RT Journal Article
SR Electronic
T1 A Practical Approach to Advanced Text Mining in Finance
JF The Journal of Financial Data Science
FD Institutional Investor Journals
SP 122
OP 129
DO 10.3905/jfds.2019.1.1.122
VO 1
IS 1
A1 Julia Klevak
A1 Joshua Livnat
A1 Kate Suslava
YR 2019
UL https://pm-research.com/content/1/1/122.abstract
AB The purpose of the study is to illustrate one application of unstructured data analysis in finance: the scoring of a text document based on its tone (sentiment) and specific events that are important for the end user. The methodology begins with the well-known practice of counting positive and negative words and progresses to illustrate the construction of relevant events. The authors show how the application of this methodology to the analysis of earnings conference call transcripts produces a signal that is incrementally additive to earnings surprises and the short-term returns around the earnings announcement. An interesting feature of the tone change extracted from the conference calls is that it has a relatively low correlation with both earnings surprises and the short-term return around the earnings announcement. This indicates how use of text mining and scoring of unstructured data can add information to investors beyond structured data.TOPICS: Big data/machine learning, fundamental equity analysis