Support for Stock Trend Prediction Using Transformers and Sentiment
  Analysis
Harsimrat Kaeley Ye Qiao Nader Bagherzadeh
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.