Article
STUDY OF CAPITAL MANAGEMENT
Capital management is central to ensuring a company’s long-term solvency, growth, and ability to maximize shareholder value. It involves balancing equity and debt financing, maintaining optimal liquidity, and making strategic decisions regarding asset utilization and retained earnings. Traditionally, capital management analysis relies on financial ratios, trend analysis, and static models. However, such methods often fail to capture complex, dynamic interactions among internal financial policies, market conditions, and macroeconomic changes.This study begins with a comprehensive review of capital structure, working capital policy, and dividend decisions to assess their collective impact on firm performance. To enhance the analysis, Machine Learning (ML) techniques like Random Forest and XGBoost are used to predict capital adequacy and optimal debt-equity mix, while Deep Learning (DL) models like LSTM networks forecast future capital requirements based on historical financial data and market trends. By integrating these AI-driven approaches, the study reveals nonlinear dependencies and patterns overlooked by traditional models, offering richer, more actionable insights for managers and investors alike.
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