Predicting bankruptcy of firms using earnings call data and transfer learning.

Business collapse is a common event in economies, small and big alike. A firm's health is crucial to its stakeholders like creditors, investors, partners, etc. and prediction of the upcoming financial crisis is significantly important to devise appropriate strategies to avoid business collapses. Bankruptcy prediction has been regarded as a critical topic in the world of accounting and finance. Methodologies and strategies have been investigated in the research domain for predicting company bankruptcy more promptly and accurately. Conventionally, predicting the financial risk and bankruptcy has been solely achieved using the historic financial data. CEOs also communicate verbally via press releases and voice characteristics, such as emotion and tone may reflect a company's success, according to anecdotal evidence. Companies' publicly available earning calls data is one of the main sources of information to understand how businesses are doing and what are expectations for the next quarters. An earnings call is a conference call between the management of a company and the media. During the call, management offers an overview of recent performance and provides a guide for the next quarter's expectations. The earning calls summary provided by the management can extract CEO's emotions using sentiment analysis. This article investigates the prediction of firms' health in terms of bankruptcy and non-bankruptcy based on emotions extracted from earning calls and proposes a deep learning model in this regard. Features extracted from long short-term memory (LSTM) network are used to train machine learning models. Results show that the models provide results with a high score of 0.93, each for accuracy and F1 when trained on LSTM extracted feature from synthetic minority oversampling technique (SMOTE) balanced data. LSTM features provide better performance than traditional bag of words and TF-IDF features.

Impact of COVID-19 on the psychological health of university students in Spain and their attitudes toward Mobile mental health solutions.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has had an impact on several aspects of life, including university students' mental health. Mobile mental care applications (apps) comprise a form of online mental care that enables the delivery of remote mental care. OBJECTIVES: This study aimed to explore the impact of COVID-19 on the mental health of university students in Spain and to explore their attitudes toward the use of mobile mental care apps. METHOD: Respondents answered a survey, which comprised two sections. The first included the 12-item General Health Questionnaire (GHQ-12) that was employed to assess the students' mental health. The second section included six questions developed by the authors to explore the students' attitudes toward mental care apps. RESULTS: The results showed that the students suffered from anxiety and depression as well as social dysfunction. Further, 91.3 % of the students had never used a mobile app for mental health, 36.3 % were unaware of such apps, and 79.2 % were willing to use them in the future. CONCLUSIONS: The COVID-19 pandemic had a significant impact on the psychological health of university students. Mobile mental care apps may be an effective and efficient way to access mental care, particularly during a pandemic.