Data Mining for Municipal Financial Distress Prediction

Alaminos, David; Fernández, Sergio; García, Francisca Miguel; Fernández, Manuel Ángel Gargallo

doi:10.1007/978-3-319-95786-9_23

Cited by 8 publications

(10 citation statements)

References 30 publications

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“…The results obtained by studies, such as that of [8], confirm the convenience, both explanatory and potential classification capacity, of global models for predicting these crisis events in comparison with regional models or with information from a single country. Besides, there is a demand for more research on global models connected with the increase of accuracy and the scope of the information used, since the studies that have obtained high levels of precision used very small samples, mainly from a single country, and, therefore, with short-term conclusions [6,9,10].…”

Section: Introductionmentioning

confidence: 52%

“…Therefore, the previous literature shows a greater predictive capacity of machine learning methodologies over statistical methodologies. But this same literature shows that the results obtained so far are not enough and that this type of methodologies can achieve a higher level of precision [8,11]. Also, it is detailed in previous works that the use of data has been very limited in time horizon and geographic space, making it a challenge to increase it for future works [4,9,13].…”

Section: Currency Crises Predictionmentioning

confidence: 99%

“…i describes the prediction value in step t, and Ω( f i ) is fixed starting the regularization defines in Equation (8).…”

Section: Extreme Gradient Boosting (Xgboost)mentioning

confidence: 99%

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Sovereign Debt and Currency Crises Prediction Models Using Machine Learning Techniques

et al. 2021

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Sovereign debt and currencies play an increasingly influential role in the development of any country, given the need to obtain financing and establish international relations. A recurring theme in the literature on financial crises has been the prediction of sovereign debt and currency crises due to their extreme importance in international economic activity. Nevertheless, the limitations of the existing models are related to accuracy and the literature calls for more investigation on the subject and lacks geographic diversity in the samples used. This article presents new models for the prediction of sovereign debt and currency crises, using various computational techniques, which increase their precision. Also, these models present experiences with a wide global sample of the main geographical world zones, such as Africa and the Middle East, Latin America, Asia, Europe, and globally. Our models demonstrate the superiority of computational techniques concerning statistics in terms of the level of precision, which are the best methods for the sovereign debt crisis: fuzzy decision trees, AdaBoost, extreme gradient boosting, and deep learning neural decision trees, and for forecasting the currency crisis: deep learning neural decision trees, extreme gradient boosting, random forests, and deep belief network. Our research has a large and potentially significant impact on the macroeconomic policy adequacy of the countries against the risks arising from financial crises and provides instruments that make it possible to improve the balance in the finance of the countries.

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Section: Introductionmentioning

confidence: 52%

Section: Currency Crises Predictionmentioning

confidence: 99%

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Sovereign Debt and Currency Crises Prediction Models Using Machine Learning Techniques

et al. 2021

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“…The percentage of correctly classified cases (accuracy) and the root of the mean square error have been used for the evaluation. Furthermore, for the treatment of each of the three groups, the 10-fold cross validation procedure has been applied with 500 iterations [28,29].…”

Section: Sample and Datamentioning

confidence: 99%

A global prediction model for sudden stops of capital flows using decision trees

et al. 2020

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Capital flows is an important aspect of the international monetary system because they provide great direct and indirect benefits, and at the same time, they carry risks of vulnerability for countries with an open economy. Numerous works have studied the behavior of these flows and have developed models to predict sudden stop events. However, the existing models have limitations and the literature demands more research on the subject given that the accuracy of the models is still poor, and they have only been developed for emerging countries. This paper presents a new prediction model of sudden stop events of capital flows for both emerging countries and developed countries with the ability to estimate accurately future sudden stop scenarios globally. A sample of 103 countries was used, including 73 emerging countries and 30 developed countries, which has allowed the use of sample combinations that consider the regional heterogeneity of the warning indicators. To the sample under study, a method of decision trees has been applied, which has provided excellent prediction results given its ability to learn characteristics and create long-term dependencies from sequential data and time series. Our model has a great potential impact on the adequacy of macroeconomic policy against the risks derived from sudden stops of capital flows, providing tools that help to achieve financial stability at the global level.

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“…Following that directive, in Spain, the Organic Law 2/2012 (2012), 27 April, defines that commercial debt is sustainable when the APP complies with the 30-day threshold. Therefore, complying with this threshold may be a proxy for identifying entities with lower indebtedness and in good financial condition (Alaminos et al 2018;Balaguer-Coll and Ivanova-Toneva 2019). This directive was transposed by the Portuguese Decree-Law 62/2013, 10 May, with the APP methodology defined by the RMC 34/2008, 22 February (2008, and by the Administrative Decision 9870/20099870/ , 13 April (2009.…”

Section: Introductionmentioning

confidence: 99%

Does the Average Payment Period Play a Relevant Role in Explaining the Portuguese Municipalities’ Financial Distress?

Santos

Albuquerque

2023

Economies

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The average payment period (APP) is an important measure to monitor public sector liquidity and commercial debt sustainability. In the European Union, the relevance of APP as an indicator to mitigate those concerns was settled by the issuance of a Directive on this topic. Then, considering the likely relationship between the APP and financial distress (FD), this paper aims to assess if the current and historical APP plays a relevant role in explaining the data for 308 Portuguese municipalities’ FD. Regression analysis, including panel data and simple linear regression models, is used for the period between 2011 and 2019 (the last year available for APP). The findings show that the number of municipalities that exceed the debt limit has been reducing. Some of them comply with the 30 days threshold being, nonetheless, in a financial distress situation and subject to recovery mechanisms. By APP levels, the findings identify that the APP significantly predicts FD for those entities that do not comply with the threshold. By size, the FD seems to be better explained by historical APP within the smaller entities. Finally, the robustness test and further analyses performed indicate the likely relevance of macroeconomic factors for predicting FD using APP as an explanatory variable. This paper contributes to previous studies on the municipalities’ financial condition, namely by considering the APP in the analysis of the financial situation of the municipalities. As a novelty, this study considers a new proxy for municipal FD and panel data analysis.

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Data Mining for Municipal Financial Distress Prediction

Cited by 8 publications

References 30 publications

Sovereign Debt and Currency Crises Prediction Models Using Machine Learning Techniques

Sovereign Debt and Currency Crises Prediction Models Using Machine Learning Techniques

A global prediction model for sudden stops of capital flows using decision trees

Does the Average Payment Period Play a Relevant Role in Explaining the Portuguese Municipalities’ Financial Distress?

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