Interest continues to grow in harnessing unstructured data to identify differentiated sources of alpha. During this webinar, our speakers will explore a number of sentiment- and behavioral-based signals that have historically demonstrated stock selection power.
Topics will include:
- A review of natural language processing (NLP) fundamentals, including the importance of dictionary selection
- Analysis of stock selection using sentiment- and behavioral-based signals from earnings call transcripts
- The potential for faster analysis of historical data through the addition of metadata tagging to unstructured, textual data
- How the integration of unstructured sources of data with traditional data sets can produce in-depth analysis
- Examples of flexible delivery options to receive alternative data sources