Application of Clustering Algorithm in Corporate Strategy and Risk.

Area of Science:

• Corporate Strategy
• Risk Management
• Economic Analysis

Background:

• Market mechanisms and corporate strategy theory drive diversification in listed companies.
• Diversification significantly impacts economic development speed, technological advancement, and internationalization.
• Enterprise strategies are inherently linked to long-term development and are influenced by risk control.

Purpose of the Study:

• To address the challenge of managing strategic risks associated with corporate diversification.
• To classify diversification strategic risks and analyze their underlying causes.
• To evaluate the effectiveness of management and control systems in strategic risk assessment.

Main Methods:

• Introduction of a clustering algorithm for strategic risk classification.
• In-depth case study analysis of an enterprise's diversification development.
• Examination of the company's situation, strategy, challenges, and behavior.

Main Results:

• The study successfully classifies diversification strategic risks and identifies their causes.
• Analysis revealed high-risk financial leverage coefficients (24.64, 3.93, 104.71, 6.59) across four quarters.
• The clustering algorithm accurately judged the company's strategic risk status.

Conclusions:

• Strategic risk management is crucial for evaluating corporate management and control systems.
• The applied clustering algorithm effectively identifies and assesses strategic risks in diversification.
• The case study demonstrates the practical application of the methodology in determining high-risk states.