Design of an Intelligent Predictive Framework for Financial Value Chain Dynamics and Stock Performance in Energy-Intensive Industries under Energy Price Fluctuations

Document Type : Original Article

Authors

1 Department of Industrial Management, AK.C., Islamic Azad University, Aliabad Katoul, Iran

2 Department of Management, AK.C., Islamic Azad University, Aliabad Katoul, Iran

3 Department of Industrial Engineering, AK.C., Islamic Azad University, Aliabad Katoul, Iran

4 Department of Mathematics and Statistics, AK.C., Islamic Azad University, Aliabad Katoul, Iran

10.22091/jdaid.2026.16733.1095

Abstract

Purpose: The industrial sector, particularly the cement industry, is highly energy-intensive, with Iran's consumption reaching 1.31 times the global average. Energy price liberalization and subsidy reforms have challenged resource management in this sector. This study aims to design an intelligent decision support system (DSS) to evaluate and predict the sustainable financial value chain of cement companies under energy price fluctuations.
Design/methodology/approach: This research integrates fundamental management principles with advanced technical components. Using time-series data mining and a sequential artificial neural network (ANN) architecture equipped with an advanced optimizer, the study models the hidden patterns of electricity and gas price fluctuations on corporate financial statements. KMO and Bartlett’s tests were employed to confirm sampling adequacy, while RSI and MACD indicators were adapted to capture price momentum and trends.
Findings: The findings reveal that changes in energy carrier prices directly affect industry returns and financial sustainability by altering price momentum and trend indicators. The proposed intelligent model demonstrated high accuracy in predicting the production chain's financial behavior, achieving a significantly reduced Mean Squared Error (MSE) and an exceptionally high coefficient of determination.
Originality/value: This study contributes to the literature by bridging technical data mining techniques with financial sustainability management. It offers a novel, highly accurate predictive framework specifically tailored for evaluating the financial resilience of heavy industries facing energy policy shifts.

Keywords

Main Subjects

References

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Journal of Data Analytics and Intelligent Decision-making

Articles in Press, Accepted Manuscript
Available Online from 30 September 2026

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