Evaluating the Impact of Financial Policy News on Market Responses Using Natural Language Processing
DOI:
https://doi.org/10.71222/dr58vx16Keywords:
natural language processing, financial policy, sentiment analysis, market prediction, textual features, policy uncertaintyAbstract
This study presents a practical and replicable framework for analyzing the impact of financial policy news on market behavior by integrating natural language processing (NLP) techniques with market prediction models. By extracting sentiment scores, sentiment volatility, news volume, topic intensity, and policy uncertainty indicators from policy texts, the research transforms unstructured information into quantitative features that help explain market returns and volatility. Using simulated yet realistic data, models such as linear regression, random forest, and LSTM are employed to evaluate the predictive value of these features, showing that NLP-derived indicators enhance the forecasting of market responses. While the study confirms the usefulness of textual features in capturing policy-driven market dynamics, it also acknowledges limitations related to data scale and model generalizability. The findings offer methodological insights that may support investors, financial institutions, and policymakers in interpreting and anticipating market behavior under policy influence.
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