Search Results (12)

Information asymmetry between banks and enterprises in the credit market is essentially the microfoundation of financial risk generation. The frequent occurrence of corporate debt defaults, mainly due to the behavior of short-term debt for long-term use (hereinafter referred to as “SDLU”), further aggravates the contagion path from individual liquidity crisis to systemic repayment crisis. In order to test whether bank financial technology (hereinafter referred to as “BankFintech”) can mitigate SDLU and reduce the possibility of financial risks, this study matched the loan data of China’s A-share listed companies with the patent data of bank-invented Fintech from 2013 to 2022 to construct the BankFintech Development Index for empirical analysis. The empirical results show that the development of BankFintech can significantly inhibit SDLU. The mechanism test reveals that BankFintech reduces bank credit risk and liquidity risk by lowering firms’ risk-weighted assets, improving capital adequacy and liquidity ratios, tilts banks’ lending preferences toward duration-matched long-term financing, and “forces” enterprises to take the initiative to improve their financial health and information transparency, enhance their ability to obtain long-term loans, and realize the active management of mismatch risk. Heterogeneity analysis finds that the effect is more significant in non-state-owned enterprises and technology-intensive industries. Further analysis shows that the level of enterprise digitization, the intensity of financial regulation, and related financial policies significantly moderate the marginal effect between the two. This study verified the “Porter’s Risk Mitigation Hypothesis” of Fintech, providing empirical evidence for effectively cracking the financial vulnerability caused by debt maturity mismatch and deepening financial supply-side reform. Full article

Credit scoring is a cornerstone of financial risk management, enabling financial institutions to assess the likelihood of loan default. However, widely recognized contemporary credit risk metrics, like FICO (Fair Isaac Corporation) or Vantage scores, remain proprietary and inaccessible to the public. This study aims to devise an alternative credit scoring metric that mirrors the FICO score, using an extensive dataset from Lending Club. The challenge lies in the limited available insights into both the precise analytical formula and the comprehensive suite of credit-specific attributes integral to the FICO score’s calculation. Our proposed metric leverages basic information provided by potential borrowers, eliminating the need for extensive historical credit data. We aim to articulate this credit risk metric in a closed analytical form with variable complexity. To achieve this, we employ a symbolic regression method anchored in genetic programming (GP). Here, the Occam’s razor principle guides evolutionary bias toward simpler, more interpretable models. To ascertain our method’s efficacy, we juxtapose the approximation capabilities of GP-based symbolic regression with established machine learning regression models, such as Gaussian Support Vector Machines (GSVMs), Multilayer Perceptrons (MLPs), Regression Trees, and Radial Basis Function Networks (RBFNs). Our experiments indicate that GP-based symbolic regression offers accuracy comparable to these benchmark methodologies. Moreover, the resultant analytical model offers invaluable insights into credit risk evaluation mechanisms, enabling stakeholders to make informed credit risk assessments. This study contributes to the growing demand for transparent machine learning models by demonstrating the value of interpretable, data-driven credit scoring models. Full article

This study aims to identify the model that best approximates the credit spread that should be fixed on debt instruments issued by both companies listed on the Mexican Stock Market, considering the particularities of the Mexican market. Five models were analyzed: Merton’s model, Brownian Motion Model, Power Law Brownian Motion Model, Bloomberg’s model, and the model presented in this paper, which includes the conformable derivatives, taking as a reference the change in the variable as other authors have done, and the Bloomberg corporate default risk model (DRSK) for publics firms. We concluded that the modified Merton model approximates, to a greater extent, the credit spreads that fix on a prime rate on the loans granted to Mexican non-financial companies. Full article

Modelling the outcome after loan default is receiving increasing attention, and survival analysis is particularly suitable for this purpose due to the likely presence of censoring in the data. In this study, we suggest that the time to loan write-off may be influenced by latent competing risks, as well as by common, unobservable drivers, such as the state of the economy. We therefore expand on the promotion time cure model and include a parametric frailty parameter to account for common, unobservable factors and for possible observable covariates not included in the model. We opt for a parametric model due to its interpretability and analytical tractability, which are desirable properties in bank risk management. Both a gamma and inverse Gaussian frailty parameter are considered for the univariate case, and we also consider a shared frailty model. A Monte Carlo study demonstrates that the parameter estimation of the models is reliable, after which they are fitted to a real-world dataset in respect of large corporate loans in the US. The results show that a more flexible hazard function is possible by including a frailty parameter. Furthermore, the shared frailty model shows potential to capture dependence in write-off times within industry groups. Full article

Based on the perspective of investment and financing term structure, this study verifies that local government debt crowds out bank loans available to corporates, resulting in corporate maturity mismatch between investment and financing, namely, short-term financing for long-term investment. According to our heterogeneity analyses, the real impact of local government debt on maturity mismatch between investment and financing is more pronounced for non-state-owned enterprises and firms with high financing demand, located in cities with more local government debt and low financial development. Furthermore, our study reveals that local government debt and corporate maturity mismatch between investment and financing bring about underinvestment and default risk, which ultimately affects local sustainable economic development. This research contributes to the literature on Chinese-specific maturity mismatches. Full article

In this study, we design stepwise ordinary least squares regression models using various amalgamations of firm features, loan characteristics and macroeconomic variables to forecast workout recovery rates for defaulted bank loans for private non-financial corporates under downturn conditions in Zimbabwe. Our principal aim is to identify and interpret the determinants of recovery rates for private firm defaulted bank loans. For suitability and efficacy purposes, we adopt a unique real-life data set of defaulted bank loans for private non-financial firms pooled from a major anonymous Zimbabwean commercial bank. Our empirical results show that the firm size, the collateral value, the exposure at default, the earnings before interest and tax/total assets ratio, the length of the workout process, the total debt/total assets ratio, the ratio of (current assets–current liabilities)/total assets, the inflation rate, the interest rate and the real gross domestic product growth rate are the significant determinants of RRs for Zimbabwean private non-financial firm bank loans. We reveal that accounting information is useful in examining recovery rates for defaulted bank loans for private corporations under distressed financial and economic conditions. Moreover, we discover that the prediction results of recovery rate models are augmented by fusing firm features and loan characteristics with macroeconomic factors. Full article

This paper contributes to the supply chain finance literature with an agent-based Monte Carlo simulation model focusing on the bank’s point of view. Our theoretical model assesses how a bank should screen a supply chain (SC) member and whether that requires different considerations and monitoring systems compared with traditional corporate loans. In the model, the SC members may cooperate, reducing their bankruptcy risk considerably; thus, the chance for and extent of inter-entity financial aid are critical to consider when assessing bankruptcy risk. A cooperative SC member cannot just be financed from debt taken by other members, but it may also offer protection to other SC members using its operating cash flow. Thus, based on our results, bankruptcy risk is SC-specific, rather than a characteristic of an individual firm. Therefore, to finance an SC member is a quasi-joint decision of its peers, so particular care should be paid to estimating and monitoring the correlations between the operational cash flows of cooperative SC members. One of the key results is that of edge default exposure of the bank; it might be optimal to limit the amount of the loan made available to a given collaborative SC member instead of charging higher rates or financing the most attractive SC member only. Another SC member offering an additional guarantee with its assets will provide the remaining need for financing. As this solution also reduces the total bankruptcy risk of the SC, the SC itself should prefer this financing structure. Full article

This paper aims to compare the accuracy of financial ratios, tax arrears and annual report submission delays for the prediction of bank loan defaults. To achieve this, 12 variables from these three domains are used, while the study applies a longitudinal whole-population dataset from an Estonian commercial bank with 12,901 observations of defaulted and non-defaulted firms. The analysis is performed using statistical (logistic regression) and machine learning (neural networks) methods. Out of the three domains used, tax arrears show high prediction capabilities for bank loan defaults, while financial ratios and reporting delays are individually not useful for that purpose. The best default prediction accuracies were 83.5% with tax arrears only and 89.1% with all variables combined. The study contributes to the extant literature by enhancing the bank loan default prediction accuracy with the introduction of novel variables based on tax arrears, and also by indicating the pecking order of satisfying creditors’ claims in the firm failure process. Full article

This work aims to illustrate an advanced quantitative methodology for measuring the credit risk of a loan portfolio allowing for diversification effects. Also, this methodology can allocate the credit capital coherently to each counterparty in the portfolio. The analytical approach used for estimating the portfolio credit risk is a binomial type based on a Monte Carlo Simulation. This method takes into account the default correlations among the credit counterparties in the portfolio by following a copula approach and utilizing the asset return correlations of the obligors, as estimated by rigorous statistical methods. Moreover, this model considers the recovery rates as stochastic and dependent on each other and on the time until defaults. The methodology utilized for coherently allocating credit capital in the portfolio estimates the marginal contributions of each obligor to the overall risk of the loan portfolio in terms of Expected Shortfall (ES), a risk measure more coherent and conservative than the traditional measure of Value-at-Risk (VaR). Finally, this advanced analytical structure is implemented to a hypothetical, but typical, loan portfolio of an Italian commercial bank operating across the overall national country. The national loan portfolio is composed of 17 sub-portfolios, or geographic clusters of credit exposures to 10,500 non-financial firms (or corporates) belonging to each geo-cluster or sub-portfolio. The outcomes, in terms of correlations, portfolio risk measures and capital allocations obtained from this advanced analytical framework, are compared with the results found by implementing the Internal Rating Based (IRB) approach of Basel II and III. Our chief conclusion is that the IRB model is unable to capture the real credit risk of loan portfolios because it does not take into account the actual dependence structure among the default events, and between the recovery rates and the default events. We underline that the adoption of this regulatory model can produce a dangerous underestimation of the portfolio credit risk, especially when the economic uncertainty and the volatility of the financial markets increase. Full article

The well-known subprime mortgage crisis, which began to manifest in early 2007, since when the effects of the speculative bubble begin to become evident from the increase in default rates in residential mortgages, has triggered a global crisis that has pushed various legislations over time to implement a series of financial reforms with the specific objective of avoiding that similar phenomena could be repeated over time. The ability to repay a loan is strongly influenced by the amortization algorithm that the bank has decided to adopt. This appears even more evident in variable interest rate loans since, as the economic conditions of the indexation parameter change, the definition of the loan balance and the related portion of interest will be decisive in relation to the borrower’s ability to repay the loaned capital. A study of the main amortization algorithms and the related descriptions in the bank contracts will allow us to show which are the main issues due to an information asymmetry that, unfortunately, characterizes this type of contract and would seem to be one of the main reasons that lie at the root of the aforementioned crisis of subprime mortgages in the USA. Moreover, the authors will provide a clear analysis of the financial indicators usually reported in loan contracts and how often these indications are insufficient to characterize the actual cost of the loan. Furthermore, by highlighting the discretionary choice that banks often obtain following the contractual loan schemes commonly offered to retail and corporate clients, we will show how this often translates into greater cost to the borrower. Finally, we will propose two possible solutions to the problems highlighted, thus allowing us to reduce this information gap, which unfortunately translates into greater costs for customers with the associated increase in default rates, or the so-called nonperforming loan (NPLs) contracts. Therefore, the objective of this contribution is to show which are the most critical aspects of the bank contracts related to contractual transparency and to the presence or otherwise of hidden costs, i.e., not expressly shown in the contract. Specifically, we refer to the loan contracts issued in Italy both with reference to the local banking legislation and to the European one to which Italy must often refer. Full article

In this paper, we develop a contingent claim model to evaluate a bank’s equity and liabilities that integrates the premature default risk conditions with loan rate-setting behavioral mode and multiple shadow banking activities under capital regulation. The barrier options theory of corporate security valuation is applied to the contingent claims of a bank. The barrier reports that default can occur at any time before the maturity date. We focus on a type of earning-asset portfolio, consisting of balance-sheet banking activities of loans and liquid assets and shadow banking activities of wealth management products (WMPs) and entrusted loans (ELs). The optimal bank interest margin, i.e., the spread between the loan rate and the deposit rate, is derived and analyzed. The results provide an alternative explanation for the decline in bank interest margins, which better fits the narrative evidence on bank spread behavior under capital regulation in particular during a financial crisis. Raising either WMPs or ELs leads to a transfer of wealth from equity holders to the debt holders, and hence increases the deposit insurance liabilities. We also show that the multiple shadow banking activities of WMPs and ELs captured by scope equities may produce superior return performance for the bank. Tightened capital requirements may reinforce the superior return performance by a surge in shadow banking activities that makes the bank less prudent and more prone to risk-taking at a reduced margin, thereby adversely affecting banking stability. We demonstrate that financial disturbance may be created because of the potential for shadow banking activities to spill over to regular banking activities and damage the real economy. Full article

This paper principally aims at examining the impact of capital requirements regulation on bank operating efficiency in Tanzania. The study employs bank level data for the period between 2009 and 2015. The findings show a positive and significant relationship between capital ratio and bank operating efficiency. This shows that commercial banks in Tanzania with more stringent capital regulations are more operationally efficient. This relationship proposes that capital adequacy does not only strengthen financial stability by providing a larger capital cushion but also improves bank operating efficiency by preventing a moral hazard problem between shareholders and debt-holders. This result may also imply that the increased regulations on capital requirements influence the bank’s decision to revisit their internal operations strategy in terms of strong corporate governance, risk assessment methods, credit evaluation procedures, employment of more qualified staffs, and enhanced internal control procedures. Another key finding is an inverse relationship between non-performing Loans (credit risk) and bank operating efficiency. The implication of this relationship may simply mean that the bank’s total loan and advances in combination with total deposit either due from customers or from other banks are of little importance in determining the operational efficiency of banks. This probably implies that the amount of money banks loan out is too excessive, which would attract a greater chance of default. The paper lays down some recommendations: first, banks in Tanzania are advised to invest in more advanced technological innovations to reduce the staff costs and other operating expenses to increase their operational efficiency; and, second, bank management is also advised to be more careful in the loan screening process to reduce the incidence of non-performing loans. Full article