Towards a multilingual financial narrative processing system

Large scale financial narrative processing for UK annual reports has only become possible in the last few years with our prior work on automatically understanding and extracting the structure of unstructured PDF glossy reports. This has levelled the playing field somewhat relative to US research where annual reports (10-K Forms) have a rigid structure imposed on them by legislation and are submitted in plain text format. The structure extraction is just the first step in a pipeline of analyses to examine disclosure quality and change over time relative to financial results. In this paper, we describe and evaluate the use of similar Information Extraction and Natural Language Processing methods for extraction and analysis of annual financial reports in a second language (Portuguese) in order to evaluate the applicability of our techniques in another national context (Portugal). Extraction accuracy varies between languages with English exceeding 95%. To further examine the robustness of our techniques, we apply the extraction methods on a comprehensive sample of annual reports published by UK and Portuguese non-financial firms between 2003 and 2015.