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

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https://doi.org/10.1007/978-1-61779-465-0d information technology in the past decade has made available vast amounts of data in various domains, which has been referred to as .. In economics and finance, in particular, tapping into these data brings research and business closer together, as data generated in ordinary economic activity can

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AMMOS Software: Method and Applicationied to address multiple research questions related to firm dynamics. Especially supervised learning (SL), the branch of ML dealing with the prediction of labelled outcomes, has been used to better predict firms’ performance. In this chapter, we will illustrate a series of SL approaches to be used fo

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Computational Drug Discovery and Design models mostly outperform conventional econometric approaches in forecasting changes in US unemployment on a 1-year horizon. To address the black box critique of machine learning models, we apply and compare two variables attribution methods: permutation importance and Shapley values. While the aggr

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The Analysis of Event-Related Potentialsthe complex, nonlinear, time-varying, and multidimensional nature of the data. A strand of literature has shown that machine learning approaches can make more accurate data-driven predictions than standard empirical models, thus providing more and more timely information about the building up of fin

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Garett D. Johnson,Dean J. Krusienskitails on derivatives but their use poses numerous challenges. To overcome one major challenge, this chapter draws from eight different data sources and develops a greedy algorithm to obtain a new counterparty sector classification. We classify counterparties’ sector for 96% of the notional value of