47,460 research outputs found

    A Review of Bankruptcy Prediction Studies: 1930-Present

    Get PDF
    One of the most well-known bankruptcy prediction models was developed by Altman [1968] using multivariate discriminant analysis. Since Altman\u27s model, a multitude of bankruptcy prediction models have flooded the literature. The primary goal of this paper is to summarize and analyze existing research on bankruptcy prediction studies in order to facilitate more productive future research in this area. This paper traces the literature on bankruptcy prediction from the 1930\u27s, when studies focused on the use of simple ratio analysis to predict future bankruptcy, to present. The authors discuss how bankruptcy prediction studies have evolved, highlighting the different methods, number and variety of factors, and specific uses of models. Analysis of 165 bankruptcy prediction studies published from 1965 to present reveals trends in model development. For example, discriminant analysis was the primary method used to develop models in the 1960\u27s and 1970\u27s. Investigation of model type by decade shows that the primary method began to shift to logit analysis and neural networks in the 1980\u27s and 1990\u27s. The number of factors utilized in models is also analyzed by decade, showing that the average has varied over time but remains around 10 overall. Analysis of accuracy of the models suggests that multivariate discriminant analysis and neural networks are the most promising methods for bankruptcy prediction models. The findings also suggest that higher model accuracy is not guaranteed with a greater number of factors. Some models with two factors are just as capable of accurate prediction as models with 21 factors

    The Discriminant Analysis Used by the IRS to Predict Profitable Individual Tax Return Audits

    Get PDF
    This paper discusses past and current methods the IRS uses to determine which individual income tax returns to audit. The IRS currently uses the discriminant function to give all individual tax returns two scores; one based on whether it should be audited or not and one based on if the return is likely to have unreported income. The discriminant function is determined by the IRS’s National Research Program, which takes a sample of returns and ensures their accuracy. Previously, the function was determined by the IRS’s Taxpayer Compliance Measurement Program. However, this was too burdensome and time consuming for taxpayers. The data mining techniques of decision trees, regression, and neural networks were researched to determine if the IRS should change its method. Unfortunately IRS tax data were not obtainable due to their confidentiality; therefore credit data from a German bank was used to compare discriminant analysis results to the three new methods. All of the methods were run to predict creditworthiness and were compared based on misclassification rates. The neural network had the best classification rate closely followed by regression, the decision tree, and then discriminant analysis. Since this comparison is not based on IRS tax data, no conclusion can be made whether the IRS should change its method or not, but because all methods had very close classification rates, it would be worthwhile for the IRS to look into them

    A Neural-CBR System for Real Property Valuation

    Get PDF
    In recent times, the application of artificial intelligence (AI) techniques for real property valuation has been on the increase. Some expert systems that leveraged on machine intelligence concepts include rule-based reasoning, case-based reasoning and artificial neural networks. These approaches have proved reliable thus far and in certain cases outperformed the use of statistical predictive models such as hedonic regression, logistic regression, and discriminant analysis. However, individual artificial intelligence approaches have their inherent limitations. These limitations hamper the quality of decision support they proffer when used alone for real property valuation. In this paper, we present a Neural-CBR system for real property valuation, which is based on a hybrid architecture that combines Artificial Neural Networks and Case- Based Reasoning techniques. An evaluation of the system was conducted and the experimental results revealed that the system has higher satisfactory level of performance when compared with individual Artificial Neural Network and Case- Based Reasoning systems

    Neural Networks, Ordered Probit Models and Multiple Discriminants. Evaluating Risk Rating Forecasts of Local Governments in Mexico.

    Get PDF
    Credit risk ratings have become an important input in the process of improving transparency of public finances in local governments and also in the evaluation of credit quality of state and municipal governments in Mexico. Although rating agencies have recently been subjected to heavy criticism, credit ratings are indicators still widely used as a benchmark by analysts, regulators and banks monitoring financial performance of local governments in stable and volatile periods. In this work we compare and evaluate the performance of three forecasting methods frequently used in the literature estimating credit ratings: Artificial Neural Networks (ANN), Ordered Probit models (OP) and Multiple Discriminant Analysis (MDA). We have also compared the performance of the three methods with two models, the first one being an extended model of 34 financial predictors and a second model restricted to only six factors, accounting for more than 80% of the data variability. Although ANN provides better performance within the training sample, OP and MDA are better choices for classifications in the testing sample respectively.Credit Risk Ratings, Ordered Probit Models, Artificial Neural Networks, Discriminant Analysis, Principal Components, Local Governments, Public Finance, Emerging Markets
    • …
    corecore