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Large language models (LLMs) are deep learning algorithms being used toperform natural language processing tasks in various fields, from socialsciences to finance and biomedical sciences. Developing and training a new LLMcan be very computationally expensive, so it is becoming a common practice totake existing LLMs and finetune them with carefully curated datasets fordesired applications in different fields. Here, we present BioFinBERT, afinetuned LLM to perform financial sentiment analysis of public text associatedwith stocks of companies in the biotechnology sector. The stocks of biotechcompanies developing highly innovative and risky therapeutic drugs tend torespond very positively or negatively upon a successful or failed clinicalreadout or regulatory approval of their drug, respectively. These clinical orregulatory results are disclosed by the biotech companies via press releases,which are followed by a significant stock response in many cases. In ourattempt to design a LLM capable of analyzing the sentiment of these pressreleases,we first finetuned BioBERT, a biomedical language representation modeldesigned for biomedical text mining, using financial textual databases. Ourfinetuned model, termed BioFinBERT, was then used to perform financialsentiment analysis of various biotech-related press releases and financial textaround inflection points that significantly affected the price of biotechstocks.

BioFinBERT: Finetuning Large Language Models (LLMs) to Analyze Sentiment  of Press Releases and Financial Text Around Inflection Points of Biotech  Stocks