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For equity analysts, navigating the large amount of financial news is a major barrier, making it hard to swiftly extract significant information. The all-mpnet-base-v2 version of the Sentence Transformer model, which converts text into semantic embeddings for improved content comprehension and retrieval, is the basis for this intelligent news extraction tool. In order to provide quick access to pertinent information, the tool incorporates Streamlit for an intuitive user interface, BeautifulSoup for web scraping, and Pickle for effective data storage. It improves the efficiency of decision-making by automating the extraction and analysis of financial news by comparing words’ relevance to user queries using cosine similarity. The time analysts spend analyzing news is significantly decreased by this tool, which also offers current insights. It has room for improvement, including machine learning improvements to improve relevance ranking and broaden its applicability to larger financial datasets.

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Automating Equity Research: An End-To-End News Analysis Tool Using Sentence Transformers

  • 会议论文

  • 发表日期：

    2026年

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  • Nadar Akila P. Mohan,
  • K. Michael Mahesh,
  • R. Vijay,
  • N. Sowmiya,
  • M. Ragul

摘要

For equity analysts, navigating the large amount of financial news is a major barrier, making it hard to swiftly extract significant information. The all-mpnet-base-v2 version of the Sentence Transformer model, which converts text into semantic embeddings for improved content comprehension and retrieval, is the basis for this intelligent news extraction tool. In order to provide quick access to pertinent information, the tool incorporates Streamlit for an intuitive user interface, BeautifulSoup for web scraping, and Pickle for effective data storage. It improves the efficiency of decision-making by automating the extraction and analysis of financial news by comparing words’ relevance to user queries using cosine similarity. The time analysts spend analyzing news is significantly decreased by this tool, which also offers current insights. It has room for improvement, including machine learning improvements to improve relevance ranking and broaden its applicability to larger financial datasets.