Titlebook: Data Science for Economics and Finance; Methodologies and Ap Sergio Consoli,Diego Reforgiato Recupero,Michaela Book‘‘‘‘‘‘‘‘ 2021 The Edito

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The Analysis of Event-Related Potentialss and retain all of the information available. Third, these methods are purely data driven. All of these characteristics contribute to their often better predictive performance. However, as “black box” models, they are still much underutilized in financial stability, a field where interpretability and accountability are crucial.

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Computational Drug Discovery and Designl forms in the data generating process and the ability to perform statistical inference. The latter is achieved by the Shapley regression framework, which allows for the evaluation and communication of machine learning models akin to that of linear models.

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Computational Earthquake Science Part Iidentiality points of view. This chapter discusses the advantages and the challenges that CBs face in using new sources of data to carry out their functions. In addition, it describes a few successful case studies in which new data sources have been incorporated by CBs to improve their economic and forecasting analyses.

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Andrea Donnellan,Peter Mora,Xiang-chu YinG) aspects. In an empirical analysis, using a Dutch-written news corpus, we create news-based ESG signals for a large list of companies and compare these to scores from an external data provider. We find preliminary evidence of abnormal news dynamics leading up to downward score adjustments and of efficient portfolio screening.

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Opening the Black Box: Machine Learning Interpretability and Inference Tools with an Application tol forms in the data generating process and the ability to perform statistical inference. The latter is achieved by the Shapley regression framework, which allows for the evaluation and communication of machine learning models akin to that of linear models.

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