Quantifying Strategic Ambiguity in Corporate Language for AI-Driven Trading Strategies

Vaisnavi Nemala

Keywords: AI in Finance; trading; NLP; sentiment analysis

TL;DR: We introduce SAS, an AI model that measures deliberate ambiguity in corporate language to extract actionable insights and predict stock movements beyond standard sentiment analysis in AI-driven trading.

Abstract: This paper introduces a novel AI-driven approach for extracting actionable insights from corporate communications by quantifying strategic ambiguity in language. While prior work in natural language analysis has largely focused on sentiment or factual content, we explore how organizations deliberately hedge, obscure, or soften information, using linguistic ambiguity as a rich signal of intent and hidden meaning. We propose the Strategic Ambiguity Score (SAS), a machine learning model that captures deliberate vagueness by integrating hedge frequency, negation patterns, and model-based attention to critical phrases. Unlike traditional sentiment models, SAS measures not just what is said, but how and where uncertainty is strategically embedded within the text. We demonstrate that SAS can effectively highlight subtle signals that correlate with subsequent outcomes, and we illustrate its utility through predictive analyses in corporate disclosures. By shifting the focus from simple sentiment interpretation to ambiguity detection, this work provides a generalizable framework for AI applications in decision-making, risk assessment, and strategic communication analysis across diverse domains.

Submission Number: 53