Speech Emotion Recognition from Earnings Conference Calls in Predicting Corporate Financial Distress

A Semiparametric Gaussian Copula Regression Model for Predicting Financial Risks from Earnings Calls

Earnings call summarizes the financial performance of a company, and it is an important indicator of the future financial risks of the company. We quantitatively study how earnings calls are correlated with the financial risks, with a special focus on the financial crisis of 2009. In particular, we perform a text regression task: given the transcript of an earnings call, we predict the volatili...

Predicting Financial Distress in Tehran Stock Exchange

Companies incur significant costs from the financial distress. Predicting financial distress will have an important role in preventing bankruptcy. The aim of the present study is to predict the financial distress costs using the Leland and Toft models, during 1996 and 1998. This study examines data relating to 49 companies listed in the Tehran stock exchange collected over ten years from 2005 t...

Emotion Recognition from Speech

This paper proposes the classification of emotions based on spectral features using the Gaussian Mixture Model as the classifier. The performance of the Gaussian Mixture Model has been evaluated for two types of databases – acted and reallife speech corpuses. The model has also been evaluated for the variation in its performance based on the speaker, gender of the speaker and the number of the ...

Corporate Response to Distress: Evidence from the Asian Financial Crisis

This paper provides a comprehensive examination of corporate responses to financial distress during an economy-wide crisis, specifically through the restructuring of assets (through asset sales, mergers, or liquidations) and/or liabilities. Using firm-level data from five countries hardest hit by the East Asian financial crisis of 1997-98, this study contrasts the effects that financial and cor...

Corporate Financial Distress: An Empirical Analysis of Distress Risk