резюме_ПС_английский_26 10 2018 (1138458), страница 3
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Up to this point, the registration of payment systems was carried out.11Operating expensesMore 1000More 3000More 5000Source: compiled by the author of the dissertation research based on data from the Bank of Russiawebsite, Mobile Agency database, and the SPARK AgencyTo assess the impact of financial indicators on the sustainability of paymentinfrastructure service providers, a binary panel regression and binary logisticregressions were constructed. The results of the regressions are presented in table 2.Table 2 – A model that determines financial indicators that have apositive/negative impact on the stability of the operator17Variable nameFull logisticregressionForeign currency assets more than3000 million rublesForeign currency assets more than6000 million rublesForeign currency assets more than9000 million rublesCash more than 1000 millionrublesCash more than 2000 millionrublesCash more than 3000 millionrublesNet assets more than 2000 millionrublesThe net assets of more than 4000million rublesThe net assets of more than 6000million rublesAuthorized capital more than 100million rublesAuthorized capital more than 200million rublesThe authorized capital is morethan 300 million rublesNon-negative net profit1,19991Net profit more than 50 millionrublesNet profit more than 100 million17Logisticregression withsignificantvariables1,364053**Marginal effects-1,045061**-0,1816333**-0,6035693*-0,1049014*-0,6004165*-0,1043534*-0,5872104*-0,1020582*0,2370748**-3,3100067,786359-4,824567-4,747453,907719-4,7841435,459388-1,48428610,05074-11,258541,238295-1,018283**0,13266960,2627694The thresholds were chosen statistically, taking into account the maximization of the quality criteriaof econometric estimates.12rublesOperating expenses more than1000 million rublesOperating expenses more than3000 million rublesOperating expenses more than5000 million rublesResiduals0,0762038-5,5096395,0951150,463274-0,3067472Number of observations144163163Prob>chi20,00910,0170-R-squared0,33600,1175-Source: compiled by the author, stars reflect p-value: *** p<0.01, **p<0.05, *p<0.1The result of the binary logistic regression is significant.
The classification tableshows that the model predicts 88,34% of cases. The ROC curve also shows a high valueof 0,7279 (figure 1). Thus, the results are significant. The average marginal effects were0.500.000.25Sensitivity0.751.00also calculated in the model.0.000.250.501 - Specificity0.751.00Area under ROC curve = 0.7279Figure 1 – ROC curve for a model with significant variablesSource: compiled by the authorAs a result, a group of significant financial indicators was determined (figure 2).13Non-negative net profitCash more than 2000 million rublesForeign currency assets more than 3000 millionrublesAuthorized capital more than 200 million rublesThe net assets of more than 6000 million rubles-25,00% -20,00% -15,00% -10,00% -5,00%0,00%5,00%10,00% 15,00% 20,00% 25,00% 30,00%The net assets of more than 6000 million rublesAuthorized capital more than 200 million rublesForeign currency assets more than 3000 million rublesCash more than 2000 million rublesNon-negative net profitFigure 2 – Factors affecting the decrease / increase in the probability of exclusion from the Register ofpayment system operatorsSource: constructed by the authorIt was found that the sustainability is positively affected by net assets, sharecapital, cash and net profit.
In case of exceeding the thresholds, such paymentinfrastructure service providers will demonstrate greater sustainability. In particular, ifthe volume of net assets of the operator is more than 6 billion rubles, the probability ofexcluding from the Register of payment system operators is reduced by 10.5%. Theexcess of foreign currency assets causes a negative impact on the stability of theoperator after 3000 million rubles. The significant financial variables will be used in thecalculation of the financial stability coefficient.Model with institutional indicators of payment systemsThe second group of factors are the institutional characteristics of the paymentsystem, including both organizational features and technological characteristics of theoperations. The constructed regression is based on the rules of payment systems.All groups of considered indicators can be divided into two main types: indicatorsof smooth functioning and institutional characteristics of payment systems associatedwith the model of risk management system, the implementation of intraday credit14service and the organization of interaction between operators and the operator of thepayment system (table 3).
Information on institutional indicators of payment systemswas taken from the Rules of payment systems, as well as from the website of the Bankof Russia.Table 3 – Description of variables for the model with institutional indicatorsVariable nameWork experiencerequirementDescription of variablesmore 3 years, more 5 yearsCrash recovery timeMore than 24 hours, more 48hoursThe use of the indicator P1 inthe payment system (dummyvariable)Application of the P2 indicatorin the payment system (dummyvariable)Application of the P3 indicatorin the payment system (dummyvariable)Application of the P4 indicatorin the payment system (dummyvariable)Application of the P5 indicatorin the payment system (dummyvariable)Is the intraday loan functionimplemented in the paymentsystem (dummy variable)Whether the payment system asnationally important (dummyvariable)Is payment system the sociallysignificant (the dummy variable)Payment system operators arenot affiliated with each otherlegal entities (1-Yes, 0-no)Failure recovery time(P1))Time between events(P2)Number of paymentsmade to total (P3)A measure of availabilityof the payment system(P4)Average number ofincidents (P5)The availability ofintraday credit in thepayment systemWhether the paymentsystem as nationallyimportantIs the payment systemsocially significantThe coincidence of theinfrastructure(completely differentoperators)The coincidence of theinfrastructure (theOperator of the paymentsystem performs one ofthe functions of theOperator)Organization of riskInterpretationThe higher the work experiencerequirement, the higher thestability of the paymentinfrastructure service operatorThe shorter the recovery time,the greater the resilienceThe use increases stabilityThe use increases stabilityThe use increases stabilityThe use increases stabilityThe use increases stabilityService increases resilienceThis category is an indicator ofhigh stabilityAt least the operator of paymentsystem performs one of thefunctions of the operators (butnot completely).
0-no, 1-YesThis category is an indicator ofhigh stabilityIf all operators of paymentinfrastructure services are notaffiliated, the probability ofstability violation increasesIf all operators of paymentinfrastructure services are notaffiliated, the probability ofsustainability violation increasesthe operator manages the risk –If the payment system operator15management system1, the operator is distributedamong the rest – 0There is a requirement tothe Operator to provideinformation to theparticipants of thepayment system1-there is a requirement, 0-thereis no requirementmanages risks independently, theprobability of disrupting thesustainability of paymentinfrastructure service operatorsis reducedThe presence of a developedsystem of interaction increasesthe stability of its functioningSource: compiled by the authorBinary probit regressions were constructed in the calculation. Regressionsturned out significant at the 5% level of significance (table 4).Table 4 – Models that determine the institutional factors that reduce / increase theprobability of the operator's exclusion from the payment systemVariable nameFull logisticregressionThe requirement for experience inthe market more than 3 yearsThe requirement for experience inthe market more than 5 yearsRecovery time after a failure of24 hoursRecovery time after an outage of48 hoursFailure recovery time (P1)-4,326319-1,508007*Full logisticregression withsignificant variablesMarginaleffects-1,495034**-0,3009474**-0,1134712-0,4537438-1,359599Time between events (P2)-0,2318881Number of payments made tototal (P3)A measure of availability of thepayment system (P4)Average number of incidents (P5)-0,8193585-1,562291*-0,3144861*-2,524358*-2,640016**-0,53143**3,024545*2,912293*0,5862388**The availability of intraday creditin the payment systemWhether the payment system asnationally importantIs the payment system sociallysignificantThe coincidence of theinfrastructure (completelydifferent operators)The coincidence of theinfrastructure (the Operator of the-1,227465-1,105836*-0,2226026*-1,836232*-1,60364*-0,3228095*5,6122592,265198*0,4559798**0,062833-0,099495916payment system performs one ofthe functions of the operator)Organization of risk managementsystemThere is a requirement to theoperator to provide information tothe participants of the paymentsystemResiduals-0,3005605-0,5444564Number of observations646464Prob>chi20,00110,0000-R-squared0,47360,4384-1,392478*1,413765**0,2845881***-0,300512Source: compiled by the author, stars reflect p-value: *** p<0.01, **p<0.05, *p<0.1The results are significant (figure 3).
The ROC curve also shows a high value of0.500.000.25Sensitivity0.751.000.9015.0.000.250.501 - Specificity0.751.00Area under ROC curve = 0.9015Figure 3 – ROC curve for the model with institutional variablesSource: compiled by the authorAs a result, the indicators that positively and negatively affect the sustainabilityof the payment infrastructure service providers were determined (figure 4).17The system has overdraftP3 is usedOperators are all differentP4 is usedExperience more than 5 yearsThe operator manages the risks himselfP5 is usedPS id social significant-60,00%-40,00%-20,00%0,00%20,00%40,00%60,00%PS id social significantP5 is usedThe operator manages the risks himselfExperience more than 5 yearsP4 is usedOperators are all differentP3 is usedThe system has overdraft80,00%Figure 4 – Factors affecting the decrease / increase in the probability of exclusion from the Register ofpayment system operatorsSource: constructed by the author of the workThe factors that have a positive impact on the sustainability: the fact that thepayment system is socially significant, the presence of a requirement for the experienceof operators for more than 5 years, the use of the availability index, the number ofsuccessful payments, the presence of intraday lending mechanism.
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