PT  - JOURNAL ARTICLE
AU  - Di-Jia Su
AU  - John M. Mulvey
AU  - H. Vincent Poor
TI  - Improving Portfolio Performance via Natural Language Processing Methods
AID  - 10.3905/jfds.2022.1.088
DP  - 2022 Apr 30
TA  - The Journal of Financial Data Science
PG  - 37--49
VI  - 4
IP  - 2
4099  - https://pm-research.com/content/4/2/37.short
4100  - https://pm-research.com/content/4/2/37.full
AB  - Recent natural language processing (NLP) breakthroughs have proven effective for addressing many language-directed tasks, such as completing sentences and addressing search queries. This technology has been successfully implemented by tech firms including Google and others. An important element consists of language embeddings linked to pretraining systems. This article describes NLP concepts and their application to portfolio models via a modern version of sentiment analysis. The authors demonstrate the advantages of employing information from Twitter along with the NLP for constructing a portfolio of stocks, especially during unusual events such as the COVID-19 pandemic.