A Framework for Measuring How News Topics Drive Stock Movement

2025-10-08

AI Summary

Existing studies often reduce news sentiment to a single scalar value, neglecting thematic heterogeneity and thus failing to capture differential market impacts across news content. This paper proposes a topic-driven framework for analyzing stock price impacts: first, fine-grained news topics are identified via semantic embeddings of headlines generated by a pretrained sentence Transformer, followed by K-means clustering; second, a topic exposure metric is constructed and used in ordinary least squares regressions to estimate the predictive effect of each topic on daily stock returns. Empirical analysis on Apple Inc. data reveals that specific topics—such as product launches and regulatory investigations—significantly predict next-day positive or negative returns, whereas others exhibit no statistically significant effects. The framework transcends conventional sentiment aggregation paradigms by enabling quantifiable, distinguishable, and empirically verifiable analysis at the news topic level, thereby establishing a novel methodological foundation for improving stock return prediction accuracy and advancing understanding of information diffusion mechanisms.

Technology Category

• Natural Language Processing: Information Extraction
• Data Mining & Knowledge Management: Semantic Web
• Machine Learning: Learning Preferences or Rankings

Application Category

• Web Mining and Content Analysis: Topic discovery and tracking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

In modern financial markets, news plays a critical role in shaping investor sentiment and influencing stock price movements. However, most existing studies aggregate daily news sentiment into a single score, potentially overlooking important variations in topic content and relevance. This simplification may mask nuanced relationships between specific news themes and market responses. To address this gap, this paper proposes a novel framework to examine how different news topics influence stock price movements. The framework encodes individual news headlines into dense semantic embeddings using a pretrained sentence transformer, then applies K-means clustering to identify distinct news topics. Topic exposures are incorporated as explanatory variables in an ordinary least squares regression to quantify their impact on daily stock returns. Applied to Apple Inc., the framework reveals that certain topics are significantly associated with positive or negative next-day returns, while others have no measurable effect. These findings highlight the importance of topic-level analysis in understanding the relationship between news content and financial markets. The proposed framework provides a scalable approach for both researchers and practitioners to assess the informational value of different news topics and suggests a promising direction for improving predictive models of stock price movement.

Problem

• Measuring how specific news topics influence stock price movements
• Addressing limitations of aggregated daily news sentiment analysis
• Quantifying topic-level impacts on financial market returns

Innovation

• Uses sentence transformer for semantic news encoding
• Applies K-means clustering to identify news topics
• Incorporates topic exposures in regression analysis