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Journal ArticleDOI

Measuring firm performance using financial ratios: A decision tree approach

01 Aug 2013-Expert Systems With Applications (Pergamon)-Vol. 40, Iss: 10, pp 3970-3983
TL;DR: Sensitivity analyses indicated that Earnings Before Tax-to-Equity Ratio and Net Profit Margin are the two most important variables, and the CHAID and C5.0 decision tree algorithms produced the best prediction accuracy.
Abstract: 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 (ie, 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, C50, 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 C50 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
Citations
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Journal ArticleDOI
TL;DR: The traditional statistical models and state-of-the-art intelligent methods for financial distress forecasting are summarized, with the emphasis on the most recent achievements as the promising trend in this area.
Abstract: The assessment of financial credit risk is an important and challenging research topic in the area of accounting and finance. Numerous efforts have been devoted into this field since the first attempt last century. Today the study of financial credit risk assessment attracts increasing attentions in the face of one of the most severe financial crisis ever observed in the world. The accurate assessment of financial credit risk and prediction of business failure play an essential role both on economics and society. For this reason, more and more methods and algorithms were proposed in the past years. From this point, it is of crucial importance to review the nowadays methods applied to financial credit risk assessment. In this paper, we summarize the traditional statistical models and state-of-the-art intelligent methods for financial distress forecasting, with the emphasis on the most recent achievements as the promising trend in this area.

128 citations


Cites methods from "Measuring firm performance using fi..."

  • ...Bae showed an SVM model with radial basis kernel function (RSVM) received outstanding performance for Korean manufacturing distress prediction (Bae 2012)....

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  • ...Delen et al. (2013) applied CHAID, C5....

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Journal ArticleDOI
TL;DR: In this paper, the authors developed five hybrid machine learning algorithms namely, Frequency Ratio-Multilayer Perceptron (FR-MLP), Frequency Ratio Logistic Regression (FRL), CART-FR, LR-FR and SVM-SVM for mapping forest fire susceptibility in the north of Morocco.

89 citations

Journal ArticleDOI
TL;DR: Experimental results reveal that the performance of the ensembles indeed depends on the prevalent type of positive samples, and that the misclassification costs are typically much higher than those associated to the non-default or non-bankrupt (negative) class.

86 citations


Cites background from "Measuring firm performance using fi..."

  • ...Support vector machines [8, 9, 10], genetic and evolutionary algorithms [11, 12, 13], artificial neural networks [14, 15, 16, 17, 18], rough sets [19, 20, 21], and decision trees [22, 23] have received much attention and widespread application in the field of finance and more specifically, to the prediction of credit risk, financial distress and corporate bankruptcy....

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Journal ArticleDOI
TL;DR: In this paper, a machine learning algorithm based on decision tree was used to predict future copper prices, with mean absolute percentage errors below 4% in both short-term and long-term.

79 citations

References
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Book
01 Jan 2000
TL;DR: Suitable for those new to statistics as well as students on intermediate and more advanced courses, the book walks students through from basic to advanced level concepts, all the while reinforcing knowledge through the use of SAS(R).
Abstract: Hot on the heels of the 3rd edition of Andy Field's award-winning Discovering Statistics Using SPSS comes this brand new version for students using SAS(R). Andy has teamed up with a co-author, Jeremy Miles, to adapt the book with all the most up-to-date commands and programming language from SAS(R) 9.2. If you're using SAS(R), this is the only book on statistics that you will need! The book provides a comprehensive collection of statistical methods, tests and procedures, covering everything you're likely to need to know for your course, all presented in Andy's accessible and humourous writing style. Suitable for those new to statistics as well as students on intermediate and more advanced courses, the book walks students through from basic to advanced level concepts, all the while reinforcing knowledge through the use of SAS(R). A 'cast of characters' supports the learning process throughout the book, from providing tips on how to enter data in SAS(R) properly to testing knowledge covered in chapters interactively, and 'real world' and invented examples illustrate the concepts and make the techniques come alive. The book's companion website (see link above) provides students with a wide range of invented and real published research datasets. Lecturers can find multiple choice questions and PowerPoint slides for each chapter to support their teaching.

25,020 citations


"Measuring firm performance using fi..." refers background or methods in this paper

  • ...Therefore, the test provided statistical analysis to prove that the matrix has significant correlations among the variables (Field, 2005)....

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  • ...In other words, the amount of variance in each variable that could be explained by the retained factors is represented by the communalities after extraction (Field, 2005)....

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  • ...Eigenvectors prove the loading of a particular variable on a particular factor (Field, 2005)....

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  • ...Though many of these studies are successful in predicting bankruptcy outcomes, they often fall short on identifying and explaining the characteristics that can be used as determinants of the firm performance....

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  • ...Any value close to 1 indicates that the patterns of correlation are compact, and therefore the analysis should result in distinct and reliable factors (Field, 2005)....

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Book
15 Oct 1992
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
Abstract: From the Publisher: Classifier systems play a major role in machine learning and knowledge-based systems, and Ross Quinlan's work on ID3 and C4.5 is widely acknowledged to have made some of the most significant contributions to their development. This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use , the source code (about 8,800 lines), and implementation notes. The source code and sample datasets are also available on a 3.5-inch floppy diskette for a Sun workstation. C4.5 starts with large sets of cases belonging to known classes. The cases, described by any mixture of nominal and numeric properties, are scrutinized for patterns that allow the classes to be reliably discriminated. These patterns are then expressed as models, in the form of decision trees or sets of if-then rules, that can be used to classify new cases, with emphasis on making the models understandable as well as accurate. The system has been applied successfully to tasks involving tens of thousands of cases described by hundreds of properties. The book starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting. Advantages and disadvantages of the C4.5 approach are discussed and illustrated with several case studies. This book and software should be of interest to developers of classification-based intelligent systems and to students in machine learning and expert systems courses.

21,674 citations


Additional excerpts

  • ...0: This was developed by Quinlan (1993)....

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Book
25 Oct 1999
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
Abstract: Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

20,196 citations

Journal ArticleDOI
TL;DR: This article gives an introduction to the subject of classification and regression trees by reviewing some widely available algorithms and comparing their capabilities, strengths, and weakness in two examples.
Abstract: Classification and regression trees are machine-learning methods for constructing prediction models from data. The models are obtained by recursively partitioning the data space and fitting a simple prediction model within each partition. As a result, the partitioning can be represented graphically as a decision tree. Classification trees are designed for dependent variables that take a finite number of unordered values, with prediction error measured in terms of misclassification cost. Regression trees are for dependent variables that take continuous or ordered discrete values, with prediction error typically measured by the squared difference between the observed and predicted values. This article gives an introduction to the subject by reviewing some widely available algorithms and comparing their capabilities, strengths, and weakness in two examples. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 14-23 DOI: 10.1002/widm.8 This article is categorized under: Technologies > Classification Technologies > Machine Learning Technologies > Prediction Technologies > Statistical Fundamentals

16,974 citations


"Measuring firm performance using fi..." refers methods in this paper

  • ...It is similar to the C&RT algorithm (Breiman et al., 1984)....

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Trending Questions (1)
What are the limitations of using traditional financial ratios to measure the performance of modern firms?

The provided paper does not discuss the limitations of using traditional financial ratios to measure the performance of modern firms.