Hanging on Every Word: Natural Language Processing Unlocks New Frontier in Corporate Earnings Sentiment Analysis
Given the growing interest in Natural Language Processing (NLP) among investors, S&P Global has published a primer to demystify many aspects of NLP and provide three illustrations, with accompanying Python code, of how NLP can be used to quantify the sentiment of earnings calls. In the first example, sector-level sentiment trends are generated providing insights around inflection points and accelerations. The other two illustrations are: i) stock-level sentiment changes and forward returns, and ii) language complexity of earnings calls.
In this presentation by Dave Pope, Managing Director of Quantamental Research, the following questions will be answered:
What is NLP? We demystify common NLP terms and provide an overview of general steps in NLP.
Why is NLP important? Forty zettabytes (10^21 bytes) of data are projected to be on the internet by 2020, out of which more than eighty percent of the data are unstructured in nature, requiring NLP to process and understand.
How can NLP help me? We derive insights from earnings call transcripts via NLP measuring industry-level sentiment trends or language complexity of earnings calls, and much more.
Where do I start? We will provide code for each use case, enabling users to replicate the sentiment analysis.