Specifically, I test two types of investors' reactions to media coverage of industry leaders during their EAs. Then I test different types of investor trading behavior in relation to leaders' news surrounding their earnings announcements. In this paper, I take a step further to investigate the extent to which different types of investors perceive peer information differently and asymmetric peer price response to leaders' good and bad news.
The main focus of my research is related to two areas of existing literature, including the different types of investor attention and peer trading behavior around intra-industry executive news releases during the peer non-disclosure period, and whether sales of Short limitations affect the process of absorbing information. Therefore, due to the well-documented positive information spillover within the same industry (Hou, 2007; . Thomas and Zhang, 2008; Arif and George, 2020), I expect that institutional investors tend to buy stocks that are covered by the industry good news of the leaders. Again, the news of executives within the industry serves as an indicator of the performance of the industry once institutional investors see bad news from them.
As a result of the short selling restriction, institutional investors will sell peers gradually in response to bad news of executives within the industry. To examine the impact of leader news on peer stocks, I use the media coverage of leaders released during each of their earnings announcements as an alternative source of price-related information for peers in the same industry. A roundup of the industry leaders' news revolution that occurred during each earnings announcement (EA).
My sample event data consists of 1,755 quarterly announcements by industry leaders disclosed by leading Chinese online financial media.
EMPIRICAL RESULTS
Table 3 documents the cumulative abnormal return (CAR) of all news sentiment, including good and bad news, as determined by an event study of intra-industry leaders' earnings announcements. To calculate abnormal return, the measure is the predicted return according to the CAPM model. T -statistics and P -statistics appear in parentheses. indicates significant at the 1% level, ** indicates significant at the 5% level, and * indicates significant at the 10% level.
According to the percentage of positive and negative words found in the article, news articles are classified as good or bad. The average abnormal return for good news is 0.18% on industry leader news days, which is economically significant at 1% levels. An abnormal return for bad management news is approx. 0.12% on industry leaders' news days, which reaches significance at 1%.
Nevertheless, we can see the share price return to negative until next month. For stocks covered by good management news, the cumulative abnormal returns are -1.17% until the next quarter, which is economically significant at 1% levels. For stocks covered by bad manager news, the cumulative abnormal returns are -3.54% until the next quarter, which is highly significant at 1% levels.
The preliminary results show that both managers' good and bad news are accompanied by an increase in peers' stock prices at first, although the price returns to negative after one quarter. In my robust test, I also calculated the abnormal return based on the well-organized benchmark, which was the average return of all matched CRSP companies on a triple-sorted portfolio sorted by decile of size, BM, and momentum. In addition, I also use the sigh of abnormal return of leaders on the news day to identify the tone of news, the result remains the same.
This plot documents the cumulative abnormal return (CAR) of all good news, as determined by a study of earnings announcements from industry leaders. To calculate the abnormal return, the benchmark is the predicted return according to the CAPM model. This plot documents the cumulative abnormal return (CAR) of all bad news, as determined by a study of earnings announcements from industry leaders.
EXPLANATION
The table documents the abnormal Baidu index search volume for institutional and retail investors surrounding the news of leading companies, as determined by an event study of industry leaders' earnings announcements. Institutional investors, faced with the news of industry leaders, actively use Baidu search engines to find peer companies based on their stock indices. Table 5 documents the unusual volume of buying and selling by institutional and retail investors in response to good news from executives, as found through an event study of industry leaders' earnings announcements.
This plot documents the abnormal volume of institutional investors' buying and selling in response to good leader news, as determined by an event study of leader earnings announcements within the industry. This plot documents the abnormal volume of retail investors' buying and selling in response to good leader news, as determined by an event study of leader earnings announcements within the industry. This Table 6 documents the abnormal buying and selling volume of institutional and retail investors in response to bad news of leaders, as determined by an event study of leader earnings announcements within the industry.
This plot documents the institutional investors' abnormal buying and selling volume in response to leaders' bad news, as determined by an event study of intra-industry leaders' earnings announcements. This plot documents the retail investors' abnormal buying and selling volume in response to leaders' bad news, as determined by an event study of intra-industry leaders' earnings announcements. I test different types of investor trading behavior in relation to leaders' news surrounding their earnings announcements.
The table reports the search behavior of Baidu institutional and retail investors in response to executive good news using logistic regression. A positive 𝑏+ indicates that institutional buying trading activity is abnormally high in response to the leaders' shock with good news. log _ab_buy',) is defined as the abnormal buying trading volume by institutional investors on news day t. The table reports the trading behavior of institutional investors in response to good news from managers using logistic regression.
The table reports institutional investors' trading behavior in response to managers' good news using OLS regression. The table reports cross-sectional analysis of the effects of the number of firm-specific news and firm size on institutional investors' overreaction to executive news. This table documents the cumulative abnormal return (CAR) of around managers' bad news, as determined by an event study of earnings announcements within the industry.
Use the positive (negative) sign of the leader return on the news day as a proxy for the good and bad news, peer feedback response to a negative leader return shock. This table documents the cumulative abnormal return (CAR) per positive and negative executive return shock as determined by an event study of industry executive earnings announcements.
Further analysis
Conclusion
The Dark Side of Low Financial Reporting Frequency: Investors' Dependence on Alternative Sources of Earnings News and Excessive Information Spillovers. Good reasons to sell: Reason-based choice among group and individual investors in the stock market.
