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Support for Stock Trend Prediction Using Transformers and Sentiment Analysis

Author

Listed:
  • Harsimrat Kaeley

    (University of California, Irvine)

  • Ye QIAO

    (University of California, Irvine)

  • Nader BAGHERZADEH

    (University of California, Irvine)

Abstract

Stock trend analysis has been an influential time-series prediction topic due to its lucrative and inherently chaotic nature. Many models looking to accurately predict the trend of stocks have been based on Recurrent Neural Networks (RNNs). However, due to the limitations of RNNs, such as gradient vanish and long-term dependencies being lost as sequence length increases, in this paper we develop a Transformer based model that uses technical stock data and sentiment analysis to conduct accurate stock trend prediction over long time windows. This paper also introduces a novel dataset containing daily technical stock data and top news headline data spanning almost three years. Stock prediction based solely on technical data can suffer from lag caused by the inability of stock indicators to effectively factor in breaking market news. The use of sentiment analysis on top headlines can help account for unforeseen shifts in market conditions caused by news coverage. We measure the performance of our model against RNNs over sequence lengths spanning 5 business days to 30 business days to mimic different length trading strategies. This reveals an improvement in directional accuracy over RNNs as sequence length is increased, with the largest improvement being close to 18.63% at 30 business days.

Suggested Citation

  • Harsimrat Kaeley & Ye QIAO & Nader BAGHERZADEH, 0000. "Support for Stock Trend Prediction Using Transformers and Sentiment Analysis," Proceedings of Economics and Finance Conferences 13815878, International Institute of Social and Economic Sciences.
    • Handle: RePEc:sek:iefpro:13815878
    as

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    File URL: https://iises.net/proceedings/18th-economics-finance-conference-london/table-of-content/detail?cid=138&iid=004&rid=15878
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    References listed on IDEAS

    as
    1. Yawei Li & Shuqi Lv & Xinghua Liu & Qiuyue Zhang & Siew Ann Cheong, 2022. "Incorporating Transformers and Attention Networks for Stock Movement Prediction," Complexity, Hindawi, vol. 2022, pages 1-10, February.
    2. Tej Bahadur Shahi & Ashish Shrestha & Arjun Neupane & William Guo, 2020. "Stock Price Forecasting with Deep Learning: A Comparative Study," Mathematics, MDPI, vol. 8(9), pages 1-15, August.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Stock Prediction; Machine Learning; Recurrent Neural Network; LSTM; Transformer; Self Attention; Sentiment; Analysis; Technical Analysis;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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