Default Decisions Research Articles

Sequential three-way decision with automatic threshold learning for credit risk prediction

Machine learning algorithms treat credit risk prediction as a binary classification problem. However, two-way decisions with a single threshold force to make either a default or non-default decision may be inappropriate. To reduce the risk of decision errors, this study introduces three-way decisions and proposes a sequential three-way decision model with automatic threshold learning to evaluate credit risk. Initially, the model uses the loan amount and interest to determine the decision loss of the three-way decision, assigning distinct decision thresholds to different samples. Subsequently, the model employs decision cost and information gain to formulate an objective for threshold optimisation. Finally, the model continuously optimises the classification process by using the outcomes of certain decisions as supplementary information. In addition, to validate our model, we conduct comparative experiments with various methods on a real credit dataset from a Chinese bank. The results indicate that the model not only enhances classification performance across several metrics but also assists financial institutions in reducing decision error costs.

Trade‐Off Theory for Dual Holders

AbstractA dual holder simultaneously owns (private) debt and equity in the same firm. Private debt has a tax advantage, a positive cashflow, which incentivizes its use. This cashflow leads to a lower net cost of debt, which again reduces default risk as well as the cost of external debt. The usual trade‐off between tax benefits and bankruptcy costs is altered. Debt priority affects both financing and default decisions. We find that an enterprise‐value maximizing firm should issue senior, external debt and junior, private debt, rather than debt with pari‐passu priority. Our analysis further highlights that tax authorities can effectively curtail the tax‐motivated use of private debt through straightforward measures.

A realist synthesis of prospective entrustment decision making by entrustment or clinical competency committees.

The real-world mechanisms underlying prospective entrustment decision making (PEDM) by entrustment or clinical competency committees (E/CCCs) are poorly understood. To advance understanding in this area, the authors conducted a realist synthesis of the published literature to address the following research question: In E/CCC efforts to make defensible prospective entrustment decisions (PEDs), what works, for whom, under what circumstances and why? Realist work seeks to understand the contexts (C), mechanisms (M) and outcomes (O) that explain how and why things work (or do not). In the authors' study, contexts included individual E/CCC members, E/CCC structures and processes, and training programmes. The outcome (i.e. desired outcome) was a PED. Mechanisms were a substantial focus of the analysis and informed the core findings. To define a final corpus of 52 included papers, the authors searched four databases, screened all results from those searches and performed a full-text review of a subset of screened papers. Data extraction focused on developing context-mechanism-outcome configurations from the papers, which were used to create a theory for how PEDM leads to PEDs. PEDM is often driven by default (non-deliberate) decision making rather than a deliberate process of deciding whether a trainee should be entrusted or not. When defaulting, some E/CCCs find red flags that sometimes lead to being more deliberate with decision making. E/CCCs that seek to be deliberate describe PEDM that can be effortful (when data are insufficient or incongruent) or effortless (when data are robust and tell a congruent story about a trainee). Both information about trainee trustworthiness and the sufficiency of data about trainee performance influence PEDM. Several moderators influence what is considered to be sufficient data, how trustworthiness data are viewed and how PEDM is carried out. These include perceived consequences and associated risks, E/CCC member trust propensity, E/CCC member personal knowledge of and experience with trainees and E/CCC structures and processes. PEDM is rarely deliberate but should be. Data about trainee trustworthiness are foundational to making PEDs. Bias, equity and fairness are nearly absent from the papers in this synthesis, and future efforts must seek to advance understanding and practice regarding the roles of bias, equity and fairness in PEDM.

Prepayment and Default Risks of Mortgage-Backed Security Collateral Pools

Mortgage-backed securities (MBS) are structured financial products that are produced via securitization of mortgage loans. Due to the nature of securitization, all risks of mortgage loans are transferred from originators to MBS investors. Prepayment and default risks of mortgages lead to uncertainty in MBS cash flows and create a complex problem for valuation of these instruments. Therefore, estimating these mortgage termination risks has become the focus of valuation of MBS collateral pools. This study explores two questions by using a publicly open dataset provided by Fannie Mae. First, two machine learning algorithms (Random Forest and Multinomial Logit Regression) are used for classification to predict whether a mortgage loan is likely to be prepaid, defaulted or current. Afterwards, Competing Risks Cox Regression Analysis is performed to see determinants of when mortgage termination risks are likely to happen. It is found that not all mortgage borrowers behave optimally in their prepayment and default decisions. Therefore, in addition to refinancing incentive and negative equity which depend on variations in prevailing mortgage interest rates and housing prices, heterogeneity in mortgage borrowers’ behaviors and loan characteristics, and also local economic factors are significantly important in estimating mortgage termination risks. It is worth noting that prominence role of mortgage payment delinquencies in particularly predicting defaults emphasizes the essential need of monitoring payments by servicers to keep safety of MBS investors and financial markets.

PERTANGGUNGJAWABAN PERDATA DALAM AKTA PERDAMAIAN (Studi Putusan PN Karawang Nomor 81/PDT.G/2022 PN Kwg)

This research aims to analyze the Deed of Peace in the Karawang District Court Decision Number 81/PDT.G/2022/PN Kwg based on the legal requirements of the Deed of Peace in the Civil Code. This research method uses a normative juridical method that focuses on studying and examining the law as norms, rules, legal principles, principles, laws, legal doctrines, legal theories. The type of data used in this research is secondary data, through primary legal sources in the form of laws and regulations and secondary legal materials in the form of books and related legal scientific articles. The results showed that the Deed of Settlement of PT Pupuk Kujang as the Plaintiff with PT. Dinamika Kembar Utama refers to the return of the Defendant's debt to the Plaintiff and is made not contrary to laws and regulations, decency, or public interest.Keywords: Deed of Peace, Default, and Court Decision.

Dynamic banking with non-maturing deposits

The majority of bank liabilities are deposits typically not withdrawn for extended periods. We propose a dynamic model of banks in which depositors forecast banks' leverage and default decisions, and withdraw optimally by trading off current against future liquidity needs. Endogenous deposit maturity creates a time-varying dilution problem that has major effects on bank dynamics. Interest rate cuts produce delayed increases in bank risk which are stronger in low rate regimes. Deposit insurance can exacerbate the deposit dilution and amplify the increase in bank risk.

Sovereign Debt and Credit Default Swaps

How do credit default swaps (CDS) affect sovereign debt markets? The answer depends crucially on trading frictions, risk-sharing, arbitrage violations, and spillovers from secondary to primary markets. We propose a sovereign default model where investors trade bonds and CDS over the counter via directed search. CDS affect bond prices through several channels. First, CDS act as a synthetic bond. Second, CDS reduce bond-investing risks, allowing exposure to be unwound. Third, CDS availability increases trading profitability, which induces entry and reduces trading costs. Last, these direct effects feedback into default decisions. Our novel identification strategy exploits confidential microdata to quantify the extent of trading frictions and risk-sharing. The model generates realistic CDS-bond basis deviations, bid/ask spreads, and CDS volumes and positions. Our baseline specification predicts large effects of frictions generally but small spillovers from a naked CDS ban. These predictions hinge crucially on the identified parameters.

Firms' financial structure with contingent convertible debt, risky debt and multiple growth options

PurposeThe objective of this paper is twofold: first, to model the value of the firm in the presence of contingent capital and multiple growth options over its life cycle in a stochastic universe to ensure financial stability and recover losses in case of default and second, to clarify how contingent convertible (CoCo) bonds as financial instruments impact the leverage-ratio policies, inefficiencies generated by debt overhang and asset substitution for a firm that has multiple growth options. Additionally, what is its impact on investment timing, capital structure and asset volatility?Design/methodology/approachThe current paper elaborates the modeling of a dynamic problem with respect to the interaction between funding and investment policies during multiple sequential investment cycles simultaneously with dynamic funding. The authors model the value of the firm in the presence of contingent capital that provides flexibility in dealing with default risks as well as growth options in a stochastic universe. The authors examine the firm's closed-form solutions at each stage of its decision-making process before and after the exercise of the growth options (with and without conversion of CoCo) through applying the backward indication method and the risk-neutral pricing theory.FindingsThe numerical results show that inefficiencies related to debt overhang and asset substitution can go down with a higher conversion ratio and a larger number of growth options. Additionally, the authors’ analysis reveals that the firm systematically opts for conservative leverage to minimize the effect of debt overhang on decisions so as to exercise growth options in the future. However, the capital structure of the firm has a substantial effect on the leverage ratio and the asset substitution. In fact, the effect of the leverage ratio and the risk-shifting incentive will be greater when the capital structure changes during the firm's decision-making process. Contrarily to traditional corporate finance theory, the study displays that the value of the firm before the investment expansion decreases and then increases with asset volatility, instead of decreasing overall with asset volatility.Research limitations/implicationsThe study’s findings reveal that funding, default and conversion decisions have crucial implications on growth option exercise decisions and leverage ratio policy. The model also shows that the firm consistently chooses conservative leverage to reduce the effect of debt overhang on decisions to exercise growth options in the future. The risk-shifting incentive and the debt overhang inefficiency basically decrease with a higher conversion ratio and multiple growth options. However, the effect of the leverage ratio and the risk-shifting incentive will be greater when the capital structure changes during the firm's decision-making process.Originality/valueThe firm's composition between assets in place and growth options evolves endogenously with its investment opportunity and growth option financing, as well as its default decision. In contrast to the standard capital structure models of Leland (1994), the model reveals that both exogenous conversion decisions and endogenous default decisions have significant implications for firms' growth option exercise decisions and debt policies. The model induces some predictions about the dynamics of the firm's choice of leverage as well as the link between the dynamics of leverage and the firm's life cycle.

Fuzzy Gaussian Decision Tree

The Decision Tree algorithm is one of the first machine learning algorithms developed. It is used both as a standalone model and as an ensemble of many cooperating trees like Random Forest, AdaBoost, Gradient Boosted Trees, or XGBoost. In this work, a new version of the Decision Tree was developed for classifying real-world signals using Gaussian distribution functions and a fuzzy decision process. The research was carried out on Power Quality Classification Dataset hosted on the platform kaggle.com for accessing the algorithm’s classification. The proposed algorithm modification produces a sparse tree of multidimensional Gaussian kernels performing fuzzy, proximity-based division of solution space instead of a typical Decision Tree performing definite space restrictions. The machine learning model can cluster samples based on the common pattern and evaluate the input’s similarity in a fuzzified fashion. The studies were conducted for the prediction of 6 classes of current. The proposed modification achieved the best accuracy and F1 score compared to the default Decision Tree and other machine learning algorithms.

The Employment Cost of Sovereign Default

This paper analyzes the interaction between government default decisions and labor market outcomes in an environment with persistent unemployment and financial frictions. Sovereign risk impairs bank intermediation through balance sheet effects, worsening the conditions for firms to pre-finance wages and vacancies. This generates a new type of endogenous domestic default cost -- the employment cost of default. The persistence of unemployment produces serial defaults and rationalizes high debt-to-GDP ratios. In the dynamic strategic game between the government and the private sector, anticipation effects allow the study of debt crises in addition to outright default episodes. Introducing employment subsidies and bank regulations affect the government's ability to commit to debt repayment.

The Impact of the COVID-19 Pandemic on Uptake of Routine Childhood Immunization: How a Behavioral Science Led Analysis Can Strengthen Vaccination Policy

In addition to the direct health and economic impacts of the COVID-19 pandemic, routine immunization of children for vaccine-preventable diseases has also suffered. Quantitative studies have shown evidence of backsliding in routine immunization, i.e., children failing to receive their vaccinations on schedule. Pandemic restrictions were the cause for some of this behavior, and there are signs that subsequent campaigns and supply-side initiatives have influenced trends toward routine immunization catch-up. However, limited qualitative research indicates that the pandemic may have disrupted parents/caregivers’ decision-making processes around routine immunization, and their attitudes toward vaccination, in ways that could have long-term effects on rates of immunization. Further behavioral research can help elucidate this and provide guidance to policymakers to boost immunization uptake. This policy article explores the decision dynamics leading to missed routine immunization doses during the pandemic, through an analysis of secondary literature, a summary of primary-research studies on COVID-19 vaccine hesitancy conducted during the pandemic across Côte d’Ivoire, Burkina Faso, Kenya and Pakistan, and a summary of a study on the impact of COVID-19 on routine immunization uptake in South Africa. We found that the COVID-19 pandemic severely disrupted prior patterns of engagement with the health system, turning routine immunization from a default decision for parents/caregivers into an “active decision-making” moment. Restrictions on movement, limited routine immunization supplies and other practical impediments combined with fear of contracting COVID-19 at health facilities, misinformation, and fear of vaccine side-effects, creating an emergent behavior of missed immunization doses during the pandemic period. We also found that hesitancy about COVID-19 vaccines may be transferred onto previously accepted vaccines, impacting routine immunization. We identified three decision-making pathways for routine immunization uptake among South African parents/caregivers once the lockdowns were lifted: the “path of procrastination,” the “path of doubt,” and the “path of persistence.” We highlight the policy implications of these, and suggest potential interventions to increase routine immunization uptake for parents/caregivers on each path, as well as for general communications, service delivery, and pandemic readiness.

How default effects and decision timing affect annuity uptake and health consciousness

Annuities provide a lifelong income stream and can therefore help individuals to mitigate the risk of outliving their savings, a highly topical issue in the context of increasing life expectancies. Given that real-world annuitization rates remain relatively low, we investigate the influence of behavioral biases on people’s choice between a lifelong annuity and a lump sum payout. In so doing, we focus on the impact of default effects due to a preselected annuity option (default option) and the impact of the decision’s timing (decisions on annuitization taken by younger individuals refer to a distant future, those taken by older individuals to a near future) on annuity uptake and health consciousness. We used a scientific survey panel to conduct an online experiment with a diversified sample of 339 participants (55.2% female, average age = 42.5 years). Our results show that the timing of the decision moderates the default effect on annuity uptake, in that the effect of a preselected (default) annuity option is stronger for distant-future decisions (i.e., choice of annuity instead of lump sum at retirement made by younger participants purchasing a deferred annuity) than for near-future decisions made by older individuals who are closer to retirement. We further find that the default effect moderates health consciousness after choosing an annuity. Health consciousness is stronger in the no-default condition than in the annuity default condition.

On advance payments in tenders with budget constrained contractors

AbstractWe consider procurement auctions in settings where potential contractors have limited funds and face ex post risks, that is, cost overruns that can lead to the contractors' defaults. The bidding strategies and the default decisions are shaped by the incentive—willing to finish—constraints, and the resource—able to finish and feasibility—constraints. We examine payment schemes where the advance share of the award is paid immediately after the auction with the rest payable upon successful completion of the project. We identify the trade‐offs the procurer faces when choosing among such schemes, and explore the factors that influence the optimal advance share.

Economic evaluation of legislation-based public health interventions – the case of tobacco control

Abstract Economic evaluation of public health interventions have mostly been performed for interventions whose cost and effectiveness can be well identified, such as protective interventions (vaccination or screening), clinical interventions (prevention drugs) or counselling and education programs. Yet, following the Thomas Frieden's health impact pyramid, these public health interventions are less effective than those “changing the context to make individuals’ default decision healthy”. The economic evaluation of population-based interventions that modify the context is rather challenging. First, such interventions include various components, e.g., taxes and subsidies, media campaigns or bans. Second, since they correspond to political decisions applied at a wide scale, their effectiveness can hardly be measured using experimental designs. Third, the cost of such interventions is difficult to assess, since they are not represented by a specific product or service, but rather by political decisions and their implementation, whose costing is not straightforward. Using the case of tobacco control policies, we show how alternative methods can be used to assess their economic value. We show that quasi-experimental methods with country comparisons allow identify the effectiveness of specific policies, and that qualitative approaches are needed to quantify their costs. Results indicate that tobacco-control policies have low costs and are highly effective, so that their cost-effectiveness is quite favorable in light of commonly referred thresholds, and in comparison with widely financed clinical interventions. Population-based interventions that focus contextual factors must also be carefully evaluated from an economic viewpoint. Although economic evaluations are challenging, alternative approaches help obtain results that are valuable for decision making.

Automated identification of innocent Still's murmur using a convolutional neural network.

BackgroundStill's murmur is the most prevalent innocent heart murmur of childhood. Auscultation is the primary clinical tool to identify this murmur as innocent. Whereas pediatric cardiologists routinely perform this task, primary care providers are less successful in distinguishing Still's murmur from the murmurs of true heart disease. This results in a large number of children with a Still's murmur being referred to pediatric cardiologists.ObjectivesTo develop a computer algorithm that can aid primary care providers to identify the innocent Still's murmur at the point of care, to substantially decrease over-referral.MethodsThe study included Still's murmurs, pathological murmurs, other innocent murmurs, and normal (i.e., non-murmur) heart sounds of 1,473 pediatric patients recorded using a commercial electronic stethoscope. The recordings with accompanying clinical diagnoses provided by a pediatric cardiologist were used to train and test the convolutional neural network-based algorithm.ResultsA comparative analysis showed that the algorithm using only the murmur sounds recorded at the lower left sternal border achieved the highest accuracy. The developed algorithm identified Still's murmur with 90.0% sensitivity and 98.3% specificity for the default decision threshold. The area under the receiver operating characteristic curve was 0.943.ConclusionsStill's murmur can be identified with high accuracy with the algorithm we developed. Using this approach, the algorithm could help to reduce the rate of unnecessary pediatric cardiologist referrals and use of echocardiography for a common benign finding.

Probability matching is not the default decision making strategy in human and non-human primates

Probability matching has long been taken as a prime example of irrational behaviour in human decision making; however, its nature and uniqueness in the animal world is still much debated. In this paper we report a set of four preregistered experiments testing adult humans and Guinea baboons on matched probability learning tasks, manipulating task complexity (binary or ternary prediction tasks) and reinforcement procedures (with and without corrective feedback). Our findings suggest that probability matching behaviour within primate species is restricted to humans and the simplest possible binary prediction tasks; utility-maximising is seen in more complex tasks for humans as pattern-search becomes more effortful, and we observe it across the board in baboons, altogether suggesting that it is a cognitively less demanding strategy. These results provide further evidence that neither human nor non-human primates default to probability matching; however, unlike other primates, adult humans probability match when the cost of pattern search is low.

Does Religion Affect Mortgage Delinquency?

We provide evidence that religiosity deters mortgage delinquency. Religiosity is the percentage of the total county population who adheres to a religion. The results show that locations with a higher level of religiosity have a significantly lower mortgage delinquency rate after controlling for income, employment, education, population, etc. A one-standard-deviation increase in religiosity in a county leads to almost a 0.096 standard-deviation decrease in the mortgage delinquency rate, which corresponds to nearly 9.1% (12.3%) of the sample average (median) mortgage delinquency rate. The impact of religiosity increases during and after the global financial crisis period. Previous studies in the literature indicate that religiosity is related to a preference to save, higher aversion to risk as well as moral values towards mortgage default, all of which are linked to mortgage default decisions. This new evidence suggests the role of local religiosity in evaluating and modeling mortgage default risk.

Welfare implications of bank capital requirements under dynamic default decisions

I study capital requirements and their welfare implications in a dynamic general equilibrium model of banking. The model is characterized by two features. First, banks choose entry and exit, which allows the number of banks change endogenously. Second, since equity issuance is costly, banks precautionarily hold capital buffers against future liquidity shocks. I find that the optimal capital requirement that maximizes social welfare is larger than 3.5%, and the value sensitively depends on the size of utility households derive from deposit holdings. In a special case without any utility from deposit holdings, I obtain welfare implications that are consistent with Corbae and D’Erasmo (2021a).

Resolution of financial crises

A financial crisis creates substantial wealth losses. How these losses are allocated determines the magnitude of the crisis and the path to recovery. We study how institutions and technological factors that shape default and debt restructuring decisions affect the amplification of aggregate shocks. For sufficiently large shocks, agents renegotiate. This limits the losses borne by borrowers, shutting the amplification mechanism via asset prices. The range of shocks that trigger renegotiation is decreasing in repossession costs and increasing in default costs, if the latter are public information. Private information may induce equilibrium default but, by allowing agents with high default costs to extract a larger haircut, facilitates the recovery. The model is consistent with evidence from real estate markets in the U.S. during the Great Recession; and rationalizes recent changes in U.S. Bankruptcy Code in the wake of the COVID-19 crisis.

Collateral Value and Strategic Default: Evidence from Auto Loans

I study the impact of changes to collateral value on borrowers’ default decisions on auto loans using two natural experiments in Sri Lanka. Changes in vehicle import tax rates and loan-to-value ratio caps on auto loans generated plausibly exogenous variation in the resale value of vehicles already pledged as collateral. Using proprietary auto loan performance data, I estimate that a 10% drop in the collateral value corresponds to a 44% increase in the default rate. I also find that collateral value is more important for borrowers with higher outstanding loan balances.