Libro

Descripción

From changes in the interest rate to the hijacking of a commercial aircraft, there are many events that may result in a major shift in airline stock performance. We measure the impact of US airlines’ stock performance following aviation related news announcements on 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. The amount of such text data and documents is expected to be enormous. We use a natural language processing, Latent Dirichlet Allocation (LDA), to investigate and search for patterns that can explain the movements of US airline stocks. 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 certain topics mentioned in aviation voice news releases. Finally, we investigate the links between stock returns and the topics identified. Our findings show that financial performance varies significantly across topics. Topics related to technology, fuel, and training positively affect the US airline stocks in the short and long terms moving average, while defense and travel cost-related topics only affect the medium-term run.