International Journal of Science, Engineering and TechnologyAuthors: Research Scholar Jyoti Wadhwani, Associate Professor Dr. Uttam Kumar Jha
Abstract: Corporate valuation is central to financial management and investment analysis, and it is especially consequential in the Fast-Moving Consumer Goods (FMCG) sector, where stable demand, recurring cash flows and sustained investor confidence make firm value highly sensitive to distribution decisions. Dividend policy is repeatedly identified in the literature as a determinant of shareholder wealth, stock price behaviour, profitability signalling and investment attractiveness, yet the evidence remains fragmented across variables, markets and estimation methods. This paper presents a structured review of contemporary research on the dividend policy-corporate valuation relationship and on the emerging use of predictive analytics in valuation modelling. Twelve recent primary studies published between 2025 and 2026 were identified, screened and classified into four themes: dividend policy and corporate financial performance; dividend policy, stock market behaviour and investor response; capital structure, ownership and governance effects; and ESG, macroeconomic and analytics-driven valuation. Each study is compared on context, the structural role assigned to dividend policy, reported findings and methodological limitations. The synthesis shows that dividend policy operates in three distinct structural roles across the literature-as a direct determinant, as a moderator and as a mediator-and that this inconsistency is a substantive source of conflicting results. It further shows that the reviewed studies rely almost exclusively on conventional regression and structural equation techniques, that ESG and macroeconomic variables are treated in isolation, and that the FMCG sector itself is under-examined despite its dividend-intensive profile. Drawing on these findings, the review derives seven research gaps and proposes an integrated conceptual framework in which financial performance, capital structure, ownership, market and ESG determinants feed a dividend policy core, which in turn feeds a predictive analytics layer combining regression, machine learning, time-series forecasting and explainable AI to generate valuation forecasts and decision-support outputs. The framework is offered as a research agenda rather than an estimated model, and the review concludes with prioritised directions for its empirical validation.
