Bad Debt Losses Research Articles

Impact of performance commitment in mergers and acquisitions on trade credit policy: evidence from China

ABSTRACT This study examines the incentive effect of performance commitments in mergers and acquisitions and discovers that performance commitments increase the supply of trade credit and that seeking profitability is the impact mechanism. According to our cross-sectional analyses, the positive impact of performance commitment on trade credit is more pronounced for firms located in low-marketization regions with fierce market competition, high corporate risk, low internal control quality, low audit quality, or state-owned enterprises. Furthermore, our study reveals that bad debt losses and earnings management rise in tandem with trade credit. This research contributes to the literature on Chinese-specific earnouts.

Sources of SMEs Financing and Their Impact on Economic Growth across the European Union: Insights from a Panel Data Study Spanning Sixteen Years

Getting access to sufficient funding is the keystone for the development of any business, but especially for small and medium enterprises (SMEs). These economic entities are crucial players in the global economy since they include almost 90% of companies, provide jobs for nearly 50% of the global workforce, and enhance long-term economic growth. In this context, our study explores important sources concerning the financing of small and medium enterprises and their impact on economic growth during the period 2005–2020 with data from SMEs covering the 28 countries belonging to the European Union. The set of predictors included Strength of legal rights index, Days sales outstanding, Bad debt loss, Interest rate, Bank support, Business angels, Private lenders, and Public support. The set of dependent variables included Cost of loans, Equity fund, GDP growth rate, and Value added growth rate. Our methodological approach was complex, it considered a panel data analysis with a first-difference generalized method of moments estimator and a multiplex time series analysis. The novelty of the study resides in combining the two methods in order to investigate significant drivers of economic growth across the EU. Empirical results showed that economic growth was mainly triggered by predictors such as Interest rate, Business angels, Bank support, and Public support. Moreover, the valuable mathematical insights elicited by the multiplex time series analysis suggested that European economies cooperated intensively through SME activities. Based on our empirical results, national and regional authorities should enact adequate policies to support business endeavors of small and medium enterprises.

Credit Card Anti-Fraud Prediction Based on Ensemble earning

In recent years, due to the development of Internet technology in the financial industry, cardless and cashless payments have become increasingly popular. At present, people only need to bind their cards to their cell phones to scan the code for payment. In the meanwhile, as the use of credit cards is vigorously promoted nationwide, younger generations relied more on the credit card debt for consumption. However, although these developments have brought a lot of convenience to people's travel and their life, the virtual nature of the Internet and the "Enjoy First, Pay Later" feature of credit cards have led to a significant increase in potential credit risk in the entire transaction, triggering more and more new credit card fraud incidents. While the overall financial environment in China is relatively healthy, the fact that fraudulent behaviors such as credit card overdrafts, counterfeit cards, and credit card frauds are all personal conduct, making it difficult to detect and prevent these individuals from acting illegally in a timely manner. Although fraud represents a small percentage of the overall transaction size, its bad debt losses to merchants and banks can be significant. In order to make a timely prediction of fraud, there are already many single-model machine learning methods such as decision trees and logistic regression. However, the generalization ability of these models is not good enough in the face of complex user behavior features. In addition, since the probability of fraudulent behavior is very small, resulting in few samples that can be trained, even if a model has a high accuracy rate, there is no guarantee that it can accurately predict fraudulent behavior. Therefore, this paper proposes the Adaboost method based on ensemble learning and uses SMOTE oversampling based on a few classes of fraudulent samples of user features to solve the above existing problems.

How Is Gross Profit Margin Overestimated in China?

At present, the index of gross profit margin is overestimated in China. However, this problem has not attracted enough attention. This paper explores the theoretical limitations of the current revenue that lead to the overestimation of the gross profit margin. Then, we present the concept of revenue to correct the limitations of the current revenue. Moreover, we test the impact on the information content of gross profit margin under revenue caliber. The findings are as follows: (1) The current revenue includes some unrealized items such as in-price tax, so it is not completely consistent with the definition of revenue from the perspective of accounting, which will lead to the overestimation of gross profit margin. Therefore, the current revenue should exclude the in-price tax, bad debt loss, and cash discount in order to obtain the revenue. The gross profit margin based on the revenue can reflect the profitability of the company’s basic business more objectively. (2) The empirical test shows that the gross profit margin based on the revenue has higher information content compared with the gross profit margin based on the current revenue. (3) The current gross profit margin is overestimated by 1.36 percentage points because of the limitations of the current revenue.

Research on financial early warning of mining listed companies based on BP neural network model

Mining industry is the basic industry of the national economy. However, in recent years, listed mining companies have suffered serious financial risks due to special reasons such as poor spot market liquidity of their products, strong policy dependence, and long investment payback periods. In the previous studies, most of the financial crisis prediction focused on the whole industry and manufacturing industry. The research on the financial risk of mining enterprises focuses more on how to adjust R&D activities, environmental performance to improve the financial performance of enterprises. There is still a lot of room for in-depth research on the systematic prevention and early warning of financial risks of listed mining companies. At the same time, in terms of research methods, many scholars used multivariate discriminant model, logistic regression model and support vector machine model. Compared with the Back-Propagation (BP) neural network model, these model methods have more or less defects. Therefore, we take mining listed companies as the research object, select the financial data of China's A-share mining listed companies in 2018, and construct the BP neural network financial early warning model, trying to provide more practical means for the financial risk early warning of mining companies. The research conclusions of this paper are as follows: (1) The BP neural network financial early warning model constructed in this paper has high prediction accuracy, which can be well used in the practice of financial early warning of mining listed companies; (2) The financial situation of China's A-share mining listed companies in 2018 is generally in a good state. The companies with good financial status can effectively control the cost and have good debt paying ability while earning income; (3) For companies with financial status that require early warning, the root cause is mainly that they do not pay attention to the risk of bad debt losses, which makes current credit sales income and accounts receivable are at high levels, and they also do not have good profitability.

Cost-sensitive learning for profit-driven credit scoring

Application scorecards allow to assess the creditworthiness of loan applicants and decide on acceptance. The accuracy of scorecards is of crucial importance for minimizing bad debt loss and maximizing returns. In this paper, we extend upon prior benchmarking studies that experimentally compare the performance of classification techniques to discriminate between good and bad applications. We evaluate a range of cost-sensitive learning methods in terms of their ability to boost the profitability of scorecards. These methods allow to take into account the variable misclassification costs that are involved in rejecting good loan applications and accepting bad loan applications. An approach is proposed to estimate these misclassification costs, and various approaches to handle missing credit bureau scores are evaluated. The results of a case study involving a Romanian nonbanking financial institution (NBFI) indicate that cost-sensitive learning complements the existing state-of-the-art scorecard of the NBFI. The best performing cost-sensitive models are found to increase profitability across the three business channels, with a single-digit improvement for two of the channels and a double-digit increase for the other one. The result is partly explained by the default rate, which is higher for this latter channel and therefore offers greater potential for improving profitability.

The Effect of Allowance for Bad Debt Loss to the Level of Profitability

SUMMARY Local Bank (BPR) as one of the financial institutions in Indonesia in carrying out its activities collecting funds from the public in the form of savings and deposits and channeling back the funds collected through the provision of credit. This study aims to determine, describe and explain the Effect of Allowance for Bad Debt to the Level of Profitability in Subang BPR of Pabuaran Branch. The method used is descriptive method, and testing of the data - the data studied. The data used is data from the financial statements of PD BPR Subang of Pabuaran Branch in 2012 to 2015 with a monthly ratio reports. Any increase in the value of allowance for bad debt (X) of 1% would cause a rise in the value of profitability level in terms of the comparison of operating cost with operating income (Y) of 0.333% and vice versa. The conclusion is the level of allowance for bad debt does not significantly affect the level of profitability as measured by the comparison of operating expenses to operating income.

Effects of Saving and Credit Cooperative Credit Policies on Members Saving Mobilization in Turkana County, Kenya

The purpose of this study investigated the effects of saving and credit cooperative credit policies on members saving mobilization in Turkana County, Kenya. It was guided by the following research questions: What are the effects of credit period on members saving mobilization? How are the effects of credit policies on members saving mobilization? And to what extent are the effects of collection policy on members saving mobilization? The study was guided by the following theories the 5 C’s Model and Neoclassical Growth Theory. The study employed a descriptive survey design. The study targeted 33 managers from 33 registered Sacco’s of Turkana County, 70 credit officers and 320 members of the SACCOs. To select the representative Sacco’s the researcher used a systematic sampling where each 2nd Sacco’s from the list of the 35 registered Sacco’s in Turkana County is sampled, further the researcher picked all Sacco managers through census methods while for simple random sampling was used to select the credit officers and customers. Questionnaires were used as the main tool for data collection. The inferential statistic multiple regressions were also used to analyze data. Data was then presented in form of tables with frequency and percentages. Credit period Sacco extends credit to customers it recognizes the possibility that customers will be unable to pay or unwilling to pay as his objective. The study found out that Sacco’s must establish policies for determining who will receive credit for how long and how much. On credit standards and members saving mobilization survival of most Sacco’s depends entirely on successful credit standards that revolves on funds and savings made to them by the members. Finally Collection policy the collection effort should, therefore aim at accelerating collections from slow payers and reducing bad debt losses. This study concludes that Saccos must understand not only the risk posed by each credit but also how the risks of individual loans and portfolios are interrelated. This study recommended that a further research to be replicated in the other SACCOs in other counties.

Joint replenishment and credit policies under two levels of trade credit financing when demand and bad-debt loss depends upon credit period

In practice, a firm usually receives trade credit financing from its supplier on the purchase of inventory. Similarly, in order to meet competition and generate credit sales over and above cash sales, the firm also gives credit period to their customers. However, the decision of granting credit period may have a disintegrating effect on cash sales apart from generating new credit sales because some of the cash customers may switch to credit purchase. In addition, despite of the best credit granting policies and collection practices, the firm may incurs some amount of bad debt losses because a certain fraction of buyers will undoubtedly be unable to pay off their debt obligations and become bad debt loss to the firm. In this paper, using discounted cash flow (DCF) approach, a mathematical model is developed to jointly determine optimal inventory and credit policies under two levels of trade credit financing when demand and bad-debt losses are dependent on credit period. The objective of the model is to maximize the present value of firm?s net profit per unit time by jointly optimizing the replenishment interval and date-terms credit period. Numerical examples and sensitivity analysis are presented to illustrate the effectiveness of the proposed model, and the results are discussed.

Retailer’s replenishment and credit policies for deteriorating inventory under credit period-dependent demand and bad-debt loss

The business world survives on trade credit financing. The three players, viz. supplier, retailer, and customer, are building blocks of any business firm. The retailer receives credit period from the supplier to settle the accounts due against the purchases. For retailer, the customer is a prominent stakeholder. To withstand the competitive market and generate credit sales and cash sales, the retailer offers credit period to attract more customers. This will boost the demand for the retailer and consequently the supplier. The offer of credit period by the result may result in bad-debt loss when the customer declares incapability to do the payment. The situation worsens when the items in the inventory system are subject to deterioration. In this article, a mathematical model is formulated to determine the optimal replenishment time and credit period under two levels of trade credit financing when the demand and bad-debt loss depend on the length of the credit period. The profit per unit time of the retailer is maximized by optimizing the cycle time and date-terms credit period when items in the inventory deteriorate at a constant rate. A numerical example is given to validate the proposed model. Sensitivity analysis is carried out and observations are deduced.

Bad Debt Loss Insurance in Settlement and Litigation

We analyze the impact of Bad Debt Loss Insurance on settlement outcomes. A success in a settlement or trial can turn into a disaster when the defendant goes bankrupt before paying the plaintiff's claim. The starting point of our paper is a simple trial and litigation model with perfect and complete information. We add the possibility of a defendants bankruptcy, and of buying Bad Debt Loss Insurance for both the settlement and the trial stage. We demonstrate that trial insurance and settlement insurance have different impacts on the predicted outcome of settlement negotiations. Trial insurance tends to increase the settlement result; therefore, it generates a contract rent for the insurer and the insured. Settlement insurance, however, has the opposite effect, as it decreases the settlement result.

A COMPARISON OF DATA MINING TECHNIQUES FOR CREDIT SCORING IN BANKING: A MANAGERIAL PERSPECTIVE

Credit scoring is a very important task for lenders to evaluate the loan applications they receive from consumers as well as for insurance companies, which use scoring systems today to evaluate new policyholders and the risks these prospective customers might present to the insurer. Credit scoring systems are used to model the potential risk of loan applications, which have the advantage of being able to handle a large volume of credit applications quickly with minimal labour, thus reducing operating costs, and they may be an effective substitute for the use of judgment among inexperienced loan officers, thus helping to control bad debt losses. This study explores the performance of credit scoring models using traditional and artificial intelligence approaches: discriminant analysis, logistic regression, neural networks and classification and regression trees. Experimental studies using real world data sets have demonstrated that the classification and regression trees and neural networks outperform the traditional credit scoring models in terms of predictive accuracy and type II errors.

The 'Rainmaker's Dilemma': Bad Debt Loss Insurance in Settlement and Litigation

In this paper, we analyze the impact of Bad Debt Loss Insurance on settlement outcomes. A huge success in a settlement or trial may turn into a disaster when the defendant goes bankrupt. Rainmakers face the following dilemma: the greater the success in court, the greater the defendant's bankruptcy risk. The starting point of our paper is a simple trial and litigation model with perfect and complete information. We add the possibility of a defendant's bankruptcy as well as Bad Debt Loss Insurance for both the settlement and the trial stage. We demonstrate that trial insurance and settlement insurance may have different impacts on the outcome of settlement negotiations. Trial insurance tends to increase the settlement result; therefore, it generates a contract rent for the insurer and the insured. Settlement insurance, however, can under certain conditions have the opposite effect: it may decrease the settlement result.

Price Elasticity of Demand and an Optimal Cash Discount Rate in Credit Policy

AbstractWhile the provision of a cash discount is equivalent to a reduction in price, the role of price elasticity of demand in determining credit terms has been neglected in the extant literature. In this paper, this role is investigated and it is shown that the optimal cash discount rate is affected by the price elasticity of demand for the firm's product. The comparative effects on the optimal cash discount rate with respect to exogenous changes in the fraction of credit sales paid after taking cash discount, the cost of short‐term funds and the bad debt loss ratio are investigated. A trade‐off between the time value gain and the price elasticity of demand is established. We find that firms which sell in locations having different price elasticities for their products, and/or which face various costs of short‐term funds in different locations, should vary their cash discount terms accordingly.

Accrual Accounting in a Dynamic Agency Setting

This paper examines the choice of asset valuation rules from a managerial control perspective. A manager creates value for a firm through his effort choice each period. To support its operating activities, the firm also engages in financing transactions such as credit sales to its customers. These financing transactions merely change the pattern of cash flows across periods but have no effect on the value of the firm. An optimal compensation scheme must therefore shield the manager from the risk associated with such transactions. If the firm operates an accrual accounting system in which receivables are capitalized at their fair values, we show that residual income eliminates this risk and provides an optimal performance measure. On the other hand, compensation schemes based only on realized cash flows can be optimal only under exceptional circumstances. The paper also considers a setting in which the principal can observe not only the aggregate cash flow, but also the amount of realized bad debt losses each period. In this setting, an optimal compensation scheme must also shield the manager from the default risk associated with credit transactions. We demonstrate that this can be achieved if receivables are valued according to the allowance method that leads to a proper matching of sales revenues and bad debt expenses each period.

Automating Credit and Collections Decisions at AT&T Capital Corporation

AT&T Capital Corporation, the largest publicly held leasing and financing company in the US, owns and manages over $12 billion in assets. In 1992, we began work to enhance the viability and profitability of its small-ticket business. We designed three sets of decision automation systems and the associated decision strategies for front-end credit decisions, life-cycle credit-line management, and delinquent account collections. The majority (73 percent) of the credit-granting decisions in AT&T Capital's small-ticket business are now made automatically, including $685 million in new transactions annually. Productivity gains have enhanced competitiveness by reducing response times and increased profitability by reducing credit and collections operating costs by over $3.1 million annually. Finally, improvements in decision quality have led to business volume gains of $86 million annually, while reducing bad debt losses by $1.1 million annually.

A Structured Sensitivity Analysis for a Markov Model of Accounts Receivable

We develop algebraic methods for a general sensitivity analysis of the classic Cyert-Davidson-Thompson model of a system of accounts receivable, and use the resulting analytical relationships to investigate the stability of estimates for the losses from doubtful accounts (bad debts) and the present value of interest income generated by charges to the accounts. The limits of applicability of our results are examined by means of a comprehensive numerical analysis of the realizable values of the coefficients in the derived relationships from a four-state model. It was found that neither bad debt losses nor interest income are overly sensitive, in general, to changes in the probabilities of transition between the account age categories, but that some of these probabilities are more critical than others. It was also found that a given percentage increase in the new charge growth rate usually produces approximately the same percentage decrease in the bad debt loss rate.

Post‐FIRREA issues involving reserve for bad‐debt losses

Journal of Corporate Accounting & FinanceVolume 1, Issue 3 p. 275-288 Feature Post-FIRREA issues involving reserve for bad-debt losses Mary V. Harcar, Mary V. Harcar Mary V. Harcar is counsel to the Washington, DC, law firm of Muldoon, Murphy & Faucette. She was formerly with the Office of Chief Counsel of the Internal Revenue Service and also served a clerkship at the United States Tax Court.Search for more papers by this author Mary V. Harcar, Mary V. Harcar Mary V. Harcar is counsel to the Washington, DC, law firm of Muldoon, Murphy & Faucette. She was formerly with the Office of Chief Counsel of the Internal Revenue Service and also served a clerkship at the United States Tax Court.Search for more papers by this author First published: Spring 1990 https://doi.org/10.1002/jcaf.3970010307 AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume1, Issue3Spring 1990Pages 275-288 RelatedInformation

Credit Union Structure, Growth and Regulatory Problems

At year end 1979, there were 12,738 Federal and 4,769 state chartered federally insured credit unions in the United States. Their assets were $36.5 billion and $18.5 billion, respectively. The industry was the fastest growing of all financial intermediaries during the early to mid-1970s. Factors contributing to a growth rate of 18 percent from 1970 to 1978 were the absence of Regulation Q restrictions on CU dividend rates, tax exempt status of CUs, the advent of share insurance in 1970 and the impact of sponsor subsidies. However, during 1979, federal credit union savings grew by only 2.4%, which was more than 85% lower than in the preceding year and was the slowest annual growth rate on record. Indeed, since 1977, federal credit unions have gone from the fastest growing to the second slowest growing type of depository institution. The reason for the changes in credit unions will be discussed after this section which provides background information on the structure of the industry. That structure has, historically led to a reduction in risk. The basic components of that structure are common bond and sponsorship.' Common Bond Credit unions are unique financial depository institutions in that they are consumer cooperatives and are limited to serving the market for consumer credit and savings. Generally, these institutions cannot do business with the general public due to charter limitations based on serving a membership that is characterized by a common bond. The common bond is based on occupation, association or residence. The only exceptions are generally found under state law and apply to state chartered institutions. The common bond restriction on credit union membership is assumed to reduce the cost of gathering credit information, reducing bad debt losses and manifests * National Credit Union Administration. The views expressed herein are those of the authors and do not necessarily reflect those of the National Credit Union Administration. ' An extensive review of the history of credit unions and the institutional structure is found in

Opportunity Cost in the Evaluation of Investment in Accounts Receivable

T he currently used method of evaluating investment in accounts receivable emphasizes that the acceptance of a proposed change in credit policy is dependent on the marginal profitability being greater than or equal to the associated marginal opportunity cost [2; 4, Chap. 6; 7; 11, Chap. 18]. The general notion is that the demand for a firm's product is elastic with respect to credit policy changes and that the policy decision involves a tradeoff between profits on the marginal credit sales and the marginal costs. Costs include costs of goods sold, cost of additional capital committed to accounts receivable (opportunity cost), additional bookkeeping costs, collection costs, and bad-debt losses. As the analysis is normally conducted in the short-run context, the arbitrary allocation of fixed costs is excluded.