bubble period, this 16
16 internet and non-internet firms in calendar time (Figure 3-a) and in event time (Figure 3-e). The effect is stronger for internet firms both in the pre-peak period as well as in the post-peak period. Finally, observe that the net news per firm spiked before March 24, 2000 (Figure 3-d), or before the firm reached its maximum value (Figure 3-h), suggesting that media sentiment turned before the market peaked. We explore this tantalizing result formally in the next section, where we ask whether media sentiment Granger-causes returns. The findings from Figures 3-a to 3-h are corroborated formally in Table 3. We report the results based on two arbitrarily selected cutoff points about the degree of price movement: price increases or decreases more than 0% and 1% from previous day for daily study, and 0% and 10% from previous month for monthly analysis. Alternative cutoff points do not change our results qualitatively. Using abnormal returns instead of raw returns yields virtually identical results and hence these results are not reported. Panel A of Table 3 reveals that if prices increased in the previous period, net news was positive this period for both internet stocks (0.07 per day) and for non-internet stocks (0.05 per day). If prices decreased in the previous period, net news was negative this period for internet stocks (-0.06 per day), but still positive this period for non-internet stocks (0.01 per day). Examining alternative cutoff points for previous price movements yields the same pattern. This means that Shiller’s (2000) positive feedback hypothesis works especially for internet shares. The analysis of the bubble and the post-bubble stages in Panels B and C of Table 3 leads to more interesting results. It seems that only one leg of the positive feedback hypothesis worked in the bubble stage, and another leg of the positive feedback hypothesis worked in the post-bubble stage. In the bubble stage, if prices increased in the previous period, net news was positive this period, but if prices decreased in the previous period, net news was not negative this period. In the post-bubble stage, if prices decreased in the previous period, net news was negative this period, but if prices increased in the previous period, net news was not positive this period. Interestingly, during the bubble period, this asymmetry was both economically and statistically stronger for internet stocks when we examine net news per month. In the post-bubble period, this
asymmetry is stronger for internet stocks whether we use net news per day or net news per month. This suggests that in the bubble stage, the media coverage is non-negative in the event of price falls, especially for internet stocks; and in the post-bubble stage, the media coverage is non-positive in the event of price rises, especially for internet stocks. These results remain whether the peak is defined in calendar time or in event time V. THE MARKET REACTION TO MEDIA COVERAGE In the previous section we documented the differences in aggregate media coverage between internet firms and non-internet firms. This is a necessary, but not sufficient, condition to conclude that the media had a role in the meteoric rise and fall of internet stocks in the late 1990s. In this section. we explicitly examine the impact of this differential media coverage on stock prices. We first conduct the analysis at the firm level, and then conduct the analysis at the portfolio level A. Firm-Level Analysis The dependent variable is the abnormal return of a firms stock estimated by fitting a Fama-French (1993)3-factor model for each firm. We use contemporaneous Fama-French factors to control for the most recent market-wide information to ensure the conservativeness of our news analysis, though our results are almost identical with respect to both economic and statistical significance if lagged factors are selected We then examine the impact of different types of news on daily and monthly abnormal returns, respectively, by including number of net news(NN), good news(GM) and bad news(BN) per firm from the previous period(either day or month) as independent variables in our regression model. Net news simply number of good news minus number of bad news per firm from the previous period(either day or mo Busse and Green(2002)use a sample of 322 news reports in the Morning Call and Midday Call segments on CNBC between June 12 and October 27, 2000, and find that prices respond within seconds. In contrast, we focus on aggregate media coverage and its effect on stocks over the entire bubble and post-bubble period. The nature of the print media, the availability of news publications, and the use of market closing prices in calculating returns generate only a daily frequency dataset. So we cannot say whether prices respond within seconds. However, will e effect lasts at least for a day. Antweiler and Frank(2004a)and Huberman and Regev(2001)also find a prolonged effect of news on returns 17
17 asymmetry is stronger for internet stocks whether we use net news per day or net news per month. This suggests that in the bubble stage, the media coverage is non-negative in the event of price falls, especially for internet stocks; and in the post-bubble stage, the media coverage is non-positive in the event of price rises, especially for internet stocks. These results remain whether the peak is defined in calendar time or in event time. V. THE MARKET REACTION TO MEDIA COVERAGE In the previous section we documented the differences in aggregate media coverage between internet firms and non-internet firms. This is a necessary, but not sufficient, condition to conclude that the media had a role in the meteoric rise and fall of internet stocks in the late 1990s. In this section, we explicitly examine the impact of this differential media coverage on stock prices. We first conduct the analysis at the firm level, and then conduct the analysis at the portfolio level. A. Firm-Level Analysis The dependent variable is the abnormal return of a firm’s stock estimated by fitting a Fama-French (1993) 3-factor model for each firm. We use contemporaneous Fama-French factors to control for the most recent market-wide information to ensure the conservativeness of our news analysis, though our results are almost identical with respect to both economic and statistical significance if lagged factors are selected. We then examine the impact of different types of news on daily and monthly abnormal returns, respectively, by including number of net news (NN), good news (GN) and bad news (BN) per firm from the previous period (either day or month) as independent variables in our regression model.12 Net news is simply number of good news minus number of bad news per firm from the previous period (either day or month). 12 Busse and Green (2002) use a sample of 322 news reports in the Morning Call and Midday Call segments on CNBC between June 12 and October 27, 2000, and find that prices respond within seconds. In contrast, we focus on aggregate media coverage and its effect on stocks over the entire bubble and post-bubble period. The nature of the print media, the availability of news publications, and the use of market closing prices in calculating returns generate only a daily frequency dataset. So we cannot say whether prices respond within seconds. However, as we will see, the effect lasts at least for a day. Antweiler and Frank (2004a) and Huberman and Regev (2001) also find a prolonged effect of news on returns