Evidence map›Paper›PMID 37251833›Full record

ArticleHeliyon2023

The effects of environmental information disclosure on stock price synchronicity in China.

Yongliang Yang, Jitao Zhang, Yi Li

Abstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yongliang YangSchool of Economics and Management, Zhejiang Sci-Tech University, Hangzhou, China.
Jitao ZhangSchool of Economics and Management, Zhejiang Sci-Tech University, Hangzhou, China.
Yi LiFashion Department, DongHai Academy, Collaborative Innovation Center of Port Economy, Ningbo University, Ningbo, 315211, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Environmental information disclosure has attracted the attention of the capital market because it can convey corporate characteristic information. But there needs to be direct evidence that environmental information disclosure can improve the market's overall efficiency. This study examines whether corporate environmental information disclosure can improve the information efficiency of the capital market. This study takes Chinese listed companies from 2008 to 2021 as samples, constructs a panel fixed effect model, and adopts multiple linear, instrumental variable method and Heckman sample selection model. We find that the disclosure of environmental information in the Chinese market reduces the information efficiency of the capital market represented by the synchronicity of stock prices. The reason is that the information after greenwashing by enterprises needs to be of better quality and more obscure, which disrupts market information. Environmental information disclosure by enterprises prone to greenwashing significantly affects stock price synchronicity, such as low institutional ownership, non-state-owned, growth, and manufacturing firms. Finally, this paper discusses the impact mechanism and confirms that stock liquidity and analyst coverage are the two paths through which environmental information disclosure impacts stock price synchronicity. This study is significant in encouraging the government to strengthen market supervision, promoting enterprises to disclose high-quality environmental information, and improving the pricing efficiency of the capital market.

Indexed as

Capital market pricing efficiencyEnvironmental information disclosureGreenwashingStock price synchronicity

Identifiers

PMID37251833
PMCPMC10213183

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.