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
Field-aware attentive neural factorization with fuzzy mutual information for company investment valuation
TL;DR: Li et al. as mentioned in this paper proposed a field-aware attentive neural factorization machine (FAFM) model for large-scale data-driven company investment valuation, which considers field heterogeneity among features with fuzzy mutual information and develops an attention neural network to learn predictive strengths of pair-wise feature interactions.
Journal ArticleDOI
Long-term risk class migrations of non-bankrupt and bankrupt enterprises
TL;DR: In this paper, the authors developed a model of a Kohonen artificial neural network to determine six different classes of risk and used it to identify the long-term behavioral pattern differences between future “good” and “bad” enterprises from the perspective of risk class migrations.
Proceedings ArticleDOI
Impacts of technology assessments on firm performance
Siti Salwa Sait,Farrah Merlinda Muharam,Thoo Ai Chin,Zuraidah Sulaiman,Norhayati Zakuan,Tan Liat Choon +5 more
TL;DR: In this paper, the impact of technology assessment on a firm's performance is analyzed and the most prevalence indicator that available for organizations to measure their performance is identified and the current assessment practices that create competitive advantage.
Journal ArticleDOI
İşletme değeri̇ i̇le fi̇nansal oranlar arasinda i̇li̇şki̇ var mi? borsa i̇stanbul’da bi̇r uygulama
TL;DR: In this article, the relationship between financial ratios and firm value was investigated in the context of 115 production firms traded at the Borsa Istanbul BIST stock exchange, using a panel data analysis method.
Journal ArticleDOI
The Factors Affect Company Performance in Renewable Energy Industry
Yinlin Tsai,Johnny Tung +1 more
TL;DR: In this paper, the authors identify the performance determinants are divided in country-specific advantages and firm-specific advantage and conclude that renewable energy industry has a great potential due to its results performed.
References
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Book
Discovering Statistics Using SPSS
Andy P. Field,Jeremy N.V. Miles +1 more
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).
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Classification and Regression Trees.
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C4.5: Programs for Machine Learning
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.
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Data Mining: Practical Machine Learning Tools and Techniques
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.
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Classification and regression trees
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.