Journal ArticleDOI
Measuring firm performance using financial ratios: A decision tree approach
TLDR
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 variablesread more
Citations
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Journal ArticleDOI
Financial credit risk assessment: a recent review
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.
Journal ArticleDOI
Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area
Meriame Mohajane,Romulus Costache,Firoozeh Karimi,Quoc Bao Pham,Ali Essahlaoui,Hoang Nguyen,Giovanni Laneve,Fatiha Oudija +7 more
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.
Journal ArticleDOI
Exploring the synergetic effects of sample types on the performance of ensembles for credit risk and corporate bankruptcy prediction
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.
Journal ArticleDOI
Forecasting copper prices by decision tree learning
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.
References
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Journal ArticleDOI
An Exploratory Technique for Investigating Large Quantities of Categorical Data
TL;DR: The technique set out in the paper, CHAID, is an offshoot of AID (Automatic Interaction Detection) designed for a categorized dependent variable with built-in significance testing, multi-way splits, and a new type of predictor which is especially useful in handling missing information.
BookDOI
Principal components analysis
TL;DR: In this paper, the concept of principal components is introduced and a number of techniques related to principal component analysis are presented, such as using principal components to select a subset of variables for regression analysis, detecting outliers, and detecting influential observations.
Journal ArticleDOI
Making best use of model evaluations to compute sensitivity indices
TL;DR: In this paper, the same set of model evaluations can be used to compute double estimates of the total effect of two factors taken together, for all such k 2 couples, where k is the dimensionality of the model.
Book
Data Mining: Concepts, Models, Methods, and Algorithms
TL;DR: This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces, to extract new information for decision making.