Spatiotemporal Transformer for Stock Movement Prediction
Daniel Boyle Jugal Kalita
Financial markets are an intriguing place that offer investors the potential
to gain large profits if timed correctly. Unfortunately, the dynamic,
non-linear nature of financial markets makes it extremely hard to predict
future price movements. Within the US stock exchange, there are a countless
number of factors that play a role in the price of a company's stock, including
but not limited to financial statements, social and news sentiment, overall
market sentiment, political happenings and trading psychology. Correlating
these factors is virtually impossible for a human. Therefore, we propose STST,
a novel approach using a Spatiotemporal Transformer-LSTM model for stock
movement prediction. Our model obtains accuracies of 63.707 and 56.879 percent
against the ACL18 and KDD17 datasets, respectively. In addition, our model was
used in simulation to determine its real-life applicability. It obtained a
minimum of 10.41% higher profit than the S&P500 stock index, with a minimum
annualized return of 31.24%.