Is this project an undergraduate, graduate, or faculty project?
Graduate
Project Type
individual
Campus
Daytona Beach
Authors' Class Standing
Graduate Student
Lead Presenter's Name
Amina Issoufou Anaroua
Lead Presenter's College
DB College of Business
Faculty Mentor Name
Dr. Hari Adhikari
Abstract
The primary objective is to measure the impact of US airlines’ stock performance following aviation related news announcements of differing topics. Our data account for aviation news of airlines, airports, regulations, safety, accidents, manufacturers, MRO, incidents, aviation training, general aviation, and others from Aviation Voice. We use a natural language processing, Latent Dirichlet Allocation (LDA) to investigate and search for patterns that can explain the movement of US airline stock. First, we mine the aviation related data through text mining and topic modeling. Second, we employ the LDA model approach to help us identify and capture the extent of certain topics mentioned in aviation voice news releases. Finally, we use multiple regression models to investigate stock price reactions to news announcements. Eventually, we succeed in extracting 10 topics. As hypothesized, the impact of those topics varies greatly from topic to topic. Some of the topics have effect on the US Airline stocks in the short and long terms moving average while other topics have only effect on the medium-term run.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
Yes, Student Internal Grants
Analysis of US Airline Stocks performance using Latent Dirichlet Allocation (LDA)
The primary objective is to measure the impact of US airlines’ stock performance following aviation related news announcements of differing topics. Our data account for aviation news of airlines, airports, regulations, safety, accidents, manufacturers, MRO, incidents, aviation training, general aviation, and others from Aviation Voice. We use a natural language processing, Latent Dirichlet Allocation (LDA) to investigate and search for patterns that can explain the movement of US airline stock. First, we mine the aviation related data through text mining and topic modeling. Second, we employ the LDA model approach to help us identify and capture the extent of certain topics mentioned in aviation voice news releases. Finally, we use multiple regression models to investigate stock price reactions to news announcements. Eventually, we succeed in extracting 10 topics. As hypothesized, the impact of those topics varies greatly from topic to topic. Some of the topics have effect on the US Airline stocks in the short and long terms moving average while other topics have only effect on the medium-term run.