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Text Mining of Stocktwits Data for Predicting Stock Prices

Author

Listed:
  • Mukul Jaggi
  • Priyanka Mandal
  • Shreya Narang
  • Usman Naseem
  • Matloob Khushi

Abstract

Stock price prediction can be made more efficient by considering the price fluctuations and understanding the sentiments of people. 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 competitive advantage is that it can help analyse the historical data effectively, and the mathematical function can be easily customised to predict stock movement.

Suggested Citation

  • Mukul Jaggi & Priyanka Mandal & Shreya Narang & Usman Naseem & Matloob Khushi, 2021. "Text Mining of Stocktwits Data for Predicting Stock Prices," Papers 2103.16388, arXiv.org.
    • Handle: RePEc:arx:papers:2103.16388
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    File URL: http://arxiv.org/pdf/2103.16388
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    References listed on IDEAS

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    1. Jaideep Singh & Matloob Khushi, 2021. "Feature Learning for Stock Price Prediction Shows a Significant Role of Analyst Rating," Papers 2103.09106, arXiv.org.
    2. Zezheng Zhang & Matloob Khushi, 2020. "GA-MSSR: Genetic Algorithm Maximizing Sharpe and Sterling Ratio Method for RoboTrading," Papers 2008.09471, arXiv.org.
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    Cited by:

    1. Ruyi Tao & Kaiwei Liu & Xu Jing & Jiang Zhang, 2024. "Predicting Company Growth by Econophysics informed Machine Learning," Papers 2410.17587, arXiv.org.
    2. Cynthia Pagliaro & Dhagash Mehta & Han-Tai Shiao & Shaofei Wang & Luwei Xiong, 2021. "Investor Behavior Modeling by Analyzing Financial Advisor Notes: A Machine Learning Perspective," Papers 2107.05592, arXiv.org.
    3. Yanzhao Zou & Dorien Herremans, 2022. "PreBit -- A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin," Papers 2206.00648, arXiv.org, revised Oct 2023.
    4. Mimansa Rana & Nanxiang Mao & Ming Ao & Xiaohui Wu & Poning Liang & Matloob Khushi, 2021. "Clustering and attention model based for intelligent trading," Papers 2107.06782, arXiv.org, revised Aug 2021.
    5. Rick Steinert & Saskia Altmann, 2023. "Linking microblogging sentiments to stock price movement: An application of GPT-4," Papers 2308.16771, arXiv.org.
    6. Yunze Li & Yanan Xie & Chen Yu & Fangxing Yu & Bo Jiang & Matloob Khushi, 2021. "Feature importance recap and stacking models for forex price prediction," Papers 2107.14092, arXiv.org.
    7. Christopher Wimmer & Navid Rekabsaz, 2023. "Leveraging Vision-Language Models for Granular Market Change Prediction," Papers 2301.10166, arXiv.org.

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