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Risk Factor Identification of Sustainable Guarantee Network

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Risk Factor Identification of Sustainable Guarantee Network sustainability Article Risk Factor Identification of Sustainable Guarantee Network Based on Logistic Regression Algorithm Han He 1, Sicheng Li 1,* , Lin Hu 1, Nelson Duarte 2 , Otilia Manta 3 and Xiao-Guang Yue 4,* 1 School of Economics and Management, Huazhong Agricultural University, Wuhan 430070, China 2 School of Management and Technology, Porto Polytechnic, Center for Research and Innovation in Business Sciences and Information Systems, 4610-156 Felgueiras, Portugal 3 Center for Financial and Monetary Research-Victor Slăvescu, Romanian Academy, 010071 Bucharest, Romania 4 Rattanakosin International College of Creative Entrepreneurship, Rajamangala University of Technology Rattanakosin, Nakon Patom 73170, Thailand * Correspondence: [email protected] (S.L.); [email protected] (X.-G.Y.); Tel.: +86-151-7253-6323 (S.L.) Received: 30 May 2019; Accepted: 22 June 2019; Published: 27 June 2019 Abstract: In order to investigate the factors influencing the sustainable guarantee network and its differences in different spatial and temporal scales, logistic regression algorithm is used to analyze the data of listed companies in 31 provinces, municipalities and autonomous regions in China from 2008 to 2017 (excluding Hong Kong, Macau and Taiwan). The study finds that, overall, companies with better profitability, poor solvency, poor operational capability and higher levels of economic development are more likely to join the guarantee network. On the temporal scale, solvency and regional economic development exert increasing higher impact on the companies’ accession to the guarantee network, and operational capacity has increasingly smaller impact. On the spatial scale, the less close link between company executives and companies in the western region suggests higher possibility to join the guarantee network. The predictive accuracy test results of the logistic regression algorithm show that the training model of the western sample enterprises has the highest prediction accuracy when predicting enterprise behavior of joining the guarantee network, while the accuracy is the lowest in the central region. When forecasting enterprises’ failure to join the guarantee network, the training model of the central sample enterprise has the highest accuracy, while the accuracy is the lowest in the eastern region. This paper discusses the internal and external factors influencing the guarantee network risk from the perspective of spatial and temporal differences of the guarantee network, and discriminates the prediction accuracy of the training model, which means certain guiding significance for listed company management, bank and government to identify and control the guarantee network risk. Keywords: guarantee network; risk factors; temporal-spatial difference; logistic regression algorithm 1. Introduction Guarantee is an important way to solve the financing problem of companies. A good guarantee system can improve the credit rating of companies and enhance their financing ability. However, non-standard guarantee behavior can also worsen the financial status of the company and even harm the local economic environment. Since the chain reaction of China’s “Fujian Guarantee Circle” in 2000, the guarantee network risk has always been a hot issue of concern to the government and companies. The negative “Domino” effect caused by it not only endangers the local financial ecology, but also forms regional financial risk, which will even spread across regions and pose a huge threat to the Chinese economy. In 2011, Zhengzhou Chengtai and Shengwo guarantee events caused capital chain rupture Sustainability 2019, 11, 3525; doi:10.3390/su11133525 www.mdpi.com/journal/sustainability Sustainability 2019, 11, x FOR PEER REVIEW 2 of 18 Sustainability 2019, 11, 3525 2 of 19 to the Chinese economy. In 2011, Zhengzhou Chengtai and Shengwo guarantee events caused capital chain rupture in a number of enterprises in Henan, and the chain reaction caused Henan to fall into inguarantee a number ofcrisis. enterprises In 2012, inZhongdan Henan, and Company the chain frau reactiondulently caused defrauded Henan SME to fall (small- into guaranteeand medium-size crisis. Inenterprise) 2012, Zhongdan loans. Company The risk broke fraudulently out at the defrauded beginning SME of the (small- year andand medium-sizethe “Domino” enterprise) effect affected loans. up Theto risk294 brokeenterprises, out at with the beginning the amount of theof obligation year and the close “Domino” to 1.3 billion effect yuan. affected In the up tosecond 294 enterprises, half of 2012, withthe the risk amount of steel of trade obligation had concentrated close to 1.3 billion exposure, yuan. and In the the second bad debts half ofreached 2012, the40 riskbillion, of steel with trade credit hadcrisis concentrated spreadingexposure, to the surrounding and the bad areas. debts In reached 2014, 40executives billion, withof Huitong credit crisis guarantee, spreading the tolargest the surroundingprivate guarantee areas. In company 2014, executives in Sichuan, of Huitong escaped guarantee, with money, the causing largest private5 billion guarantee funds to be company implicated. in Sichuan,Enterprises escaped and with banks money, in the causing guarantee 5 billion network funds ar toe facing be implicated. huge risks. Enterprises Ma, Zhang and and banks Liu in (2009) the guaranteeshow that network the risk are facingof the hugeguarantee risks. Ma,circle Zhang orig andinates Liu from (2009) the show “guarantee that therisk warranty,” of the guarantee which is circleintensified originates by the from “default the “guarantee information” warranty,” of the ente whichrprise is intensified and the “group by the response” “default information”of the bank’s debt of therecovery, enterprise finally and triggering the “group the response” crisis of the of theguarantee bank’s circle debt recovery,[1]. Aleksiejuk finally (2001) triggering and Angelini the crisis (2004) of thefound guarantee that loans circle or [1 credits]. Aleksiejuk between (2001) banks and are Angelini widespread (2004) around found the that world loans [2,3]. or credits According between to the banksdirected are widespread penetration around model, the the world large [2amount,3]. According of inter-bank to the directedcredits can penetration lead to penetration model, the large phase amountchange of characterized inter-bank credits by bank can bankruptcy lead to penetration expansio phasen to the change entire characterized bank network. by bankUpper bankruptcy and Worms expansion(2004) also to thefound entire that bank credit network. risk associated Upper andwith Worms interbank (2004) loans also may found lead that to credita “Domino” risk associated effect [4]. with interbankWith the loans rapid may development lead to a “Domino” of China’s eeconomy,ffect [4]. the number of guarantee networks formed by corporateWith the debt rapid financing development has also of increased China’s economy, year by theyear. number The distribution of guarantee of guarantee networks formednetworks by in corporateChina’s debtprovinces financing obtained has also through increased the year guarantee by year. Thedata distributionof CSMAR of (China guarantee Stock networks Market in & China’sAccounting provinces Research) obtained database through is the shown guarantee in Figure data of1. CSMARIt can be (China found Stockfrom MarketFigure &1 that Accounting there are Research)obvious databasespatial differences is shown in the Figure number1. It can of China’s be found guarantee from Figure networks.1 that thereWhere, are Beijing, obvious the spatial Yangtze diffRivererences Delta, in the and number Guangdong of China’s have guarantee the largest networks. number Where, of guarantee Beijing, networks. the Yangtze Once River the Delta, guarantee and Guangdongnetwork risk have breaks the largest out, successive number of insurance-remova guarantee networks.l of enterprises Once the guarantee will follow, network banks riskwill breakscompete out,for successive loan collection, insurance-removal the trust between of enterprises banks and will en follow,terprises banks will will drop compete sharply, for and loan the collection, regional thefinancial trust between ecology banks will andbe damaged, enterprises which will dropwill caus sharply,e an andadverse the regionalimpact on financial the development ecology will of be the damaged,real economy which [5]. will cause an adverse impact on the development of the real economy [5]. FigureFigure 1. 1.Distribution Distribution of of guarantee guarantee networks networks in in various various provinces provinces in in China. China. ComparedCompared with with China, China, the developedthe developed market market economy economy countries countries in Europe in Europe and the and United the StatesUnited haveStates more have diversified more diversified financing channels,financing and channels, quite di ffanderent quite legal different and regulatory legal environmentsand regulatory fromenvironments China. Therefore, from China. there Therefore, is no prevalence there is no of mutualprevalence guarantee of mutual loans guarantee between loans enterprises. between China’senterprises. guarantee China’s network guarantee has also network become ahas relatively also become unique a phenomenon relatively unique in the internationalphenomenon credit in the market.international At present, credit Chinese market. scholars’ At present, research Chinese on the scholars’ factors research affecting on guarantee the factors network affecting risk guarantee
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