Factoring policy with constant demand and limited capital

The convention of selling on credit (to customers) results in mass accumulation of accounts receivable (AR) on the balance sheet of firms. However, capital‐constrained firms do not have enough capital to invest in AR and cover the production cost incurred during the credit period. To finance future business, a capital‐constrained firm can employ factoring—a financing scheme wherein firms sell AR to a financial institution (called a factor) at a discount—to advance cash from AR. By formulating a time‐continuous model with constant demand over an infinite horizon, we study the factoring policy of firms in two practice‐based discounting schemes: automatic discounting and manual discounting. In the automatic discounting scheme, AR should be discounted at the same age, whereas this requirement is relaxed in the manual discounting scheme and how long in advance to discount the AR is contingent over time. In both discounting schemes, the firm needs to choose the timing of discounting in order to reach a capital‐unconstrained state as soon as possible. In the automatic discounting scheme, we approximate the firm's objective function with a quasi‐convex function whose error is demonstrated to be small. Based on this approximation, the firm's optimal decision and the factor's profit can be calculated more easily. When manual discounting is adopted, the firm should meet all the demand by exploiting factoring if the profit margin under immediate discounting is nonnegative. Given the same factoring discount rate, manual discounting is always more attractive to the firm than automatic discounting. However, the preference of the factor over the two discounting schemes depends on the factoring discount rate.     

The nature of sellers’ cyber credit in C2C e-commerce: the perspective of social capital

With the fast evolution of e-commerce, it is getting harder for traditional credit management systems to service online businesses with diversified needs in dynamic scenarios. This paper studies the nature of cyber credit from the perspective of social capital. We propose the credit assessment model using social capital variables extracted from the reputation system of an e-commerce platform and the associated online social network. In addition, we consider the dynamic and diversified effects of online reputation on sellers’ cyber credit, and we verify the rationality of the credit assessment model through analyzing the relationship between cyber credit and social network variables. We take Alibaba C2C e-commerce market as our experimental study platform and use the social networking information from Sina’s microblogging services. We find that social capital variables can be used to effectively measure the cyber credit of online sellers in C2C businesses.     

Computational time reduction for credit scoring: An integrated approach based on support vector machine and stratified sampling method

With the rapid growth of credit industry, credit scoring model has a great significance to issue a credit card to the applicant with a minimum risk. So credit scoring is very important in financial firm like bans etc. With the previous data, a model is established. From that model is decision is taken whether he will be granted for issuing loans, credit cards or he will be rejected. There are several methodologies to construct credit scoring model i.e. neural network model, statistical classification techniques, genetic programming, support vector model etc. Computational time for running a model has a great importance in the 21st century. The algorithms or models with less computational time are more efficient and thus gives more profit to the banks or firms. In this study, we proposed a new strategy to reduce the computational time for credit scoring. In this approach we have used SVM incorporated with the concept of reduction of features using F score and taking a sample instead of taking the whole dataset to create the credit scoring model. We run our method two real dataset to see the performance of the new method. We have compared the result of the new method with the result obtained from other well known method. It is shown that new method for credit scoring model is very much competitive to other method in the view of its accuracy as well as new method has a less computational time than the other methods.     

What is at stake in the construction and use of credit scores?

Most of statistical studies on credit scoring focus on scores construction. It is more unusual that they link the statistical technics with a detailed analysis of the users’ requirements regarding the properties of these tools. Concerning companies’ failure the users are financial analysis experts or bankers in credit risk departments or banking supervisors. The increasing need for better control of credit risk by banks has led to a stepping-up of research concerning credit scoring. In the context of the Basel II agreement, the International Banking Committee has stressed the importance of forecasting the expected loss (EL) and, using extreme quantiles, the unexpected loss (UL) for a population of companies, in particular for customers of each commercial bank. In order to do so, it is necessary to estimate the default probability of each company at a given time horizon (PD). The objective of an accurate forecasting gives rise to several needed properties and questions that are presented in Sect. 1. We stress what is at stake in the construction and the use of credit scores. The experience of Banque de France in prudential supervision and the importance of its data files on companies give the possibility to developp a scoring system able to fullfil these needed properties, at least partially. Some principles of credit scoring construction in order to increase the quality of the tool and the accuracy of default probability are presented in Sect. 2. Without leading a complete debate on models’choice we discuss some arguments regarding this choice and we concentrate on comparison between Fisher linear discriminant analysis (LDA) and logistic regression (LOGIT) in Sect. 3. In relation with the early detection of companies default, two pratical uses of a credit scoring system are presented in Sect. 4. Research under way on Banque de France data concentrates on informations that can be extracted from these data on purpose to study how to increase the quality of tools needed by the Basel II agreement. A short overview of this research is given in Sect. 5. “Statistical inference techniques, if not applied to the real world, will lose their import and appear to be deductive exercises. Furthermore, it is my belief that a statistical course emphasis should be given to both mathematical theory of statistics and to application of the theory to practical problems. A detailed discussion on the application of a statistical technique facilitates better understanding of the theory behind the technique.” C. Radhakrishna RAO in Linear Statistical Inference and Its Applications     

A Flexible Markov Chain Approach for Multivariate Credit Ratings

Modeling the dependence of credit ratings is an important issue for portfolio credit risk analysis. Multivariate Markov chain models are a feasible mathematical tool for modeling the dependence of credit ratings. Here we develop a flexible multivariate Markov chain model for modeling the dependence of credit ratings. The proposed model provides a parsimonious way to capture both the cross-sectional and temporal associations among ratings of individual entities. The number of model parameters is of the magnitude O(sm ² + s ² m), where m is the number of ratings categories and s is the number of entities in a credit portfolio. The proposed model is also easy to implement. The estimation method is formulated as a set of s linear programming problems and the estimation algorithm can be implemented easily in a Microsoft EXCEL worksheet, see Ching et al. Int J Math Educ Sci Eng 35:921–932 (2004). We illustrate the practical implementation of the proposed model using real ratings data. We evaluate risk measures, such as Value at Risk and Expected Shortfall, for a credit portfolio using the proposed model and compare the risk measures with those arising from Ching et al. IMRPreprintSeries (2007), Siu et al. Quant Finance 5:543–556 (2005).     

基于改进GS-XGBoost的个人信用评估

信用评估分类器的好坏能够直接影响信贷金融机构的盈利能力. 传统的网格搜索法进行参数寻优时会耗费大量的时间, 基于此提出改进的网格搜索法优化XGBoost (GS-XGBoost)的个人信用评估算法. 该算法利用随机森林进行特征选择后, 将改进的网格搜索法对XGBoost中的n_estimators和learning_rate进行参数寻优, 建立评估模型. 从UCI数据库中选取信贷数据进行分析, 分别与支持向量机、随机森林、逻辑回归、神经网络以及未改进的XGBoost进行比较. 实验结果表明, 该模型的F-score和G-mean的值均有提高.     

Learning to Predict by the Methods of Temporal Differences

This article introduces a class of incremental learning procedures specialized for prediction – that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit by means of the difference between predicted and actual outcomes, the new methods assign credit by means of the difference between temporally successive predictions. Although such temporal-difference methods have been used in Samuel's checker player, Holland's bucket brigade, and the author's Adaptive Heuristic Critic, they have remained poorly understood. Here we prove their convergence and optimality for special cases and relate them to supervised-learning methods. For most real-world prediction problems, temporal-difference methods require less memory and less peak computation than conventional methods and they produce more accurate predictions. We argue that most problems to which supervised learning is currently applied are really prediction problems of the sort to which temporal-difference methods can be applied to advantage.     

Reaction Function Based Dynamic Location Modeling in Stackelberg–Nash–Cournot Competition

We formulate a dynamic facility location model for a firm locating on a discrete network. It is assumed that this locating firm will act as the leader firm in an industry characterized by Stackelberg leader–follower competition. The firm’s I competitors are assumed to act as Cournot firms and are each assumed to operate under the assumption of zero conjectural variation with respect to their I–1 Cournot competitors. Using sensitivity analysis of variational inequalities within a hierachical mathematical programming approach, we develop reaction function based dynamic models to optimize the Stackelberg firm’s location decision. In the second half of this paper, we use these models to illustrate through a numerical example the insights yielded by our approach.     

债务融资、公司绩效与国有银行改革

本文选择中国上市公司2001～2006年的面板数据,利用回归方法研究了中国上市公司债务融资与公司经营绩效之间的关系。研究发现,第一,中国上市公司债务融资与公司经营绩效之间呈负相关关系,这一结果与国外公司金融文献中的预测相反,中国国有银行普遍存在的预算软约束是导致这一现象的根源;第二,债务融资与公司绩效的负相关性随时间的推移在逐渐减弱,这一效应可以归结于近年来中国银行业改革增强了国有银行的独立性,加强了贷款约束。该研究的一个重要启示是,预算硬约束是债务融资发挥治理效应的一个重要前提条件,中国银行业的改革有利于硬化预算约束。     

Retailer's optimal ordering policies with trade credit financing

In this article, we extended Goyal's model to develop an Economic Order Quantity (EOQ) model in which the supplier offers the retailer the permissible delay period M, and the retailer in turn provides the trade credit period N (with N?≤?M) to his/her customers. In addition, we assume that (1) the retailer's selling price per unit is necessarily higher than its unit cost, and (2) the interest rate charged by a supplier or a bank is not necessarily higher than the retailer's investment return rate. We then establish an appropriate EOQ model with trade credit financing, and provide an easy-to-use closed-form solution to the problem. Furthermore, we find it is possible that a well-established buyer may order a lower quantity and take the benefit of the permissible delay more frequently, which contradicts to the result by the previous researchers. Finally, we perform some sensitivity analyses to illustrate the theoretical results and obtain some managerial results.     

Profit-maximizing firm investments in customer information security

When a customer interacts with a firm, extensive personal information often is gathered without the individual's knowledge. Significant risks are associated with handling this kind of information. Providing protection may reduce the risk of the loss and misuse of private information, but it imposes some costs on both the firm and its customers. Nevertheless, customer information security breaches still may occur. They have several distinguishing characteristics: (1) typically it is hard to quantify monetary damages related to them; (2) customer information security breaches may be caused by intentional attacks, as well as through unintentional organizational and customer behaviors; and (3) the frequency of such incidents typically is low, although they can be very costly when they occur. As a result, predictive models and explanatory statistical analysis using historical data have not been effective. We present a profit optimization model for customer information security investments. Our approach is based on value-at-risk methods and operational risk modeling from financial economics. The main results of this work are that we: (1) provide guidance on the trade-offs between risk and return in customer information security investments; (2) define the range of efficient investments in technology-supported risk indemnification for sellers; (3) model how to handle government-dictated levels of investment versus self-regulation of investments in technology; and (4) characterize customer information security investment levels when the firm is able to pass some of its costs on to consumers. We illustrate our theoretical findings with empirical data from the Open Security Foundation, as a means of grounding our analysis and offering the reader intuition for the managerial interpretation of our theory and main results. The results show that we can narrow the decision set for solution providers and policy-makers based on the estimable risks and losses associated with customer information security. We also discuss the application of our approach in practice.     

Technology credit scoring model with fuzzy logistic regression

Technology credit scoring models have been used to screen loan applicant firms based on their technology. Typically a logistic regression model is employed to relate the probability of a loan default of the firms with several evaluation attributes associated with technology. However, these attributes are evaluated in linguistic expressions represented by fuzzy number. Besides, the possibility of loan default can be described in verbal terms as well. To handle these fuzzy input and output data, we proposed a fuzzy credit scoring model that can be applied to predict the default possibility of loan for a firm that is approved based on its technology. The method of fuzzy logistic regression as an appropriate prediction approach for credit scoring with fuzzy input and output was presented in this study. The performance of the model is improved compared to that of typical logistic regression. This study is expected to contribute to practical utilization of the technology credit scoring with linguistic evaluation attributes.     

Quantifying credit portfolio losses under multi-factor models

ABSTRACT

In this work, we investigate the challenging problem of estimating credit risk measures of portfolios with exposure concentration under the multi-factor Gaussian and multi-factor t-copula models. It is well-known that Monte Carlo (MC) methods are highly demanding from the computational point of view in the aforementioned situations. We present efficient and robust numerical techniques based on the Haar wavelets theory for recovering the cumulative distribution function of the loss variable from its characteristic function. To the best of our knowledge, this is the first time that multi-factor t-copula models are considered outside the MC framework. The analysis of the approximation error and the results obtained in the numerical experiments section show a reliable and useful machinery for credit risk capital measurement purposes in line with Pillar II of the Basel Accords.     

Two-echelon trade credit financing for perishable items in a supply chain when demand depends on both selling price and credit period

A profitable decision policy between a supplier and the retailers can be characterized by an agreement on the trade credit scenario such as permissible delay in payments. In real life business, we observe that the demand is a function of both the selling price and credit period rather than the constant demand. Incorporating this demand function to the retailer of a supply chain, we develop an EPQ – based model for perishable items under two-echelon trade financing. The purpose of this paper is to maximize the profit by determining the optimal selling price, credit period and replenishment time. It is shown that the model developed by Jaggi et al. [Jaggi, J. K., Goyal, S. K., & Goel, S. K., 2008. Retailer’s optimal replenishment decisions with creditlinked demand under permissible delay in payments. European Journal of Operational Research, 190, 130–135] can be treated as a special case of this paper. Finally, through numerical examples, sensitivity analysis shows the influence of key model parameters.     

Measuring firm performance using financial ratios: A decision tree approach

Determining the firm performance using a set of financial measures/ratios has been an interesting and challenging problem for many researchers and practitioners. Identification of factors (i.e., financial measures/ratios) that can accurately predict the firm performance is of great interest to any decision maker. In this study, we employed a two-step analysis methodology: first, using exploratory factor analysis (EFA) we identified (and validated) underlying dimensions of the financial ratios, followed by using predictive modeling methods to discover the potential relationships between the firm performance and financial ratios. Four popular decision tree algorithms (CHAID, C5.0, QUEST and C&RT) were used to investigate the impact of financial ratios on firm performance. After developing prediction models, information fusion-based sensitivity analyses were performed to measure the relative importance of independent variables. The results showed the CHAID and C5.0 decision tree algorithms produced the best prediction accuracy. Sensitivity analysis results indicated that Earnings Before Tax-to-Equity Ratio and Net Profit Margin are the two most important variables.     

A hybrid multi-criteria decision-making model for firms competence evaluation

In this paper, we present a hybrid multi-criteria decision-making (MCDM) model to evaluate the competence of the firms. According to the competence-based theory reveals that firm competencies are recognized from exclusive and unique capabilities that each firm enjoy in marketplace and are tightly intertwined within different business functions throughout the company. Therefore, competence in the firm is a composite of various attributes. Among them many intangible and tangible attributes are difficult to measure. In order to overcome the issue, we invite fuzzy set theory into the measurement of performance. In this paper first we calculate the weight of each criterion through adaptive analytic hierarchy process (AHP) approach (A³) method, and then we appraise the performance of firms via linguistic variables which are expressed as trapezoidal fuzzy numbers. In the next step we transform these fuzzy numbers into interval data by means of α-cut. Then considering different values for α we rank the firms through TOPSIS method with interval data. Since there are different ranks for different α values, we apply linear assignment method to obtain final rank for alternatives.     

Integrating Real and Financial Markets in an Agent-Based Economic Model: An Application to Monetary Policy Design

This article presents an agent-based integrated model of a real, financial, and monetary economy. The model is characterized by a monopolist firm that supplies a single homogeneous product in the goods market, hires workers in the labor market, and demands loans in the credit market; a trade union that sets the nominal wage; N heterogeneous households that buy the consumption good, provide the labor force, and trade the firm’s equity in the stock market; and a bank that lends money to the firm at an interest rate set according to a monetary policy strategy. The model is used to perform monetary policy experiments. A monetary policy rule which targets the gap between the current output and the potential output in the full employment case is investigated, studying the effects on the economy for different degrees of policy tightness. The monetary policy rule is compared to a random policy rule that conserves a similar structure. Results show that a tight monetary policy clearly over performs the random policy rule. Moreover, results corroborate the effectiveness of monetary policy in limiting inflation and increasing welfare.     

Data quality and firm performance: empirical evidence from the Korean financial industry

Despite popular belief that timely and precise data are important and indispensable to good decisions and that good decisions are related to better firm performance, empirical research that examines the effect of data quality on firm performance is still scarce. How great an impact does data quality have on firm performance? This study empirically investigates the effect of firm-level data quality on firm performance in the Korean financial industry during 2008–2010. The results show that commercial banks have high-quality data, while credit unions have comparatively low-quality data. They also show that better data quality has a positive influence on sales, operating profit, and value added. Improving the level of data quality management maturity by one can increase firm performance by 33.7 % in sales, 64.4 % in operating profit, and 26.2 % in value added.     

基于C2C交易的卖方信用增长模式分析

针对目前缺乏有效区分卖方信用增长类型的问题,提出了一种卖方交易数据转换的方法,并提出了C2C交易信用增长模式分类算法。通过引入监督的XYF网络方法对电子商务交易数据进行分析,能够对处于同一行业中、具有不同信用增长模式的卖方分类,为卖方的虚假信用识别提供了一种有效的途径,也为卖方的交易模式进行分类识别提供了一种新的思路。     

Manufacturing learning propensity in operations improvement

In this article, we present an exploratory research on manufacturing firms' choices of operations improvement strategies. We found that in the subject firms there was significant correlation between learning propensity espoused by managers and their choices of strategies for operations improvement. Relying on empirical data and information, we also identified three primary factors that influenced the process for the managers to form particular learning propensities: infrastructure in the firm, product mix, and top management. The empirical study, albeit bearing only exploratory results, enabled us to propose a tentative conclusion that as more managerial resources and managers' attention were devoted to a particular improvement strategy for which the managers formed a learning propensity, that “way of doing things” became more effective as the intentional experience and substantive investment compatible with the selected strategy accumulated. The enhanced effectiveness of that strategy reinforced the learning propensity, and the improvement process became more induced for the selected strategy. © 1998 John Wiley & Sons, Inc.