  Most applications of machine learning for finance are related to forecasting
tasks for investment decisions. Instead, we aim to promote a better
understanding of financial markets with machine learning techniques. Leveraging
the tremendous progress in deep learning models for natural language
processing, we construct a hierarchical Reformer ([15]) model capable of
processing a large document level dataset, SEDAR, from canadian financial
regulatory filings. Using this model, we show that it is possible to predict
trade volume changes using regulatory filings. We adapt the pretraining task of
HiBERT ([36]) to obtain good sentence level representations using a large
unlabelled document dataset. Finetuning the model to successfully predict trade
volume changes indicates that the model captures a view from financial markets
and processing regulatory filings is beneficial. Analyzing the attention
patterns of our model reveals that it is able to detect some indications of
material information without explicit training, which is highly relevant for
investors and also for the market surveillance mandate of financial regulators.
