Multi-Task Learning for Features Extraction in Financial Annual Reports
Syrielle Montariol Matej Martinc Andraž Pelicon Senja Pollak Boshko Koloski Igor Lončarski Aljoša Valentinčič
For assessing various performance indicators of companies, the focus is
shifting from strictly financial (quantitative) publicly disclosed information
to qualitative (textual) information. This textual data can provide valuable
weak signals, for example through stylistic features, which can complement the
quantitative data on financial performance or on Environmental, Social and
Governance (ESG) criteria. In this work, we use various multi-task learning
methods for financial text classification with the focus on financial
sentiment, objectivity, forward-looking sentence prediction and ESG-content
detection. We propose different methods to combine the information extracted
from training jointly on different tasks; our best-performing method highlights
the positive effect of explicitly adding auxiliary task predictions as features
for the final target task during the multi-task training. Next, we use these
classifiers to extract textual features from annual reports of FTSE350
companies and investigate the link between ESG quantitative scores and these
features.