A Deep Learning Approach for Dynamic Balance Sheet Stress Testing

In the aftermath of the financial crisis, supervisory authorities have considerably improved their approaches in performing financial stress testing. However, they have received significant criticism by the market participants due to the methodological assumptions and simplifications employed, which are considered as not accurately reflecting real conditions. First and foremost, current stress testing methodologies attempt to simulate the risks underlying a financial institution's balance sheet by using several satellite models, making their integration a really challenging task with significant estimation errors. Secondly, they still suffer from not employing advanced statistical techniques, like machine learning, which capture better the nonlinear nature of adverse shocks. Finally, the static balance sheet assumption, that is often employed, implies that the management of a bank passively monitors the realization of the adverse scenario, but does nothing to mitigate its impact. To address the above mentioned criticism, we introduce in this study a novel approach utilizing deep learning approach for dynamic balance sheet stress testing. Experimental results give strong evidence that deep learning applied in big financial/supervisory datasets create a state of the art paradigm, which is capable of simulating real world scenarios in a more efficient way.Comment: Preprint submitted to Journal of Forecastin

Cytokines induced during chronic hepatitis B virus infection promote a pathway for NK cell–mediated liver damage

Hepatitis B virus (HBV) causes chronic infection in more than 350 million people worldwide. It replicates in hepatocytes but is non-cytopathic; liver damage is thought to be immune mediated. Here, we investigated the role of innate immune responses in mediating liver damage in patients with chronic HBV infection. Longitudinal analysis revealed a temporal correlation between flares of liver inflammation and fluctuations in interleukin (IL)-8, interferon (IFN)-α, and natural killer (NK) cell expression of tumor necrosis factor–related apoptosis-inducing ligand (TRAIL) directly ex vivo. A cross-sectional study confirmed these findings in patients with HBV-related liver inflammation compared with healthy carriers. Activated, TRAIL-expressing NK cells were further enriched in the liver of patients with chronic HBV infection, while their hepatocytes expressed increased levels of a TRAIL death–inducing receptor. IFN-α concentrations found in patients were capable of activating NK cells to induce TRAIL-mediated hepatocyte apoptosis in vitro. The pathogenic potential of this pathway could be further enhanced by the ability of the IFN-α/IL-8 combination to dysregulate the balance of death-inducing and regulatory TRAIL receptors expressed on hepatocytes. We conclude that NK cells may contribute to liver inflammation by TRAIL-mediated death of hepatocytes and demonstrate that this non-antigen–specific mechanism can be switched on by cytokines produced during active HBV infection

Power-law mixtures of bayesian forests for value added tax audit case selection

Tax authorities need to maximize the yield of the limited tax audits they afford to perform each year. Thus, they need to predict the likelihood of a candidate audit resulting in a satisfactory yield; this predictive process is usually referred to as audit case selection. Random Forests (RFs) constitute a standard method for Value Added Tax (VAT) audit case selection. Despite, though, their success, their predictive performance is still below the expectations of tax authorities, that need to timely detect cases of significant audit yield potential. This lackluster performance is mainly attributed to the fact that RFs cannot deal with data that entail non-stationary nature, multiple modalities, or discontinuities. These are common characteristics of real-world datasets; thus, the incapacity to properly address them is a major suspect for undermining their performance. This work addresses these issues by considering a generative non-parametric Bayesian model with power-law behavior, capable of generating distinct (Bayesian) RFs over the observations space of the modeled data. This way, our approach enables capturing an indefinite number of distinct classification patterns, while being able to effectively handle outliers. The latter advantage is of paramount importance for the effectiveness of the modeling procedure in cases where few large parts of the observations space can be modeled by few RF classifiers, yet there is a large number of small parts of the observations space that require distinct RFs to be properly modeled (power-law nature). We provide an efficient algorithm for model inference, based on the variational Bayesian framework, and prove its efficacy using real-world datasets

Improved indoor air quality during desert dust storms: The impact of the MEDEA exposure-reduction strategies

Desert dust storms (DDS) are natural events that impact not only populations close to the emission sources but also populations many kilometers away. Countries located across the main dust sources, including countries in the Eastern Mediterranean, are highly affected by DDS. In addition, climate change is expanding arid areas exacerbating DDS events. Currently, there are no intervention measures with proven, quantified exposure reduction to desert dust particles. As part of the wider "MEDEA" project, co-funded by LIFE 2016 Programme, we examined the effectiveness of an indoor exposure-reduction intervention (i.e., decrease home ventilation during DDS events and continuous use of air purifier during DDS and non-DDS days) across homes and/or classrooms of schoolchildren with asthma and adults with atrial fibrillation in Cyprus and Crete-Greece. Participants were randomized to a control or intervention groups, including an indoor intervention group with exposure reduction measures and the use of air purifiers. Particle sampling, PM10 and PM2.5, was conducted in participants' homes and/or classrooms, between 2019 and 2022, during DDS-free weeks and during DDS days for as long as the event lasted. In indoor and outdoor PM10 and PM2.5 samples, mass and content in main and trace elements was determined. Indoor PM2.5 and PM10 mass concentrations, adjusting for premise type and dust conditions, were significantly lower in the indoor intervention group compared to the control group (PM2.5-intervention/PM2.5-control = 0.57, 95% CI: 0.47, 0.70; PM10-intervention/PM10-control = 0.59, 95% CI: 0.49, 0.71). In addition, the PM2.5 and PM10 particles of outdoor origin were significantly lower in the intervention vs. the control group (PM2.5 infiltration intervention-to-control ratio: 0.49, 95% CI: 0.42, 0.58; PM10 infiltration intervention-to-control ratio: 0.68, 95% CI: 0.52, 0.89). Our findings suggest that the use of air purifiers alongside decreased ventilation measures is an effective protective measure that reduces significantly indoor exposure to particles during DDS and non-DDS in high-risk population groups