Corporate Transparency and Investors’ Perception of Risk with Big Data Mining

DOI

10.2991/978-94-6463-198-2_110How to use a DOI?

Keywords

corporate transparency; investors’ perception of risk; big data mining; futures hedging; the event study method

Abstract

To reveal the factors impacting investors’ perception of risk in stock market, this paper studied the performance of stocks with different corporate transparency under the shock of a futures hedging contingency. Through big data mining and further analysis, including crawling down announcements of listed companies with Python to scale corporate transparency, calculating the abnormal return of stocks with a market model to indicate investors’ perception of risk, testing the negative impact caused by the contingency with the event study method and verifying the relationship between corporate transparency and investors’ perception of risk with a cross-sectional regression model, this paper finds that the increased corporate transparency by active disclosure can effectively reduce investors’ perception of risk, while the passive disclosure cannot. The innovation of this paper includes the findings of the relationship between corporate transparency and investors’ perception of risk and the method of quantifying corporate transparency.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Wen Mu
AU  - Jing Zhang
AU  - Ziyang Li
PY  - 2023
DA  - 2023/08/10
TI  - Corporate Transparency and Investors’ Perception of Risk with Big Data Mining
BT  - Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
PB  - Atlantis Press
SP  - 1068
EP  - 1077
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-198-2_110
DO  - 10.2991/978-94-6463-198-2_110
ID  - Mu2023
ER  -