<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper examines the impact of media communication on the stock market in the context of the digital age, particularly its role in stock price volatility. With the significant increase in the speed, breadth and depth of media communication, the efficiency of stock market information transmission has been dramatically improved, significantly impacting stock price volatility. The article first explores the specific impact of media coverage characteristics and methods on stock price volatility by quantitatively analyzing media coverage’s breadth, manner and content. Then, the article utilizes multiple linear regression models to deeply explore the impact of various news texts on stock price volatility in different reporting modes (e.g., live reporting, television, and text reporting) in the Shanghai and Shenzhen stock markets. The findings show a significant association between the breadth of media coverage and stock prices at the 1% significance level. In addition, the regression coefficients of content and manner of coverage on stock price changes are 0.096 and 0.229, respectively, which are both significant at the 5% significance level. The research results of this paper not only reveal the close relationship between media communication and stock market volatility and provide theoretical support for predicting dramatic stock market volatility, which helps to promote the construction of a healthy and orderly capital market order.</p>
</abstract>

This paper examines the impact of media communication on the stock market in the context of the digital age, particularly its role in stock price volatility. With the significant increase in the speed, breadth and depth of media communication, the efficiency of stock market information transmission has been dramatically improved, significantly impacting stock price volatility. The article first explores the specific impact of media coverage characteristics and methods on stock price volatility by quantitatively analyzing media coverage’s breadth, manner and content. Then, the article utilizes multiple linear regression models to deeply explore the impact of various news texts on stock price volatility in different reporting modes (e.g., live reporting, television, and text reporting) in the Shanghai and Shenzhen stock markets. The findings show a significant association between the breadth of media coverage and stock prices at the 1% significance level. In addition, the regression coefficients of content and manner of coverage on stock price changes are 0.096 and 0.229, respectively, which are both significant at the 5% significance level. The research results of this paper not only reveal the close relationship between media communication and stock market volatility and provide theoretical support for predicting dramatic stock market volatility, which helps to promote the construction of a healthy and orderly capital market order.

A study of the impact of financial news media coverage patterns on stock price fluctuations

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper examines the impact of media communication on the stock market in the context of the digital age, particularly its role in stock price volatility....

A study of the impact of financial news media coverage patterns on stock price fluctuations

Published Online: Feb 26, 2024

Page range: -

Received: Jan 15, 2024

Accepted: Jan 21, 2024

DOI: https://doi.org/10.2478/amns-2024-0592

Keywords
Multiple linear regression model, Stock price volatility, Quantitative analysis, Financial reports

© 2024 Ziyu Xue et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A study of the impact of financial news media coverage patterns on stock price fluctuations

Published Online: Feb 26, 2024

Page range: -

Received: Jan 15, 2024

Accepted: Jan 21, 2024

DOI: https://doi.org/10.2478/amns-2024-0592

KeywordsMultiple linear regression model, Stock price volatility, Quantitative analysis, Financial reports

© 2024 Ziyu Xue et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Multiple linear regression model, Stock price volatility, Quantitative analysis, Financial reports