ESG controversies and investor trading behavior in the Korean market

Jeongseok Bang; Doojin Ryu; Jinyoung Yu

doi:10.1016/j.frl.2023.103750

ESG controversies and investor trading behavior in the Korean market

Jeongseok Bang
, Doojin Ryu
, Jinyoung Yu

Department of Economics

Sungkyunkwan University

Research output: Contribution to journal › Article › peer-review

66 Scopus citations

Abstract

This study examines how investor trading behavior changes following environmental, social, and governance (ESG) controversies by analyzing textual news data. We use deep-learning-based natural language processing to classify news articles into specific categories of controversy. ESG controversies generally increase investors’ trading activities regardless of their type, while their reactions differ by ESG pillar. Interestingly, domestic institutions tend to sell stocks with controversies.

Original language	English
Article number	103750
Journal	Finance Research Letters
Volume	54
DOIs	https://doi.org/10.1016/j.frl.2023.103750
State	Published - Jun 2023

Keywords

ESG
Institutional investor
Investor trading behavior
Natural language processing

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10.1016/j.frl.2023.103750

Cite this

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title = "ESG controversies and investor trading behavior in the Korean market",

abstract = "This study examines how investor trading behavior changes following environmental, social, and governance (ESG) controversies by analyzing textual news data. We use deep-learning-based natural language processing to classify news articles into specific categories of controversy. ESG controversies generally increase investors{\textquoteright} trading activities regardless of their type, while their reactions differ by ESG pillar. Interestingly, domestic institutions tend to sell stocks with controversies.",

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author = "Jeongseok Bang and Doojin Ryu and Jinyoung Yu",

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AU - Ryu, Doojin

AU - Yu, Jinyoung

PY - 2023/6

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N2 - This study examines how investor trading behavior changes following environmental, social, and governance (ESG) controversies by analyzing textual news data. We use deep-learning-based natural language processing to classify news articles into specific categories of controversy. ESG controversies generally increase investors’ trading activities regardless of their type, while their reactions differ by ESG pillar. Interestingly, domestic institutions tend to sell stocks with controversies.

AB - This study examines how investor trading behavior changes following environmental, social, and governance (ESG) controversies by analyzing textual news data. We use deep-learning-based natural language processing to classify news articles into specific categories of controversy. ESG controversies generally increase investors’ trading activities regardless of their type, while their reactions differ by ESG pillar. Interestingly, domestic institutions tend to sell stocks with controversies.

KW - ESG

KW - Institutional investor

KW - Investor trading behavior

KW - Natural language processing

UR - https://www.scopus.com/pages/publications/85150041149

U2 - 10.1016/j.frl.2023.103750

DO - 10.1016/j.frl.2023.103750

M3 - Article

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SN - 1544-6123

VL - 54

JO - Finance Research Letters

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ER -

ESG controversies and investor trading behavior in the Korean market

Abstract

Keywords

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