Authors: Research Scholar Jyoti Wadhwani, Associate Professor Dr. Uttam Kumar Jha
Abstract: Corporate valuation and dividend policy analysis are essential components of financial management and investment decision-making, particularly in the Fast-Moving Consumer Goods (FMCG) sector, where stable financial performance, investor confidence and market competitiveness play a critical role. The rapid advancement of machine learning and predictive financial analytics has transformed traditional valuation methods into intelligent data-driven decision-support systems. This study proposes a machine learning framework for corporate valuation and dividend policy analysis in FMCG companies that integrates financial indicators, market variables, corporate governance factors and dividend-related analytics into a unified predictive structure. The framework incorporates profitability, liquidity, solvency, ownership structure, dividend payout ratios, market volatility and macroeconomic indicators to improve firm valuation forecasting and investment analysis, and applies regression models, random forests, gradient boosting, neural networks and ensemble forecasting systems to analyse complex financial relationships and improve predictive accuracy. Twelve recent studies published between 2019 and 2026 are reviewed and organised into three themes covering market-facing dividend effects, governance and financial determinants, and artificial intelligence in financial prediction. The reviewed evidence is consolidated into comparative tables that map each study to its context, focus, principal finding and contribution to the proposed design, and eight research gaps are identified and mapped to corresponding research directions. The findings indicate that machine learning-based predictive financial analytics can enhance corporate valuation reliability, optimise dividend decision-making and support strategic investment planning in the FMCG sector.
