AI-Driven Dividend Signaling Theory: A Conceptual Framework for Computational Corporate Finance

Abstract

Purpose: The paper attempts to develop a new theoretical model of AI-Driven Dividend Signaling Theory for understanding the integration of artificial intelligence technology into the classical signaling theory in providing significantly effective corporate payout decisions. The paper helps to explore the theoretical gap between traditional theory and algorithmic corporate finance.

Design/Methodology/Approach: Using a theoretical modeling approach, the study combines the classical signaling theory with artificial intelligence in decision-making processes through mathematical representations. This exhibits how machine learning techniques work on data variability dynamics, signaling expenses, and market analysis methods.

Findings: Three signaling levels are identified, namely, improved data processing, dynamic signal optimization, and computational market engagement from the theoretical framework where traditional signaling approaches and market interpretation transform at each level.

Originality/Value: This paper is an introductory theoretical model intended for the AI-driven dividend policy in algorithmic corporate finance and also proposes testable hypotheses for further experiential studies.