Industry Superstars: Unmasking Key Features that Drive Firm-Level Performance in Chinese Markets Using Ensemble Learning with Genetic Algorithm
Date
2024-08
Authors
Rostami, Omid
Major Professor
Mirka, Gary
Advisor
Committee Member
Hu
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Abstract
This study presents a comprehensive analysis of firm-level performance within five distinct industries, utilizing data from the Chinese Industrial Enterprises Database covering the years 2002-2007. This study aims to unravel the dynamics that govern market share in given industries, focusing on identifying key features that make a firm a “Superstar” in that industry. We designed an ensemble machine learning algorithm with Random Forest, XGBoost, AdaBoost, and least absolute shrinkage and selection operator (LASSO) as the base learners coupled with a Genetic Algorithm (GA) for the optimal aggregation. We evaluated the sequential interplay of features influencing market share, allowing us to capture the relationships within these industries and highlighting the heterogeneity and industry-specific factors that shape market leadership. Our findings reveal that "Last year's market share" consistently emerges as a significant predictor across all industries, underscoring the impact of historical performance on future market trajectory. However, the importance of other factors, such as "net total fixed assets" and "main business revenue," varies across industries. This study contributes to the academic understanding of market dynamics and offers practical insights for policymakers and business strategists, emphasizing the need for industry-specific approaches in decision-making.
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creative component
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CC0 1.0 Universal
Copyright
2024