  A public firm's bankruptcy prediction is an important financial research
problem because of the security price downside risks. Traditional methods rely
on accounting metrics that suffer from shortcomings like window dressing and
retrospective focus. While disclosure text-based metrics overcome some of these
issues, current methods excessively focus on disclosure tone and sentiment.
There is a requirement to relate meaningful signals in the disclosure text to
financial outcomes and quantify the disclosure text data. This work proposes a
new distress dictionary based on the sentences used by managers in explaining
financial status. It demonstrates the significant differences in linguistic
features between bankrupt and non-bankrupt firms. Further, using a large sample
of 500 bankrupt firms, it builds predictive models and compares the performance
against two dictionaries used in financial text analysis. This research shows
that the proposed stress dictionary captures unique information from
disclosures and the predictive models based on its features have the highest
accuracy.
