English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Most applications of machine learning for finance are related to forecastingtasks for investment decisions. Instead, we aim to promote a betterunderstanding of financial markets with machine learning techniques. Leveragingthe tremendous progress in deep learning models for natural languageprocessing, we construct a hierarchical Reformer ([15]) model capable ofprocessing a large document level dataset, SEDAR, from canadian financialregulatory filings. Using this model, we show that it is possible to predicttrade volume changes using regulatory filings. We adapt the pretraining task ofHiBERT ([36]) to obtain good sentence level representations using a largeunlabelled document dataset. Finetuning the model to successfully predict tradevolume changes indicates that the model captures a view from financial marketsand processing regulatory filings is beneficial. Analyzing the attentionpatterns of our model reveals that it is able to detect some indications ofmaterial information without explicit training, which is highly relevant forinvestors and also for the market surveillance mandate of financial regulators.

Discovering material information using hierarchical Reformer model on financial regulatory filings