Can Rumor Clarification Eliminate the Effects of Rumors? Evidence From China

This﻿article﻿analyzes﻿the﻿effects﻿of﻿rumor﻿and﻿official﻿rumor﻿clarification﻿on﻿Chinese﻿stock﻿returns﻿under﻿different﻿rumor﻿conditions﻿using﻿an﻿event﻿study.﻿The﻿results﻿are﻿based﻿on﻿a﻿sample﻿of﻿832﻿rumor﻿ clarification﻿announcements﻿from﻿China﻿Listed﻿Companies﻿spanning﻿the﻿period﻿of﻿2015﻿to﻿2017.﻿ The﻿results﻿show﻿that﻿the﻿average﻿cumulative﻿abnormal﻿return﻿after﻿the﻿rumor﻿event﻿is﻿significantly﻿positive﻿in﻿the﻿positive﻿rumor﻿sample﻿and﻿neutral﻿sample,﻿and﻿significantly﻿negative﻿in﻿the﻿negative﻿ rumor﻿sample.﻿After﻿the﻿clarification﻿announcements,﻿we﻿find﻿the﻿announcements﻿effective﻿for﻿the﻿ positive﻿and﻿neutral﻿rumor﻿sample,﻿but﻿not﻿in﻿the﻿case﻿of﻿the﻿negative﻿sample.﻿However,﻿by﻿comparing﻿ different﻿clarification﻿times﻿of﻿each﻿sample,﻿we﻿find﻿that﻿the﻿earlier﻿the﻿clarification﻿time﻿is,﻿the﻿smaller﻿ the﻿impact﻿on﻿the﻿companies﻿in﻿positive﻿and﻿negative﻿rumor﻿examples.


Research Results for the Negative Rumor Clarification Example
In this part, we show the effect of negative rumor clarification on the stock price in different clarificationtimeintervalsaftertherumor.Weillustratethetwoaspectsofthedescriptivestatistical resultsoftheabnormalreturnsandtheresultsofZtestrespectively.

Figure 2. CAR after rumor day for full example over time
Table4reportsthemeanvalueandZvalueoftheCARofdifferentnegativerumorsamples.

Research Results for the Positive Rumor Clarification Example
Whatthissectionshowsistheresultsoftheinfluencetomarketvalueaftertherumordayforthe positive rumor in different announcement times.We illustrate the two aspects of the descriptive statisticalresultsoftheabnormalreturnsandtheresultsofZtestrespectively.In Table 5, we notice that most companies, more than 70%, chose to issue a clarification announcementwithinoneday,amongthecompaniesthatchosetoclarifyapositiverumor.Specifically, companieswhichchosetoissuetheclarificationannouncementintheseconddayandmorethantwo daysis16.35%and12.50%inourresearchsample,respectively.

Research Results for the Neutral Rumor Clarification Example
Whatthissectionshowsistheresultsoftheinfluencetomarketvalueaftertherumordayforthe neutral rumor in different announcement times.We illustrate the two aspects of the descriptive statisticalresultsoftheabnormalreturnsandtheresultsofZtestrespectively.
From Table 6, we know that for China listed companies, clarifying the neutral rumors can eliminatetheimpactofrumorsonthestockpriceofthecompany.Wecanseefromallgroups(C1\  Future research could pursue several other directions.First, it could explore the impact of clarificationannouncementonfirmswhentherumorscomefromdifferentsources,suchaswhetherthe rumorscomefromauthoritativemedia.Moreover,futurestudiescouldprovideadvisestofirmsfacing therumorsandcouldprovideevidencewhenandhowtoclarifytherumorswillhaveabesteffect.

Figure 1 .
Figure 1.Estimation and event period

Figure 3 .
Figure 3. CAR after rumor day for negative example over time

Table 3 . Cumulative abnormal returns after rumor clarification over time
a Z-statistic is under the null hypothesis that the average excess return is zero.b T is the rumor day and D is the rumor clarification day.*p<0.1, ** p<0.05, *** p<0.01

Table 4 . Cumulative abnormal returns after rumor day for negative rumor
a Z-statistic is under the null hypothesis that the average excess return is zero.b Sample sizes are in parentheses.*p<0.1, ** p<0.05, *** p<0.01

Table 5 . Cumulative abnormal returns after rumor day for positive rumor
a Z-statistic is under the null hypothesis that the average excess return is zero.b Sample sizes are in parentheses.*p<0.1, ** p<0.05, *** p<0.

Value Mean Z-Value Mean Z-Value Mean Z-Value Mean Z-Value Mean Z-Value
Z-statistic is under the null hypothesis that the average excess return is zero.b Sample sizes are in parentheses.*p<0.1, ** p<0.05, *** p<0.01 a