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AI artificial intelligence brings about new quantitative techniques to assess the state of an economy. Here, we describe a new measure for systemic risk: the Financial Risk Meter (FRM). This measure is based on the penalization parameter (λ) of a linear quantile lasso regression. The FRM is calculated by taking the average of the penalization parameters over the 100 largest US publicly-traded financial institutions. We demonstrate the suitability of this AI-based risk measure by comparing the proposed FRM to other measures for systemic risk, such as VIX, SRISK and Google Trends. We find that mutual Granger causality exists between the FRM and these measures, which indicates the validity of the FRM as a systemic risk measure. The implementation of this project is carried out using parallel computing, the codes are published on www.quantlet.de with keyword  FRM. The R package RiskAnalytics is another tool with the purpose of integrating and facilitating the research, calculation and analysis methods around the FRM project. The visualization and the up-to-date FRM can be found on hu.berlin/frm.

There are about 1 billion hypertensives patients on a global scale. Hypertension has become the main cause of shorter lifespan and disability for humans worldwide. In this essay, we constructed a new model based on hybrid feature selection and the standard XGBoost for hypertension detection and prediction. After having successfully utilized Lasso regression to identify hypertension-related factors, we used the standard XGBoost model for hypertension prediction. The result from the experiments conducted on the data from the BRFSS shows that proposed model can achieve 77.2% accuracy and 84.6% AUC, both about 7% higher than that without the nonoptimized model. Our proposed model can not only be used to predict the risk of hypertension, but also provide customers with suggestions on how to lead a healthy lifestyle.