مجلة إدارة الأعمال والدراسات الاقتصادية
Volume 12, Numéro 1, Pages 029-056
2026-06-02
Machine Learning As A Structural Driver Of Investment Enhancement: Evidence From The United States Innovation Economy (2000-2025)
Authors : Ghebouli Ahmed . Mechri Sara .
Abstract
This study examines the role of machine learning (ML) as a structural driver of investment enhancement within the United States innovation economy over the period 2000–2025. The research addresses the central question of whether the systematic integration of ML technologies can improve investment efficiency, strengthen market resilience, and reinforce long-term economic competitiveness. Methodologically, the study adopts a mixed analytical approach combining descriptive analysis, correlation and regression techniques, and scenario-based simulations, drawing on macroeconomic indicators, financial market data, innovation metrics, and institutional governance indices from internationally recognized databases. The primary objective is to bridge investment economics and artificial intelligence by conceptualizing ML as an endogenous growth factor embedded within modern investment ecosystems. The findings provide robust empirical evidence that ML adoption significantly enhances capital allocation efficiency, reduces information asymmetry, mitigates market volatility, and stimulates venture capital and innovation-driven investment flows. Moreover, the results highlight the critical mediating role of institutional quality, demonstrating that ML deployment in public administration improves regulatory effectiveness and investor confidence. Overall, the study concludes that machine learning constitutes a systemic investment enabler, reshaping the foundations of investment decision-making, innovation financing, and economic performance in advanced economies.
Keywords
Machine Learning ; Investment Efficiency ; Innovation Economy ; Capital Allocation ; United States
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