{"title":"预测财务制约因素,引领社会经济发展:神经网络在意大利市场的应用","authors":"G.G. Calabrese , G. Falavigna , R. Ippoliti","doi":"10.1016/j.seps.2024.101973","DOIUrl":null,"url":null,"abstract":"<div><p>This study applies a neural network framework to optimize the classification of firms and to predict their difficulties in collecting external financial resources in the short term. In detail, we adopt replicated bootstrapped algorithms optimized on sensitivity and specificity as error measures and we propose a comparative analysis to identify the best-performing one. According to our results, the Conjugate gradient backpropagation with Fletcher-Reeves updates (i.e., CGF) is the best-performing algorithm, with sensitivity equal to 74.41 % and specificity equal to 70.11 %. Then, we use this algorithm and its weights to provide a classification of the Italian manufacturing industry in 2019, identifying the geographical areas in which firms under financial constraints are located, as well as the most critical industrial sectors. Based on this evidence, and considering the implementation of a cohesion policy, we highlight interventions by policy makers to support firms’ access to the capital market, fostering their investments and the consequent socio-economic development.</p></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":null,"pages":null},"PeriodicalIF":6.2000,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0038012124001721/pdfft?md5=1066d42c6a376ed349494c72cd406aa7&pid=1-s2.0-S0038012124001721-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Financial constraints prediction to lead socio-economic development: An application of neural networks to the Italian market\",\"authors\":\"G.G. Calabrese , G. Falavigna , R. Ippoliti\",\"doi\":\"10.1016/j.seps.2024.101973\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This study applies a neural network framework to optimize the classification of firms and to predict their difficulties in collecting external financial resources in the short term. In detail, we adopt replicated bootstrapped algorithms optimized on sensitivity and specificity as error measures and we propose a comparative analysis to identify the best-performing one. According to our results, the Conjugate gradient backpropagation with Fletcher-Reeves updates (i.e., CGF) is the best-performing algorithm, with sensitivity equal to 74.41 % and specificity equal to 70.11 %. Then, we use this algorithm and its weights to provide a classification of the Italian manufacturing industry in 2019, identifying the geographical areas in which firms under financial constraints are located, as well as the most critical industrial sectors. Based on this evidence, and considering the implementation of a cohesion policy, we highlight interventions by policy makers to support firms’ access to the capital market, fostering their investments and the consequent socio-economic development.</p></div>\",\"PeriodicalId\":22033,\"journal\":{\"name\":\"Socio-economic Planning Sciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.2000,\"publicationDate\":\"2024-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0038012124001721/pdfft?md5=1066d42c6a376ed349494c72cd406aa7&pid=1-s2.0-S0038012124001721-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Socio-economic Planning Sciences\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0038012124001721\",\"RegionNum\":2,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Socio-economic Planning Sciences","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0038012124001721","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
Financial constraints prediction to lead socio-economic development: An application of neural networks to the Italian market
This study applies a neural network framework to optimize the classification of firms and to predict their difficulties in collecting external financial resources in the short term. In detail, we adopt replicated bootstrapped algorithms optimized on sensitivity and specificity as error measures and we propose a comparative analysis to identify the best-performing one. According to our results, the Conjugate gradient backpropagation with Fletcher-Reeves updates (i.e., CGF) is the best-performing algorithm, with sensitivity equal to 74.41 % and specificity equal to 70.11 %. Then, we use this algorithm and its weights to provide a classification of the Italian manufacturing industry in 2019, identifying the geographical areas in which firms under financial constraints are located, as well as the most critical industrial sectors. Based on this evidence, and considering the implementation of a cohesion policy, we highlight interventions by policy makers to support firms’ access to the capital market, fostering their investments and the consequent socio-economic development.
期刊介绍:
Studies directed toward the more effective utilization of existing resources, e.g. mathematical programming models of health care delivery systems with relevance to more effective program design; systems analysis of fire outbreaks and its relevance to the location of fire stations; statistical analysis of the efficiency of a developing country economy or industry.
Studies relating to the interaction of various segments of society and technology, e.g. the effects of government health policies on the utilization and design of hospital facilities; the relationship between housing density and the demands on public transportation or other service facilities: patterns and implications of urban development and air or water pollution.
Studies devoted to the anticipations of and response to future needs for social, health and other human services, e.g. the relationship between industrial growth and the development of educational resources in affected areas; investigation of future demands for material and child health resources in a developing country; design of effective recycling in an urban setting.