CSRC oral communication and corporate disclosure

Chenxue Hou; Qijian  Wang

doi:10.1016/j.jcorpfin.2022.102351

CSRC oral communication and corporate disclosure

Chenxue Hou, Qijian Wang^*

^*Corresponding author for this work

Research output: Contribution to journal › Article (Academic Journal) › peer-review

5 Citations (Scopus)

Abstract

Oral communication has increasingly been used as a policy tool by the China Securities Regulatory Commission (CSRC) to regulate the Chinese financial market. However, less is known about whether and how this newly developed policy tool affects corporate decisions. Using machine-learning techniques, this paper develops a measure to evaluate the CSRC's oral emphasis on financial disclosure based on transcripts of its press conferences and official speeches. We find that when the CSRC places more emphasis on disclosure, both the quantity and quality of corporate disclosure are improved. Further evidence suggests that listed firms with external financing plans respond more to CSRC oral communication. Moreover, under political pressure, state-owned enterprises (SOEs) comply more with CSRC oral communication in terms of disclosure quantity but not disclosure quality.

Original language	English
Article number	102351
Journal	Journal of Corporate Finance
Volume	79
Early online date	13 Jan 2023
DOIs	https://doi.org/10.1016/j.jcorpfin.2022.102351
Publication status	Published - 1 Apr 2023

Bibliographical note

Funding Information:
☆ We are grateful for helpful comments and advice from Hui Chen, Yun Dai, Jianhao Lin, Jun Wang, seminar participants from Lingnan College, Qin Yu from the CSRC, Feng Gao from Guangfa Securities, and Yipin Xu from Anxin Securities. We also thank our research assistants Junyao Wang and Ruijie Xu. All remaining errors are our own. We appreciate the financial support from the National Natural Science Foundation of China (Nos. 72002228,71902199, and 71991474) and National Social Science Foundation of China (No. 21ZDA039).

Funding Information:
We are grateful for the helpful comments and advice from Hui Chen, Yun Dai, Jianhao Lin, Jun Wang, seminar participants from Lingnan College, Qin Yu from the CSRC, Feng Gao from GF Securities, and Yipin Xu from Essence Securities. We also thank our research assistants Junyao Wang and Ruijie Xu. All remaining errors are our own. We appreciate the financial support from the National Natural Science Foundation of China (Nos.72002228,71902199, and 71991474) and the National Social Science Foundation of China (No. 21ZDA039).

Publisher Copyright:
© 2023 Elsevier B.V.

Keywords

Oral communication
Regulatory commission
Corporate disclosure
Machine Learning

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title = "CSRC oral communication and corporate disclosure",

abstract = "Oral communication has increasingly been used as a policy tool by the China Securities Regulatory Commission (CSRC) to regulate the Chinese financial market. However, less is known about whether and how this newly developed policy tool affects corporate decisions. Using machine-learning techniques, this paper develops a measure to evaluate the CSRC's oral emphasis on financial disclosure based on transcripts of its press conferences and official speeches. We find that when the CSRC places more emphasis on disclosure, both the quantity and quality of corporate disclosure are improved. Further evidence suggests that listed firms with external financing plans respond more to CSRC oral communication. Moreover, under political pressure, state-owned enterprises (SOEs) comply more with CSRC oral communication in terms of disclosure quantity but not disclosure quality.",

keywords = "Oral communication, Regulatory commission, Corporate disclosure, Machine Learning",

author = "Chenxue Hou and Qijian Wang",

note = "Funding Information: ☆ We are grateful for helpful comments and advice from Hui Chen, Yun Dai, Jianhao Lin, Jun Wang, seminar participants from Lingnan College, Qin Yu from the CSRC, Feng Gao from Guangfa Securities, and Yipin Xu from Anxin Securities. We also thank our research assistants Junyao Wang and Ruijie Xu. All remaining errors are our own. We appreciate the financial support from the National Natural Science Foundation of China (Nos. 72002228,71902199, and 71991474) and National Social Science Foundation of China (No. 21ZDA039). Funding Information: We are grateful for the helpful comments and advice from Hui Chen, Yun Dai, Jianhao Lin, Jun Wang, seminar participants from Lingnan College, Qin Yu from the CSRC, Feng Gao from GF Securities, and Yipin Xu from Essence Securities. We also thank our research assistants Junyao Wang and Ruijie Xu. All remaining errors are our own. We appreciate the financial support from the National Natural Science Foundation of China (Nos.72002228,71902199, and 71991474) and the National Social Science Foundation of China (No. 21ZDA039). Publisher Copyright: {\textcopyright} 2023 Elsevier B.V.",

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doi = "10.1016/j.jcorpfin.2022.102351",

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AU - Hou, Chenxue

AU - Wang, Qijian

N1 - Funding Information: ☆ We are grateful for helpful comments and advice from Hui Chen, Yun Dai, Jianhao Lin, Jun Wang, seminar participants from Lingnan College, Qin Yu from the CSRC, Feng Gao from Guangfa Securities, and Yipin Xu from Anxin Securities. We also thank our research assistants Junyao Wang and Ruijie Xu. All remaining errors are our own. We appreciate the financial support from the National Natural Science Foundation of China (Nos. 72002228,71902199, and 71991474) and National Social Science Foundation of China (No. 21ZDA039). Funding Information: We are grateful for the helpful comments and advice from Hui Chen, Yun Dai, Jianhao Lin, Jun Wang, seminar participants from Lingnan College, Qin Yu from the CSRC, Feng Gao from GF Securities, and Yipin Xu from Essence Securities. We also thank our research assistants Junyao Wang and Ruijie Xu. All remaining errors are our own. We appreciate the financial support from the National Natural Science Foundation of China (Nos.72002228,71902199, and 71991474) and the National Social Science Foundation of China (No. 21ZDA039). Publisher Copyright: © 2023 Elsevier B.V.

PY - 2023/4/1

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N2 - Oral communication has increasingly been used as a policy tool by the China Securities Regulatory Commission (CSRC) to regulate the Chinese financial market. However, less is known about whether and how this newly developed policy tool affects corporate decisions. Using machine-learning techniques, this paper develops a measure to evaluate the CSRC's oral emphasis on financial disclosure based on transcripts of its press conferences and official speeches. We find that when the CSRC places more emphasis on disclosure, both the quantity and quality of corporate disclosure are improved. Further evidence suggests that listed firms with external financing plans respond more to CSRC oral communication. Moreover, under political pressure, state-owned enterprises (SOEs) comply more with CSRC oral communication in terms of disclosure quantity but not disclosure quality.

AB - Oral communication has increasingly been used as a policy tool by the China Securities Regulatory Commission (CSRC) to regulate the Chinese financial market. However, less is known about whether and how this newly developed policy tool affects corporate decisions. Using machine-learning techniques, this paper develops a measure to evaluate the CSRC's oral emphasis on financial disclosure based on transcripts of its press conferences and official speeches. We find that when the CSRC places more emphasis on disclosure, both the quantity and quality of corporate disclosure are improved. Further evidence suggests that listed firms with external financing plans respond more to CSRC oral communication. Moreover, under political pressure, state-owned enterprises (SOEs) comply more with CSRC oral communication in terms of disclosure quantity but not disclosure quality.

KW - Oral communication

KW - Regulatory commission

KW - Corporate disclosure

KW - Machine Learning

U2 - 10.1016/j.jcorpfin.2022.102351

DO - 10.1016/j.jcorpfin.2022.102351

M3 - Article (Academic Journal)

SN - 0929-1199

VL - 79

JO - Journal of Corporate Finance

JF - Journal of Corporate Finance

M1 - 102351

ER -

CSRC oral communication and corporate disclosure

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Keywords

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