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author:

Gao, Yihong (Gao, Yihong.) [1] (Scholars:高艺红) | Gao, Jiayan (Gao, Jiayan.) [2] | Li, Haili (Li, Haili.) [3]

Indexed by:

SSCI Scopus

Abstract:

Green credit regulation may increase the operating uncertainty and worsen the financial reporting quality for pollution intensive firms. Literature mentions that enhanced information asymmetry would trigger more irrational trading and lead to a decrease in the market efficiency, which is worth investigating under green credit regulation. Using the data of China's A-share listed firms from 2009 to 2015, we study the relationship between green credit regulation and market efficiency based on a difference-in-differences analysis. It finds that green credit regulation reduces market efficiency of pollution intensive firms. Mechanism tests show that policy effects are more pronounced in firms with higher operating uncertainty, greater information asymmetry and stocks with more irrational traders. This study not only contributes to a deeper understanding of the impact of green credit policies on business and stock markets based on irrational theory, but also provides valuable guidance for green finance and economic development.

Keyword:

Emerging markets Green credit regulation Irrational trading Market efficiency

Community:

  • [ 1 ] [Gao, Yihong]Fuzhou Univ, Sch Econ & Management, Fuzhou, Peoples R China
  • [ 2 ] [Gao, Jiayan]Nanjing Audit Univ, Sch Internal Audit, Nanjing, Peoples R China
  • [ 3 ] [Li, Haili]Xian Univ Finance & Econ, Sch Business, Xian, Peoples R China
  • [ 4 ] [Li, Haili]Xiamen Univ, Sch Management, Xiamen, Peoples R China

Reprint 's Address:

  • [Li, Haili]Xian Univ Finance & Econ, Sch Business, Xian, Peoples R China;;[Li, Haili]Xiamen Univ, Sch Management, Xiamen, Peoples R China;;

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Source :

ECONOMIC ANALYSIS AND POLICY

ISSN: 0313-5926

Year: 2024

Volume: 82

Page: 199-219

7 . 9 0 0

JCR@2023

CAS Journal Grade:2

Cited Count:

WoS CC Cited Count: 2

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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