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.     

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 Decision Model for the Robot Selection Problem Using Robust Regression*

Industrial robots are increasingly used by many manufacturing firms. The number of robot manufacturers has also increased with many of these firms now offering a wide range of models. A potential user is thus faced with many options in both performance and cost. This paper proposes a decision model for the robot selection problem. The proposed model uses robust regression to identify, based on manufacturers' specifications, the robots that are the better performers for a given cost. Robust regression is used because it identifies and is resistant to the effects of outlying observations, key components in the proposed model. The robots selected by the model become candidates for testing to verify manufacturers' specifications. The model is tested on a real data set and an example is presented.     

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 Comparison of Linear Programming and Parametric Approaches to the Two-Group Discriminant Problem*

Recent simulation-based studies of linear programming models for discriminant analysis have used the Fisher linear discriminant function as the benchmark for parametric methods. This article reports experimental evidence which suggests that, while some linear programming models may match or even exceed the Fisher approach in classification accuracy, none of the fifteen models tested is as accurate on normally distributed data as the Smith quadratic discriminant function. At the minimum, further testing is warranted with an emphasis on data sets that arise from significantly non-Gaussian populations.     

Combining Neural Networks and Statistical Predictions to Solve the Classification Problem in Discriminant Analysis*

A number of recent studies have compared the performance of neural networks (NNs) to a variety of statistical techniques for the classification problem in discriminant analysis. The empirical results of these comparative studies indicate that while NNs often outperform the more traditional statistical approaches to classification, this is not always the case. Thus, decision makers interested in solving classification problems are left in a quandary as to what tool to use on a particular data set. We present a new approach to solving classification problems by combining the predictions of a well-known statistical tool with those of an NN to create composite predictions that are more accurate than either of the individual techniques used in isolation.     

Solving the Classification Problem in Discriminant Analysis Via Linear and Nonlinear Programming Methods*

This paper demonstrates the feasibility of applying nonlinear programming methods to solve the classification problem in discriminant analysis. The application represents a useful extension of previously proposed linear programming-based solutions for discriminant analysis. The analysis of data obtained by conducting a Monte Carlo simulation experiment shows that these new procedures are promising. Future research that should promote application of the proposed methods for solving classification problems in a business decision-making environment is discussed.     

Non-Normality and the Design of Control Charts for Averages*

Non-normality has a significant effect on the performance of control charts for averages. The design considerations for a control chart for averages must include recognition of the degree of non-normality of the underlying data. The performance of a control chart may be judged on its ability to correctly identify the probabilities of assignable causes of variation and chance causes of variation in a process. This paper examines the effects of non-normality, as measured by skewness and kurtosis, on the performance, and hence the design, of control charts for averages and provides an alternative method of designing charts for averages of data with non-normal distributions.     

Mathematical Programming Formulations for the Discriminant Problem: An Old Dog Does New Tricks*

In recent years, much research has been done on the application of mathematical programming (MP) techniques to the discriminant problem. While promising results have been obtained, many of these techniques are plagued by a number of problems associated with the model formulation including unbounded, improper, and unacceptable solutions as well as solution instability under linear transformation of the data. In attempting to solve these problems, numerous formulations have been proposec involving additional variables and/or normalization constraints. While effective, these models can also become quite complex. In this paper we demonstrate that a simple, well-known special case of Hand's [13] original formulation provides an implicit normalization which avoids the problems for which various complicated remedies have been devised. While other researchers have made use of this formulation, its properties have not previously been fully recognized.     

A TIME-SERIES APPROACH FOR CONSTRUCTING EXPECTATIONS MODELS*

This article presents an efficient way of dealing with adaptive expectations models—a way that makes use of all the information available in the data. The procedure is based on multiple-input transfer functions (MITFs): by calculating lead and lag cross correlations between innovations associated with the variables in the model, it is possible to determine which periods have the greatest effects on the dependent variable. If information about k periods ahead is required, fitted values for the expectation variables are used to generate k-period-ahead forecasts. These in turn can be used in the estimation of the transfer function equation, which not only contains the usual lagged variables but also allows for incorporation of lead-fitted values for the expectation variables. The MITF identification and estimation procedures used are based on the corner method. The method is contrasted with the Almon distributed-lag approach using a model relating stock market prices to interest rates and expected corporate profits.     

On the Repeated Test-Retest Reliability of the End-User Computing Satisfaction Instrument: A Comment*

This comment extends the test-retest reliability of the end-user computing satisfaction (EUCS) instrument by Torkzadeh and Doll [10]. Whereas Torkzadeh and Doll [10] reportedstability for two hour and two week EUCS test-retest reliability, we investigate the test-retest reliability of the EUCS instrument at two points in time, separated by a two yearinterval. We assess the end user computing satisfaction of personal computer, as well as mainframe, administrative end users in a large public organization. The results of the repeated test-retest using differing application platforms add further support for the reliability of the EUCS measure and highlight some areas of concern for managers of information systems.     

A Test of the Cross-Sectional Robustness of the Arbitrage Pricing Model Using Foreign Exchange Rates*

This paper tests the cross-sectional robustness of the arbitrage pricing theory (APT) model using foreign exchange rate data to determine if the model is robust with respect to the various random samples and various factor analytic techniques. Factor scores are developed using various samples and factor analytic techniques to explain the returns for other samples and groupings. The APT model is found to be robust across samples and techniques.     

A Multivariate Methodology for Simultaneously Capturing and Clustering Judgment Policies

This paper presents a new linear model methodology for clustering judges with homogeneous decision policies and differentiating dimensions which distinguish judgment policies. This linear policy capturing model based on canonical correlation analysis is compared to the standard model based on regression analysis and hierarchical agglomerative clustering. Potential advantages of the new methodology include simultaneous instead of sequential consideration of information in the dependent and independent variable sets, decreased interpretational difficulty in the presence of multicollinearity and/or suppressor/moderator variables, and a more clearly defined solution structure allowing assessment of a judge's relationship to all of the derived, ideal policy types. An application to capturing policies of information systems recruiters responsible for hiring entry-level personnel is used to compare and contrast the two techniques.     

ASSESSING ENVIRONMENTAL SCANNING AT THE SUBUNIT LEVEL: A MULTITRAIT-MULTIMETHOD ANALYSIS*

Environmental scanning activities of over 400 top management subunits in 108 European manufacturing firms provided the data base for this research. Four traits (constructs) of scanning using three methods (interest, frequency, and time) were examined. A confirmatory factor analysis approach to multitrait-multimethod (MTMM) data was employed. Verification of the reliability as well as convergent and discriminant validity of the scanning scales are reported and discussed. The viability of confirmatory factor analysis in providing a precise analysis of partitioning variance according to trait, method, and error is demonstrated.     

Estimating the Geographic Extent of Part-Time Labor Markets: U.S. Army Reserves*

Local labor market supportability is becoming an increasingly important issue for the United States Army Reserves. As military bases close and Reserve units are consolidated at fewer Reserve centers, the appropriate reassignments of units to Reserve centers require accurate measures of the ability of local labor markets to support such consolidations. A two-stage random effect model is applied to evaluate the geographical extent of the labor market for Army Reserve centers. In the first stage model, a lognormal distribution is used to describe the commuting distance behavior of the Reserve center members. In the second stage model, we estimate the mean of log transformed commute distance as a function of regional characteristics of the Reserve center. An iterative weighted stepwise selection method is used to find a set of characteristics that adequately predict variation of the mean commute distance over Reserve centers. The resulting model is used as inputs to location and market assessment models to assist the marketing decisions of the Army Recruiting Command.     

SOME STATISTICAL ISSUES IN THE ESTIMATION OF A SIMPLE COST-VOLUME-PROFIT MODEL*

This paper presents point and interval estimators of both long-run and single-period target quantities in a simple cost-volume-profit (C-V-P) model. This model is a stochastic version of the “accountant's break-even chart” where the major component is a semivariable cost function. Although these features suggest obvious possibilities for practical application, a major purpose of this paper is to examine the statistical properties of target quantity estimators in C-V-P analysis. It is shown that point estimators of target quantity are biased and possess no moments of positive order, but are consistent. These properties are also shared by previous break-even models, even when all parameters are assumed known with certainty. After a test for positive variable margins, Fieller's [6] method is used to obtain interval estimators of relevant target quantities. This procedure therefore minimizes possible ambiguities in stochastic break-even analysis (noted by Ekern [3]).     

Necessary and Sufficient Conditions of Unacceptable Solutions in LP Discriminant Analysis*

Characterization of unacceptable solutions for linear programming (LP) discriminant models have been discussed in the literature and the results presented so far are not satisfactory. This paper establishes necessary and sufficient conditions of unacceptable solutions for a number of LP models, addresses the practical implications of these conditions, and discusses the relationship between unacceptable solutions and multiple solutions.     

Characterization of Unacceptable Solutions in LP Discriminant Analysis*

Many linear programming models have been proposed for performing discriminant analysis. Partial characterizations for unacceptable solutions have been presented and new models proposed to circumvent these problems. In this paper those conditions leading to unacceptable solutions for all two-group models are characterized.     

Theoretical Distributions of Optima for Univariate Discrimination of Random Data*

Optimal linear discriminant models maximize percentage accuracy for dichotomous classifications, but are rarely used because a theoretical framework that allows one to make valid statements about the statistical significance of the outcomes of such analyses does not exist. This paper describes an analytic solution for the theoretical distribution of optimal values for univariate optimal linear discriminant analysis, under the assumption that the data are random and continuous. We also present the theoretical distribution for sample sizes up to N= 30. The discovery of a statistical framework for evaluating the performance of optimal discriminant models should greatly increase their use by scientists in all disciplines.     

A MODEL FOR THE PREDICTION OF JUROR DECISIONS*

A logit model approach designed to assign a probability whether prospective jurors will favor the defendant or plaintiff in a case, as a function of perceived individual juror characteristics, is described in the context of a case situation. The model, which has been programmed on a hand-held computer, is designed for implementation in courtroom settings to help defense attorneys evaluate and select jurors in order to minimize the likelihood of large jury awards. Empirical tests of the model are also described.