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The popularity of news analytics in finance and economics is attributed to real‐time data, sentiment analysis, and automated decision‐making. Although there is an increasing body of research on financial news analytics, a quantitative bibliometric and framework‐based assessment is absent. This hybrid systematic review analyses the intellectual landscape and development of news analytics in financial markets via bibliometric analysis (performance trends, co‐authorship analysis, and keyword co‐occurrence mapping) and the Theories‐Context‐Characteristics‐Methods (TCCM) framework. We recognize substantial academic achievements, pivotal concepts, prevailing research trajectories, and methodological innovations from a selection of peer‐reviewed journal articles spanning two decades. Key subject clusters include the impact of scheduled and unscheduled news on market volatility, the utilization of AI‐driven sentiment research in investment strategies, and the relationship between news sentiment and macroeconomic indicators. We further delineate theoretical underpinnings, empirical contexts, methodological tendencies, and unexamined research domains. Our study provides scholars and professionals with a comprehensive synthesis of financial news analytics, directing future inquiries and methodological progress in high‐frequency data analysis, machine learning integration, and market behavior modeling.
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Pal et al. (2025) studied this question.
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