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%.

翻译：金融市场为投资者提供了潜在的高额利润机会，但其动态、非线性的特性使得预测未来价格走势极为困难。在美国股市中，影响公司股价的因素数不胜数，包括但不限于财务报表、社交与新闻情绪、整体市场情绪、政治事件及交易心理。人类几乎无法将这些因素关联起来。因此，我们提出STST，一种基于时空Transformer-LSTM模型的新型股票走势预测方法。我们的模型在ACL18和KDD17数据集上分别达到63.707%和56.879%的准确率。此外，我们通过仿真测试评估了模型的现实应用潜力：模型获得的利润较标普500指数至少高出10.41%，年化收益率最低为31.24%。