THE ANALYSIS OF OUTLYING DATA POINTS USING ROBUST REGRESSION: A MULTIVARIATE PROBLEM-BANK IDENTIFICATION MODEL*

Because the eight largest bank failures in United States history have occurred since 1973 [24], the development of early-warning problem-bank identification models is an important undertaking. It has been shown previously [3] [5] that M-estimator robust regression provides such a model. The present paper develops a similar model for the multivariate case using both a robustified Mahalanobis distance analysis [21] and principal components analysis [10]. In addition to providing a successful presumptive problem-bank identification model, combining the use of the M-estimator robust regression procedure and the robust Mahalanobis distance procedure with principal components analysis is also demonstrated to be a general method of outlier detection. The results from using these procedures are compared to some previously suggested procedures, and general conclusions are drawn.     

A Robust Multivariate Procedure for the Identification of Problem Savings and Loan Institutions*

The 1980s have seen a great increase in the number of failed savings and loan institutions. In order to prevent such failures, it would be helpful if regulators have an early warning model. Such a model should be able to flag potential failed firms to prevent failure. In this paper, a robust multivariate procedure is used to successfully identify potentially failed firms well ahead of an actual failure date.     

Neural Networks: A New Tool for Predicting Thrift Failures*

A neural network model that processes input data consisting of financial ratios is developed to predict the financial health of thrift institutions. The network's ability to discriminate between healthy and failed institutions is compared to a traditional statistical model. The differences and similarities in the two modelling approaches are discussed. The neural network, which uses the same financial data, requires fewer assumptions, achieves a higher degree of prediction accuracy, and is more robust.     

A Microeconomic Model of the Focused Factory-Vertical Integration Strategic Decision Problem

The design of a manufacturing strategy incorporates the decision of whether to focus or vertically integrate, or adopt a policy somewhere between the two extremes. Unfortunately, the literature contains few models that aid a manager in this decision process. In this paper we design a model to evaluate and compare various strategic alternatives along the focused factory-vertical integration continuum. By defining a point along this continuum as the percent of components manufactured internally that are needed to make one finished item (where 0 indicates complete focus and one manufacturing step, and 1 indicates full integration and 100 percent internal manufacturing) we are able to delineate the effects of the alternative decision strategies on flexibility and the firm's cash flow. The capital asset pricing model is invoked to assess the impact on the firm's risk and value.     

TOWARD UNDERSTANDING THE CONTENTS OF THE “BLACK BOX” FOR PREDICTING COMPLEX DECISION-MAKING OUTCOMES

The purpose of this research is to show the usefulness of three relatively simple nonlinear classification techniques for policy-capturing research where linear models have typically been used. This study uses 480 cases to assess the decision-making process used by 24 experienced national bank examiners in classifying commercial loans as acceptable or questionable. The results from multiple discriminant analysis (a linear technique) are compared to those of chi-squared automatic interaction detector analysis (a search technique), log-linear analysis, and logit analysis. Results show that while the four techniques are equally accurate in predicting loan classification, chi-squared automatic interaction detector analysis (CHAID) and log-linear analysis enable the researcher to analyze the decision-making structure and examine the “human” variable within the decision-making process. Consequently, if the sole purpose of research is to predict the decision maker's decisions, then any one of the four techniques turns out to be equally useful. If, however, the purpose is to analyze the decision-making process as well as to predict decisions, then CHAID or log-linear techniques are more useful than linear model techniques.     

Consistency and Accuracy in Decision Aids: Experiments with Four Multiattribute Systems*

There have been a number of multiattribute decision aids developed to aid selection problems. Multiattribute value theory and the analytic hierarchy process are two commonly used techniques. Different systems can result in radically different conclusions if they inaccurately and inconsistently reflect the preference structure of decision makers, or if they are based on inappropriate theoretical models. This study examines the impact of the underlying theoretical model, the method in which preference information is elicited, and the structure of alternatives as influences on the results from using various decision aids. It was found that two systems based on the multiattribute value theory model were just as diverse in their conclusions as were results between AHP and the multiattribute value theory models. Therefore, accuracy of information reflecting decision maker preference is an important consideration. Feedback capable of assuring the decision maker that information provided is consistent is a necessary feature required of decision aids applied to selection problems. The study also found that the way in which information is elicited influenced the result more than did the underlying model. Exact numerical data for complex concepts such as attribute importance and alternative performance on attributes is not necessary, and elicitation procedures that are more natural for the user are likely to be more accurate.     

基于供应链金融的银行贷款价值比研究

供应链金融是金融业面向供应链的创新服务。考虑单一供应商和单一零售商构成的二级供应链,产品市场为典型的报童模型,零售商以其与制造商签订的产品采购合同向银行申请抵押贷款。将供应链上下游企业的决策引入银行基于下侧风险控制模式的贷款价值比决策模型,刻画供应链的决策对采购合同抵押价值的影响。首先确定了贷款价值比的解析式,然后讨论了其与批发价格、回购率等供应链决策的关系,并确定了相应的取值区间。     

Combining Judgmental and Statistical Forecasts: An Application to Earnings Forecasts

This study investigated the accuracy of combinations of statistical and judgmental forecasts of annual accounting earnings. Combined forecasts were generated as equally weighted (i.e., simple averages) and unequally weighted combinations of individual forecasts from time-series models of quarterly and annual earnings (statistical forecasts) and security analysts' forecasts of quarterly and annual earnings (judgmental forecasts). The effect of the number of individual forecasts combined on the accuracy of the combined forecasts was also examined. The empirical results indicated that, on the average, combined forecasts were more accurate than individual forecasts. The results also indicated that although analysts' forecasts are based on a wider information set, the accuracy of their forecasts could be improved by combining them with forecasts generated from statistical models. Even if the best individual forecast could be identified in advance, gains in accuracy could be achieved by using combinations of two other forecasting methods. Several of the combined forecasts were superior to the most accurate individual forecast. Forecasts combined by using unequal weights derived from a regression model proved more accurate than equally weighted combinations. Forecasting accuracy improved and the variability of accuracy across different combinations decreased as the number of forecasts in the combination increased.     

An application of multicriteria decision aid models in the prediction of open market share repurchases

This study presents the first attempt to develop classification models for the prediction of share repurchase announcements using multicriteria decision aid (MCDA) techniques. We use three samples consisting of 434 UK firms, 330 French firms, and 296 German firms, to develop country-specific models. The MCDA techniques that are applied for the development of the models are the UTilités Additives DIScriminantes (UTADIS) and the ELimination and Choice Expressing REality (ELECTRE) TRI. We adopt a 10-fold cross validation approach, a re-sampling technique that allows us to split the datasets in training and validation sub-samples. Thus, at the first stage of the analysis the aim is the development of a model capable of reproducing the classification of the firms considered in the training samples. Once this stage is completed, the model can be used for the classification of new firms not included in the training samples (i.e. validation stage). The results show that both MCDA models achieve quite satisfactory classification accuracies in the validation sample and they outperform both logistic regression and chance predictions. The developed models could provide the basis for a decision tool for various stakeholders such as managers, shareholders, and investment analysts.     

A test of the Maslach Burnout Inventory in three samples of healthcare professionals

Structural equation modelling with LISREL was used to investigate the factor structure of the Maslach Burnout Inventory (MBI). Emotional exhaustion was the most robust of the MBI's three factors, followed by depersonalization, while the personal accomplishment factor performed weakly. A new measurement model was developed in a sample of 197 nurses consisting of the emotional exhaustion and depersonalization dimensions, which were measured with five and two empirical MBI indicators, respectively. A test of invariance of the two-factor model across three samples (i.e. one calibration sample of nurses, and two validation samples consisting of hospital laboratory technicians and hospital managers with an effective sample size of 445) produced a good fit for the proposed two-factor model. Assessment of psychometric properties of the two-factor model produced (1) internal consistencies comparable to those reported in the literature for the MBI's originally specified emotional exhaustion and depersonalization scales, and (2) correlations with criterion variables that were all in the expected direction and magnitude, comparable to those produced by the originally specified scales. Theoretical implications for the use of the two-factor model in burnout research are discussed.     

Economic order quantity model with trade credit financing for non-decreasing demand

Researchers in the past have established their inventory lot-size models under trade credit financing by assuming that the demand rate is constant. However, from a product life cycle perspective, it is only in the maturity stage that demand is near constant. During the growth stage of a product life cycle (especially for high-tech products), the demand function increases with time. To obtain robust and generalized results, we extend the constant demand to a linear non-decreasing demand function of time. As a result, the fundamental theoretical results obtained here are suitable for both the growth and maturity stages of a product life cycle. In addition, we characterize the optimal solutions and obtain conclusions on important and relevant managerial phenomena. Lastly, we provide several numerical examples to illustrate the proposed model and its optimal solution.     

Modelling process robustness: a case study of centrifugal casting

A robust process minimises the effect of the noise factors on the performance of a product or process. The variation of the performance of a robust process can be measured through modelling and analysis of process robustness. In this paper, a comprehensive methodology for modelling and analysis of process robustness is developed considering a number of relevant tools and techniques such as multivariate regression, control charting and simulation within the broad framework of Taguchi method. The methodology as developed considers, in specific terms, process modelling using historical data pertaining to responses, inputs variables and parameters as well as simulated noise variables data, identification of the model responses at each experimental setting of the controllable variables, estimation of multivariate process capability indices and control of their variability using control charting for determining optimal settings of the process variables using design of experiment-based Taguchi Method. The methodology is applied to a centrifugal casting process that produces worm-wheels for steam power plants in view of its critical importance of maintaining consistent performance in various under controllable situations (input conditions). The results show that the process settings as determined ensure minimum in-control variability with maximum performance of the centrifugal casting process, indicating improved level of robustness.     

A test of the Maslach Burnout Inventory in three samples of healthcare professionals

Structural equation modelling with LISREL was used to investigate the factor structure of the Maslach Burnout Inventory (MBI). Emotional exhaustion was the most robust of the MBI's three factors, followed by depersonalization, while the personal accomplishment factor performed weakly. A new measurement model was developed in a sample of 197 nurses consisting of the emotional exhaustion and depersonalization dimensions, which were measured with five and two empirical MBI indicators, respectively. A test of invariance of the two-factor model across three samples (i.e. one calibration sample of nurses, and two validation samples consisting of hospital laboratory technicians and hospital managers with an effective sample size of 445) produced a good fit for the proposed two-factor model. Assessment of psychometric properties of the two-factor model produced (1) internal consistencies comparable to those reported in the literature for the MBI's originally specified emotional exhaustion and depersonalization scales, and (2) correlations with criterion variables that were all in the expected direction and magnitude, comparable to those produced by the originally specified scales. Theoretical implications for the use of the two-factor model in burnout research are discussed.     

Managing the Risk of Uncertain Threshold Responses: Comparison of Robust, Optimum, and Precautionary Approaches

Many commentators have suggested the need for new decision analysis approaches to better manage systems with deeply uncertain, poorly characterized risks. Most notably, policy challenges such as abrupt climate change involve potential nonlinear or threshold responses where both the triggering level and subsequent system response are poorly understood. This study uses a simple computer simulation model to compare several alternative frameworks for decision making under uncertainty -- optimal expected utility, the precautionary principle, and three different approaches to robust decision making -- for addressing the challenge of adding pollution to a lake without triggering unwanted and potentially irreversible eutrophication. The three robust decision approaches -- trading some optimal performance for less sensitivity to assumptions, satisficing over a wide range of futures, and keeping options open -- are found to identify similar strategies as the most robust choice. This study also suggests that these robust decision approaches offer a quantitative, decision analytic framework that captures the spirit of the precautionary principle while addressing some of its shortcomings. Finally, this study finds that robust strategies may be preferable to optimum strategies when the uncertainty is sufficiently deep and the set of alternative policy options is sufficiently rich.     

Robust Stochastic Lot‐Sizing by Means of Histograms

Traditional approaches in inventory control first estimate the demand distribution among a predefined family of distributions based on data fitting of historical demand observations, and then optimize the inventory control using the estimated distributions. These approaches often lead to fragile solutions whenever the preselected family of distributions was inadequate. In this article, we propose a minimax robust model that integrates data fitting and inventory optimization for the single‐item multi‐period periodic review stochastic lot‐sizing problem. In contrast with the standard assumption of given distributions, we assume that histograms are part of the input. The robust model generalizes the Bayesian model, and it can be interpreted as minimizing history‐dependent risk measures. We prove that the optimal inventory control policies of the robust model share the same structure as the traditional stochastic dynamic programming counterpart. In particular, we analyze the robust model based on the chi‐square goodness‐of‐fit test. If demand samples are obtained from a known distribution, the robust model converges to the stochastic model with true distribution under generous conditions. Its effectiveness is also validated by numerical experiments.     

Project risk management using multiple criteria decision-making technique and decision tree analysis: a case study of Indian oil refinery

This study proposes an integrated analytical framework for effective management of project risks using combined multiple criteria decision-making technique and decision tree analysis. First, a conceptual risk management model was developed through thorough literature review. The model was then applied through action research on a petroleum oil refinery construction project in the Central part of India in order to demonstrate its effectiveness. Oil refinery construction projects are risky because of technical complexity, resource unavailability, involvement of many stakeholders and strict environmental requirements. Although project risk management has been researched extensively, practical and easily adoptable framework is missing. In the proposed framework, risks are identified using cause and effect diagram, analysed using the analytic hierarchy process and responses are developed using the risk map. Additionally, decision tree analysis allows modelling various options for risk response development and optimises selection of risk mitigating strategy. The proposed risk management framework could be easily adopted and applied in any project and integrated with other project management knowledge areas.     

A NOTE ON THE USE OF SELECTED NONFINANCIAL RATIO VARIABLES TO PREDICT SMALL-BUSINESS LOAN PERFORMANCE*

Small business loan applications have not been evaluated successfully by traditional methods. This paper explores the possibility of using three types of nonfinancial ratio variables (owner, firm, and loan characteristics) to predict whether a small business will pay off or default its loan. The owner and loan variables were better predictors of loan success than the firm variables.     

一人多机的动态配置模型及其解法

本文以机械企业为背景,探讨了一人多机看管问题中的人员配置问题,并采用层次序列法对人员配置数学模型进行了求解,给出了启发式解法,指出了进一步研究的方向。     

A PREFORMANCE ANALYSIS OF PARAMETRIC AND NONPARAMETRIC DISCRIMINANT APPROACHES TO BUSINESS DECISION MAKING

This paper empirically investigation the performance of four relatively new nonparametric techniques against four different parameteric versions of discriminant analysis. The models were constructed and analyzed using financial data drawn from 232 bankrupt and nonbankrupt companies. Generally, the nonparametric approaches, with the exception of linear programming, performed as well as or better than the more traditional discriminant analysis.