The Role of News and News Sentiment in Systematic Risk Dynamics and Asset Pricing: A Systematic Review
Keywords:
news sentiment, asset pricing, systematic risk, behavioral finance, textual analysis, natural language processingAbstract
This systematic review study was conducted with the aim of examining the role of news and news sentiment in the dynamics of systematic risk and asset pricing processes. Evidence reviewed from articles published between 2000 and 2025 indicates that investor sentiment—particularly negative sentiment—intensifies systematic risk by increasing volatility and correlations among assets, whereas positive sentiment can have a mitigating effect. These findings suggest that the qualitative information embedded in financial news exerts significant effects on asset pricing beyond the predictions of classical efficient market models. Recent advances in natural language processing and machine learning, especially transformer-based architectures, have enhanced the accuracy of sentiment analysis and improved the predictive capacity and modeling of market risk. Nevertheless, challenges such as investor base heterogeneity, linguistic and cultural differences in expressing sentiment, and the necessity of integrating real-time multi-source data (news, social media, and macroeconomic indicators) continue to constrain research. Overall, the findings reveal that financial markets are neither fully efficient nor entirely irrational but rather complex adaptive systems in which information processing, behavioral biases, and arbitrage forces collectively influence the formation of prices and risks. News sentiment analysis, therefore, provides a valuable tool for enriching financial theories and strengthening practical policies toward market stability and efficiency, a point that should not be overlooked.
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Copyright (c) 2025 Zohreh Memarpour, Gholamreza Askarzadeh Dareh, Hamid Khajeh Mahmoudabadi, Seyed Yahya Abtahi (Author)
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