2023/05/05 by Daniel Boyle, Jugal Kalita, Boyle, Daniel +1
Decision Sciences · Economics, Econometrics and Finance · #Artificial Intelligence (cs.AI) #Complex Systems and Time Series Analysis #Computational Engineering #FOS: Computer and information sciences #Finance #Financial Markets and Investment Strategies #Machine Learning (cs.LG) #Stock Market Forecasting Methods #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2305.03835
openalex publication_date 2023/05/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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%.