Improving Portfolio Performance via Natural Language Processing Methods

Di Jia Su, John M. Mulvey, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

Abstract

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 ar ticle describes NLP concepts and their application to por tfolio 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.

Original languageEnglish (US)
Pages (from-to)37-49
Number of pages13
JournalJournal of Financial Data Science
Volume4
Issue number2
DOIs
StatePublished - Mar 1 2022

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Information Systems
  • Finance
  • Business and International Management
  • Strategy and Management
  • Business, Management and Accounting (miscellaneous)
  • Information Systems and Management

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