Text mining of stocktwits data for predicting stock prices

Mukul Jaggi*, Priyanka Mandal, Shreya Narang, Usman Naseem, Matloob Khushi*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)
28 Downloads (Pure)

Abstract

Stock price prediction can be made more efficient by considering the price fluctuations and understanding people’s sentiments. A limited number of models understand financial jargon or have labelled datasets concerning stock price change. To overcome this challenge, we introduced FinALBERT, an ALBERT based model trained to handle financial domain text classification tasks by labelling Stocktwits text data based on stock price change. We collected Stocktwits data for over ten years for 25 different companies, including the major five FAANG (Facebook, Amazon, Apple, Netflix, Google). These datasets were labelled with three labelling techniques based on stock price changes. Our proposed model FinALBERT is fine-tuned with these labels to achieve optimal results. We experimented with the labelled dataset by training it on traditional machine learning, BERT, and FinBERT models, which helped us understand how these labels behaved with different model architectures. Our labelling method’s competitive advantage is that it can help analyse the historical data effectively, and the mathematical function can be easily customised to predict stock movement.

Original languageEnglish
Article number13
Pages (from-to)1-22
Number of pages22
JournalApplied System Innovation
Volume4
Issue number1
DOIs
Publication statusPublished - Mar 2021
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2021. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • BERT
  • FinBERT
  • ALBERT
  • NLP
  • StockTwits
  • FinALBERT
  • FAANG
  • transformer
  • pretraining
  • fine-tuning

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