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

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Data Science Technologies in Economics and Finance: A Gentle Walk-In,d 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

Omniscient

Supervised Learning for the Prediction of Firm Dynamics,ied 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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Opening the Black Box: Machine Learning Interpretability and Inference Tools with an Application to 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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Machine Learning for Financial Stability,the 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

Insensate

不满分子

Classifying Counterparty Sector in EMIR Data,tails 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

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New Data Sources for Central Banks,ential of exploiting new sources of data to enhance the economic and statistical analyses of central banks (CBs). These sources are typically more granular and available at a higher frequency than traditional ones and cover structured (e.g., credit card transactions) and unstructured (e.g., newspape

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Sentiment Analysis of Financial News: Mechanics and Statistics,tion, as we focus our target of predictions on financial time series, we present a set of stylized empirical facts describing the statistical properties of lexicon-based sentiment indicators extracted from news on financial markets. Examples of these modeling methods and statistical hypothesis tests

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