By reviewing the coincident news reports and financial analysis articles to identify the proximate causes for the largest daily index returns, Cutler et al. (1989) estimate the fraction of the variation in stock movements that can be attributed to public news and Kaminsky and Schmukler (1999) analyze the type of news that rocks the stock markets during the Asian Crisis. Similarly, Kim and Mei (2001) identify jump return dates in Hong Kong stock market and reckon the frequent association between the largest stock market movements and political news.
Motivated by this strand of empirical studies, a modified ARIMA outlier detection procedure of Vandaele (1983) is used to identify dates with fifty largest outliers in index returns and assess the coincidence between return hikes and regulatory changes or policy adjustments reported by the newspaper media.8 A similar approach has been used by Lo and Chan (2000) to analyze the shocks in ‘Greater
8 As a robustness check, I also use a less sophisticated approach of Culter et al. (1999) to match the largest daily index returns to their ‘causes’. The results are similar to those reported in Table 3.2. The advantage of using ARIMA outlier detection procedure is its ability to identify level shifts and trend in stock market movements, which are however, not exist in the return series I examine in this chapter. Vandaele (1983) provides a comprehensive description for the methodology used in this section.
China’ stock markets. They observe that most return outliers in Shanghai and Shenzhen B-share markets are driven by local events, such as government policy announcements.
To identify the events that ‘cause’ the market return hikes, I check the relevant news reports and financial analysis articles on People’s Daily (the most authoritative newspaper in China with the highest circulation rate, whose online search function is available at http://www.peopledaily.com.cn), China Securities, Securities Times and Shanghai Securities (three official securities market publications authorized by the CSRC), and China Securities Bulletin (a Hong Kong-based financial press available at Factive information service provided by Reuters and Dow Jones).9 An event is defined to ‘cause’ or ‘match’ with return outliers if two criteria are met: (1) the event falls on the same day or one trading day before the outlier date and (2) reported by at least one news report and financial analysis article to explain the unusual stock market movement. The appendix 2 presents the dates, sizes and t-values for the fifty largest return outliers and their associated event dates and contents.10 For a number of outlier dates with
9 Inclusion of China Securities Bulletin into the data sources makes it possible to match the return outliers with market rumors about possible regulatory changes and political events, which sometimes may not appear on the official media of Mainland China to explain the large stock market movements. Interviews with some Chinese domestic equity analysts and investors confirm that such information is generally accessible to domestic investors through the brokerage houses in the forms of informal handouts, ‘word of mouths’ or internet forums.
10 Following Culter et al. (1999) I identify fifty largest daily movements for each of the index return series. When I assess thirty and seventy outliers, the proportions of return outliers attributed to regulatory events are close to those reported in this section. There is an AR(1) item for the SHB returns and a MA(1) item for the SZB returns. No AR or MA item is found for SHA and SZA return time series.
no plausible explanation of extreme daily returns provided by news reports and financial analysis articles, the event entries are left empty.
Table 3.2 shows the classification of the ‘causes’ of the fifty largest daily return outliers.11 Authorities’ regulatory changes and policy adjustments ‘cause’ more than 60% of return outliers for SHA market and about 50% for SZA, SHB and SZB markets. A further perusal of outlier causing regulatory events indicates that most are related to (1) demand boosting policy and measure; (2) disciplinary action and strengthening of supervision; (3) market supply expansion and (4) transaction cost adjustment. In Table 3.2, I further classify the regulatory events into the above four categories, with the rest put under the ‘others’ category.
Several largest market shifts are brought out by ‘market rumor/ expectation’ about the regulatory change and policy adjustment. Rumors about political issues, such as the health of China’s leader Deng Xiaoping, also rock the market a few times.
6%, 8%, 10% and 8% of return outliers in SHA, SZA, SHB and SZB are related to political events, respectively. ‘Macroeconomic data announcement’ could be used to match only 0-2 cases of 50 return outliers, the result seems to complement Kutan and Yuan (2003) who uncover that macroeconomic news release dummy variable has no statistically significant impact on China’s stock markets in general.
I also find that for each submarket two return outliers occur on the first trading days after the long Chinese New Year pubic holidays. 2%, 12% and 4% of daily
11 The percentages for all ‘causes’ categories may add up to more than 100% since certain return outliers can be explained by more than one events by the media reports and financial analysis articles, therefore outlier dates may be coded under multiple categories of ‘causes’.
return outliers in SZA, SHB and SZB are associated with the fluctuation in other stock markets, for most cases, the Hong Kong stock market.
Table 3.2
Classification of ‘Causes’ of Fifty Largest Daily Return Outliers in China’s Submarkets (1995-2003) – Demand boosting policy and measure 6
(12%) – Disciplinary action and strengthening of
supervision 7 – Transaction cost adjustment 1
(2%) Market Rumor / Expectation 6
(12%) Macroeconomic Data Announcement 1
(2%) 1st trading day after Chinese New Year
Holiday Fluctuation in Other Markets 0
(0%) Note: The percentages for all ‘causes’ categories add up to more than 100% since certain outlier dates are coded under multiple ‘causes’.
The main finding of this section is that about half of daily return outliers in China’s stock markets are closely associated with the regulatory changes or policy adjustment announcements, as reported by the news reports and financial analysis articles. In contrast, Kaminsky (1998) finds that about half of the largest U.S.
stock market swings are unrelated to the arrival of public news. This section provides a general picture of the importance of regulatory events in influencing the Chinese stock market movements, which motivates a further study of the market responses to certain categories of regulatory events.