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Using Government Data and Machine Learning for Predicting Firms’ Vulnerability to Economic Crisis

Abstract : The COVID-19 pandemic is expected to lead to a severe recessionary economic crisis with quite negative consequences for large numbers of firms and citizens; however, this is an ‘old story’: recessionary economic crises appear repeatedly in the last 100 years in the market-based economies, and they are recognized as one of the most severe and threatening weaknesses of them. They can result in closure of numerous firms, and decrease of activities of many more, as well as poverty and social exclusion for large parts of the population, and finally lead to political upheaval and instability; so they constitute one of the most threatening and difficult problems that governments often face. For the above reasons it is imperative that governments develop effective public policies and make drastic interventions for addressing these economic crises. Quite useful for these interventions can be the prediction of the vulnerability of individual firms to recessionary economic crisis, so that government can focus its attention as well as its scarce economic resources on the most vulnerable ones. In this direction our paper presents a methodology for using existing government data in order to predict the vulnerability of individual firms to economic crisis, based on Artificial Intelligence (AI) Machine Learning (ML) algorithms. Furthermore, a first application of the proposed methodology is presented, based on existing data from the Greek Ministry of Finance and Statistical Authority concerning 363 firms for the economic crisis period 2009–2014, which gives encouraging results.
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https://hal.inria.fr/hal-03282760
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Submitted on : Friday, July 9, 2021 - 2:01:08 PM
Last modification on : Tuesday, July 13, 2021 - 3:44:17 AM
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Euripidis Loukis, Niki Kyriakou, Manolis Maragoudakis. Using Government Data and Machine Learning for Predicting Firms’ Vulnerability to Economic Crisis. 19th International Conference on Electronic Government (EGOV), Aug 2020, Linköping, Sweden. pp.345-358, ⟨10.1007/978-3-030-57599-1_26⟩. ⟨hal-03282760⟩

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