Does individual investors’ online search activities reduce information asymmetry? Evidence from stock exchanges’ comment letters in China

ABSTRACT

Commonly treated as ‘dumb noise traders’, individual investors are widely known as the group who are unaware, unsophisticated, and have low financial literacy. Their trading behavior in the capital markets is often assumed away by previous literature, let alone their information demand and the impacts on capturing the market pricing dynamics. In this study, we build a framework for understanding the information demand of individual investors and analyze their information role using the releases of comment letters in China as the setting. We find that: (a) individual investors’ information demand is high when there is an inferior information environment; (b) individual investors’ online search behavior dampens negative shocks to unexpected events. In an additional analysis, we find that the information searching of investors has abating effects on the cost of information remediation.

KEYWORDS:

• Individual investors’ behavior
• investors’ information demand
• comment letters
• emerging markets

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. It is possible that both roles impact the information collection in the Chinese capital market due to this institutional setting’s uniqueness. In our study, we assume the interpretation role dominate the effects of investors’ information searching on the market reaction to comment letters. Further, our results are robust to using ‘triggered demand’ for information in the section of supplementary tests.

2. Unlike previous literature that also involves ‘no-letter firms’, we focus our studies based on firms that have receive comment letters due to the concern of self-selection bias. This is because in China: the decision of publicize a comment letter by the stock exchanges is not randomly made; and the choice of reviewing a firm’s disclosure filings is not a regular routine but based on regulatory agencies’ professional judgements.

3. http://index.baidu.com/.

4. The figure comparing the trends of Baidu Index using the ticker and full name of ‘Kweichow Moutai’ is shown in the Appendix.

5. Specifically, we use a Chinese Python module called ‘jieba’ (Chinese for ‘to stutter’) to segment the words in the comment letters and develop a wordlist consisting of 1,076 positive words and 3,758 negative words. Our list refers to the wordlist developed by (Henry 2008) based on earnings press releases and the wordlist developed by Loughran and McDonald (2011) based on 10-K filings.

6. We also use proportional occurrence of accounting terms to measure the level of severity and employ the NTUSD database (a Chinese sentiment dictionary developed by National Taiwan University which contains 2,810 positive words and 8,276 negative words) to measure the tone of the letter. The results are qualitatively consistent. For the sake of brevity, the results are untabulated but are available upon request.

Additional information

Funding

This work was supported by the Jilin Province Philosophy and Social Science Planning Fund Office [2018BS37].