Text Summarization and Financial Sentiment Evaluation from SEC filling

Challenges.

• Inefficient use of valuable analyst time. 
• Increased risk of missing crucial financial information. 
• Difficulty in uncovering potential opportunities or hidden risks. 

Solution.

• Optical Character Recognition (OCR) technology converts scanned PDFs into machine-readable text. For HTML documents, the solution extracts structured content like tables and text sections. 
• NLP algorithms analyze the extracted text and identify key points relevant to investors. These algorithms use techniques like summarization to create concise summaries that capture the essence of the financial document. 
• Industry standard Financial sentiment analysis is done using multiple years data. Learning mechanisms were developed. This allows for identifying trends in sentiment over time and provides a broader context for current financial performance. 

The Impact.

• Pessimism Factor Analysis – Stock Prices prediction accuracy improved by 11% 
• Speculative Language helped in identifying and addressing potential risks underlying the statements. 
• Overall manual effort saving of more than 70% with added benefit of getting better understanding of data