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
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Book ChapterDOI
Determinants of Profitability: Evidence of Renewable Energy Companies
TL;DR: In this article, the authors examined the effect of firm-specific characteristics and macroeconomic determinants of renewable energy companies using the Fixed Effects Model spanning the period 2005-2018 and found that internal factors that depend on firm management are significant and have a positive impact on profitability.
Journal ArticleDOI
Measuring stock performance using stochastic frontier analysis model with dependent error approach
TL;DR: In this article , the authors focused on analyzing the technical efficiency of Malaysian stock performance over the period of 2013 to 2018, by utilizing the stochastic frontier analysis (SFA) production function Cobb-Douglas, the inefficiency effect of time-invariant is allowed and predicted to estimate technical efficiency score as well as provide a ranking efficiency based on the model estimation performance.
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Predicting stock returns with financial ratios: A new methodology incorporating machine learning techniques to beat the market
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Internal Relevance between Analysts’ Forecasts and Target Prices - Informativeness and Investment Value
TL;DR: In this paper , a new multivariate information metric, Internal Relevance (IR), is proposed to measure the extent to which the revisions of analysts' consensus earnings and sales forecasts are incorporated into the revision of consensus target prices.
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).
Journal ArticleDOI
Classification and Regression Trees.
Book
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.
Book
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.
Journal ArticleDOI
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.