Are analysts effective speaking tubes? Evidence from site visits

ABSTRACT

This study examines the association between analysts’ site visits and stock price crash risk using a dataset of Chinese firms listed on the Shenzhen Stock Exchange (SZSE) from 2012 to 2019. We find that analysts’ site visit frequency is positively associated with future stock price crash risk, and the positive association is primarily driven by the firms that analysts keep silent on. Furthermore, we confirm that analysts’ silence is associated with negative news about firms’ fundamentals. We also show that the effect of analysts’ silence on crash risk is more pronounced when analysts face conflicts of interest, when firms have poorer information transparency, and when firms have fewer alternative information channels. Overall, our findings shed light on the dark side of analysts’ site visits.

KEYWORDS:

• Site visits
• analyst behaviour
• crash risk
• hidden information

JEL CLASSIFICATION:

• G12
• G14
• G32

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ We caution that our findings should be interpreted as denoting a positive association between analysts’ site visit frequency and stock price crash risk rather than a causal effect.

² Among all visits that analysts participated in from 2012 to 2019, 79.35% were joint visits.

³ In Article 41 of the Guidelines of Investor Relations Management, the SZSE states that “Listed companies should do their utmost to accommodate requests from investors, analysts and fund managers to visit company and project sites.”.

⁴ For more details on the background of analysts’ site visits in China, see Cheng et al. (2016).

⁵ Hudongyi is an official interactive platform for the investor relations of listed companies created by the SZSE. The website is located at http://irm.cninfo.com.cn.

⁶ See an article published by Shanghai Securities News at https://company.cnstock.com/company/scp_gsxw/202201/4812472.htm.

⁷ Untabulated results suggest that our results are robust when using yearly crash risk measures.

⁸ Since some visit reports only disclose the names of brokerages to which analysts belong, we follow Chang, Chi, and Wu (2017) and Han, Kong, and Liu (2018) to merge visit data and report data at the brokerage level.

⁹ Corresponding to the quarterly visit variables, we calculate the average value of analysts’ work experience visited in a quarter.

¹⁰ $Dummy$ is coded as one for 44.68% of the sample.

¹¹ Following Han, Kong, and Liu (2018), we assume that there is a commission relationship between a brokerage firm and a fund if the fund pays commissions to the brokerage firm in the quarter of visits. The total commission payments made by each fund and their distributions among brokerage firms are drawn from RESSET (https://www.resset.com).

¹² Following Xu et al. (2013b), we define an analyst as a star analyst if he is selected by New Fortune magazine as the best analyst in the prior year. The star analyst data are from the CSMAR database.

¹³ We obtain media coverage data from the Chinese Research Data Services Platform (CNRDS). The CNRDS documents the data of over 600 national and local news outlets, including the major financial newspapers widely circulated in China.

¹⁴ Guba Eastmoney is one of the largest and most popular internet stock message boards in (Li, Shen, and Zhang 2018).

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [71790592]. National Natural Science Foundation of China [71771147]