Group, Extract and Aggregate: Summarizing a Large Amount of Finance News for Forex Movement Prediction
Table 1: Results of baselines and proposed methods on the test set (input time window is 40 minutes, and prediction delay is 5 minutes, we observe similar result in other time settings). All the experiment results have proven significant with p<0.05 by student t-test.
['Method', 'USD-EUR F1', 'USD-EUR MCC', 'USD-JPY F1', 'USD-JPY MCC', 'USD-RMB F1', 'USD-RMB MCC', 'USD-GBP F1', 'USD-GBP MCC']
The BHAM-Category performs best among these methods, which shows that the semantic information of finance news is mostly aggregated by category. All the methods get improved after introducing the text information, which proves the related finance news is helpful for the prediction. The performance of NoGroup method decreases by a large margin compared to BHAM-Category, which demonstrates that the hierarchical structure works well. Without hierarchical structure, selecting essential news directly from all news has more noise and requires the model to have a stronger fitting ability for a longer paragraph. After removing the news connection, the performance of NoConnect method drops sharply compared to BHAM-Category. Accessing the news representation from the connected paragraph helps the news representation realize the context information in the group. The LSTM+Attention method performs worse than the BERT-based method, which proves that BERT has stronger power of sentence encoding. The two methods borrowed from stock movement prediction are designed to consider all news’s information, but the forex related news is redundant, which can explain the poor performance of these two methods.