Regional GDP Distortion and Analyst Forecast Accuracy: Evidence from China

Abstract

The reliability of China’s GDP data has been questioned for a long time. Prior studies have discussed the causes of GDP distortion in China, but the evidence on the economic consequences is scant. This paper examines the economic consequences of regional GDP distortion from the perspective of analyst forecasts. We find that regional GDP distortion leads to lower analyst forecast accuracy, and this result is robust to potential endogeneity. Further investigations show that analysts with information advantages have the ability to resist the distorted GDP data and issue accurate forecasts. Political pressure is another factor leading to inaccurate analyst forecasts. Our paper contributes to the literature by highlighting the importance of the reliability of GDP figures as a determinant of analyst forecast accuracy.

KEYWORDS:

• Regional GDP distortion
• information advantages
• political pressure
• analyst forecast accuracy

JEL CLASSIFICATIONS:

• G1
• M4

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 China has launched the reform of unified statistics system. Related news: https://xw.qq.com/cmsid/20200105A0GUUJ00 (visited on March 24, 2021).

2 Even if analysts have anticipated the biased GDP information into their forecasts, such adjustment could not be 100% perfect since analysts do not have the perfect information on the deviation of the inflated GDP. Therefore, compared with the unbiased GDP information, the inflated GDP information could still lower the forecast accuracy lower.

3 Because we only focus on analyst forecasts with duration within two years, the possible values of z are 0, 1, or 2.

4 In our sample, some earnings forecasts are issued by analyst teams. For this part of sample, control variables for analyst characteristics are to control features of analyst teams. However, majority of forecasts are issued by a single analyst. The ratio of analyst reports issued by analyst teams is less than 30% in each year of our sample period. Thus, we don’t distinguish analyst teams from analysts and collectively call them as ‘analyst’ in following sections.

5 We thank anonymous referees for pointing this out.

6 http://irm.cninfo.com.cn/szse/index.html

7 CSMAR provides articles from main business press (e.g. China Securities Journal, Securities Daily, Securities Times and so on) in the Chinese capital market. We use press articles related to listed firms in the following analyses.

8 We drop the analyst reports issued before the firm’s first earnings announcement after its IPO so that observations decline from 96,764 to 96,469.

Additional information

Funding

Danglun Luo acknowledges financial support from the National Natural Science Foundation of China (NSFC Grant Numbers: 71790603, 71872186), the Basic and Applied Basic Research Funding of Guangdong Province, China (Grant Number: 2019A1515011409), and the Soft Science Research Project of Guangdong Province, China (Grant Number: 2019A1010). Feida Zhang acknowledges financial support from the MOE (Ministry of Education in China) Project of Humanities and Social Sciences [grant number 20JZD014, 17YJA790012].