Quantitative Management Associates: A Practical Approach to Advanced Text Mining in Finance

Quantifying the Significance of NLP Text Mining in Finance

A recent study from Quantitative Management Associates applied Amenity Analytics’ NLP platform to quantify the impact of using unstructured data analysis in Finance. To conduct their research the authors applied our text analytics methodology which scores earnings call transcripts based on sentiment extracted from financial events identified within the text.

The research detailed how the application of our NLP model to earnings call transcripts yielded three statistically significant results:

  1. 1. It produced a signal that is incrementally additive to earnings surprises and the short-term returns around the earnings announcement
  2. 2. The change in sentiment has a relatively low correlation with both earnings surprises and the short-term returns; in other words, the signal is a sufficiently different potential source of information
  3. 3. Using Amenity Analytics added 26 bps of alpha per quarter

These findings indicate how text analytics adds a meaningful layer of insight for investors that can’t be obtained from structured data.

Download This White Paper

To obtain a copy of this research you can visit the Journal of Financial Data Science for the Volume 1, Issue 1, Winter 2019 publication: A Practical Approach to Advanced Text Mining in Finance.

Please note: This in no way represents investment advice. All transcript text provided by S&P Global Market Intelligence. Analysis provided in collaboration with Quantitative Management Associates.

Copyright ©2019 Amenity Analytics. 

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