Analyzing the Impact of External Factors on Stock Market Performance in the Fast Food Industry During Boycott Movements Using Multiple Linear Regression

Pengarang

  • Adibah Amni Shamsulbadri Universiti Utara Malaysia
  • Ros Idayuwati Alaudin Universiti Utara Malaysia
  • Ayu Abdul Rahman Universiti Utara Malaysia

DOI:

https://doi.org/10.32890/jcia2026.5.1.1

Kata kunci:

Boycott, McDonalds's, multiple linear regression, Pizza Hut, stock market performance

Abstrak

This study examines the impact of boycott movements on the stock market performance of McDonald’s and Pizza Hut from October 2023 to January 2025. Using Multiple Linear Regression (MLR), the research analyzes key factors affecting stock prices, including war-related news, boycott-related news, trading volume, and boycott duration. The findings indicate that historical stock prices significantly influence both companies’ stock performance, with McDonald’s showing an R-squared value of 83.0% and Pizza Hut 67.7%. War-related and boycott-related news were measured based on weekly article frequency, trading volume was obtained from official stock data, and boycott duration was calculated as the number of days since the boycott announcement, measured weekly. Trading volume was found not to be statistically significant in affecting McDonald’s stock price at the 99% confidence level. At the same time, other factors, such as boycott-related news and war-related news, do not have a significant impact on either company. The results suggest that investors prioritize historical stock trends over external socio-political events. This study provides insights into the financial consequences of consumer activism and can assist investors, policymakers, and business strategists in understanding market reactions to boycott movements.

Rujukan

Muat turun

Diterbitkan

26262626-Januari01-3131

Cara Memetik

Analyzing the Impact of External Factors on Stock Market Performance in the Fast Food Industry During Boycott Movements Using Multiple Linear Regression. (2026). Journal of Computational Innovation and Analytics (JCIA), 5(1), 1-19. https://doi.org/10.32890/jcia2026.5.1.1

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