Explaining Closing Stock Prices Using Daily Market Indicators: Evidence from the Global Restaurant Industry

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Published: 2026-03-30
DOI: https://doi.org/10.48161/qelj.v5n1a23
Keywords:
Accounting Performance, Stock Price Explanation, Restaurant-Related Firms, Market Indicators, Regression, Value Relevance

Main Article Content

Septien Dwi Savandha
School of Management, UTEL University — Universidad Tecnológica Latinoamericana en Línea, Mexico
Kumaresu Murugasu
School of Global Hospitality and Tourism, Asia Pacific University of Technology & Innovation (APU), Kuala Lumpur, Malaysia.
Jorge Isaac Torres Manrique
Praeeminentia Iustitia Interdisciplinary School of Fundamental Rights (PIISFR), Arequipa, Peru;

Abstract

This study examines the extent to which daily market trading variables explain closing stock prices in a dataset of listed restaurant-related firms. The empirical model uses the closing stock price as the dependent variable and the opening price, high price, low price, and trading volume as explanatory variables, with the adjusted closing price as a comparison variable. Python is employed for data preparation, descriptive statistics, visualization, correlation analysis, and linear regression modeling. The literature review draws on thirty-one studies and shows that accounting information, financial ratios, and market indicators are commonly linked to share prices, although their explanatory strength varies across markets, industries, and methods. The empirical results show that same-day price variables are strongly associated with closing price, whereas trading volume has a weaker direct relationship with price levels. The model therefore provides evidence of same-day price explanation rather than a complete future-forecasting system. The study provides a structured framework for integrating accounting-performance concepts with Python-based market analysis and identifies the accounting data required for a more comprehensive journal-level model.

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Vol. 5 No. 1 (2026)

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Articles
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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