Spatiotemporal Transformer for Stock Movement Prediction

TL;DR

This paper introduces STST, a spatiotemporal Transformer-LSTM model that predicts stock movements by integrating diverse market factors, achieving notable accuracy and profitability improvements over benchmarks.

Contribution

The paper presents a novel spatiotemporal Transformer-LSTM model for stock prediction, effectively capturing complex market dynamics and outperforming existing methods in accuracy and profitability.

Findings

Achieved 63.7% accuracy on ACL18 dataset

Achieved 56.9% accuracy on KDD17 dataset

Generated at least 10.41% higher profit than S&P 500

Abstract

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%.