Dynamics of Nonlinear Causality: Exploring the Influence of Positive and Negative Financial News on the Indian Equity Market

dc.contributor.authorVarghese, R.R.
dc.contributor.authorMohan, B.R.
dc.date.accessioned2026-02-06T06:34:32Z
dc.date.issued2023
dc.description.abstractRecent attention has focused on the interplay between news media and stock prices, prompted by extensive exploration of stock market dynamics. This study is designed to examine the existence of non-linear causal links between positive and negative financial news and stock market valuations. Employing sentiment analysis, the Finbert model evaluates news content, while the Transfer Entropy method assesses the impact of both positive financial news and negative news. Investigating the causal relationships between fluctuations in positive and negative news and stock price performance across diverse companies through transfer entropy analysis, our findings confirm the evident disparity in the influence of positive and negative news on daily stock prices. Quantification of these effects adopts the Sliding Window approach. Furthermore, our evaluations indicate that negative financial news exerts a more significant influence on stock prices compared to positive financial news. These outcomes bolster the concept of an asymmetric effect, wherein negative sentiment wields a more substantial influence compared to its positive counterpart. © 2023 IEEE.
dc.identifier.citation2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023, 2023, Vol., , p. -
dc.identifier.urihttps://doi.org/10.1109/AICERA/ICIS59538.2023.10420348
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/29305
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectFinancial News
dc.subjectFinbert
dc.subjectNon linear Causal linkage
dc.subjectTransfer Entropy
dc.titleDynamics of Nonlinear Causality: Exploring the Influence of Positive and Negative Financial News on the Indian Equity Market

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