Quantifying News Sentiment During the COVID-19 Crisis: A Zero-Shot Classification Approach and Its Impact on U.S. Household Stock Market Expectations

Additional Metadata
Keywords	zero-shot classification; sentiment analysis; large language models; natural language processing; covid-19; news sentiment; household stock market expectations
Thesis Advisor	Lumsdaine, RL
Persistent URL	hdl.handle.net/2105/73341
Series	Econometrie
Organisation	Erasmus School of Economics
Citation APA Style AAA Style APA Style Cell Style Chicago Style Harvard Style IEEE Style MLA Style Nature Style Vancouver Style American-Institute-of-Physics Style Council-of-Science-Editors Style BibTex Format Endnote Format RIS Format CSL Format DOIs only Format	Sin, M.J. (2024, September 17). Quantifying News Sentiment During the COVID-19 Crisis: A Zero-Shot Classification Approach and Its Impact on U.S. Household Stock Market Expectations. Econometrie. Retrieved from http://hdl.handle.net/2105/73341

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