An Integrated Multimodal AttentionBased Approach for Bank Stress Test Prediction Since the financial crisis in late 20082009, several global regulatory authorities have mandated stresstesting exercises to evaluate the potential capital shortfalls u0026 systemic impacts that large banks may face during adverse economic conditions. Thus, having the ability to analyze economic conditions u0026 banking performance profiles together to determine relationships among their respective features may provide insights for stress testing tasks. In this paper, we propose an Integrated Multimodal Bank Stress Test Prediction (IMBSTP) model framework consisting of a twostages; (1) economic conditions estimator to approximate joint representation among the exogenous factors using generative models, (2) bank capital u0026 loss forecaster to project stress test measures based on dimensional u0026 temporal features selected from the exogenous economic conditions u0026 banking performance profiles using a dualattention recurrent neural network. Extensive experimentation is performed on historical economic conditions u0026 consolidated financial statements of U.S. bank holdings companies to show the effectiveness of our approach when compared to stateoftheart baseline methods.

Stress Recognition with State Classification Considering Temporal Variation of Stress Responses To avoid and manage stressrelated problems, several works have investigated how to recognize stress states of people by exploiting the machine learning. Most of works utilize the supervised learning which requires labels for data to be trained and tested. Thus, labels which represent stress state of each data such as stressed or not become definitely important. Conventional stress state classification methods assign labels to unit data per a certain experimental period by applying stressor appearances or selfevaluation scores. However, those methods ignore temporal variations in stress responses which are involuntarily triggered inside the body within a shorter period of time than an experimental period. Therefore, we propose a stress state classification method by considering not only useru0027s subjective evaluations but also temporal changes of stress responses in short periods. For the demonstration, we label our experimental data of 40 subjects by using our proposed classification method and conventional ones, respectively. Then, we train and test stress recognition models with 6 machine learning algorithms and our implemented neural network ones based on the labeled data. Finally, binary stress recognition with our proposed classification method improves the recognition accuracy by up to 31.6% as compared to those with conventional techniques.