Detecting biotechnology industry's earnings management using Bayesian network, principal component analysis, back propagation neural network, and decision tree

Chen, Fu-Hsiang; Chi, Der‐Jang; Wang, Yicheng

doi:10.1016/j.econmod.2014.12.035

Cited by 33 publications

(23 citation statements)

References 25 publications

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“…Like partial least squares, the outcome is modelled by an intermediary set of unobserved variables called hidden layers (Kuhn and Johnson, 2013). These hidden layers are located in between the first layer, which contains the input neurons, and the final layer, which contains the predictions of the neural network for any case presented at its inputs neurons (Torgo, 2011;Chen et al, 2015). Therefore, one needs to decide on the number of hidden layers and the number of inputs in the various layers, as well as the weights that connect the inputs and the outputs of these layers (Ledolter, 2013;Feng and Zhang, 2014).…”

Section: On the Use Of Artificial Neural Network For Performance Prementioning

confidence: 99%

Energy efficiency drivers in South Africa: 1965–2014

Aye

Gupta

2018

Energy Efficiency

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This paper presents an assessment of energy performance in South Africa from 1965-2014 using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). In this research, TOPSIS is used first in a two-stage approach to assess how energy in South Africa has performed using the most frequent indicators adopted by the literature. Afterwards, in the second stage, neural networks are combined with TOPSIS results as part of an attempt to produce a model for energy performance with good predictive ability. The results reveal different impacts of contextual variables such as the rise of China in foreign trade, the Apartheid Regime, and oil shocks on energy performance in South Africa.

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Section: On the Use Of Artificial Neural Network For Performance Prementioning

confidence: 99%

Energy efficiency drivers in South Africa: 1965–2014

Aye

Gupta

2018

Energy Efficiency

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“…In practice, blast-induced ground motion is commonly expressed by a peak particle velocity (PPV) [9,10]. In order to well predict PPV, a large number of researchers 2 Mathematical Problems in Engineering conducted in-depth study and achieved some available results, which are called conventional empirical attenuation equations methods.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Peak Velocity of Blasting Vibration Based on Artificial Neural Network Optimized by Dimensionality Reduction of FA-MIV

Zhang

Jin

2018

Mathematical Problems in Engineering

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Blasting vibration is harmful to the nearby habitants and dwellings in diverse geotechnical engineering. In this paper, a novel scheme based on Artificial Neural Network (ANN) method optimized by dimensionality reduction of Factor Analysis and Mean Impact Value (FA-MIV) is proposed to predict peak particle velocity (PPV) of blasting vibration. To construct the model, nine parameters of field measurement are taken as undetermined input parameters for research, while peak particle velocity (PPV) is considered as output parameter. With the application of FA, common factors are extracted from undetermined input parameters. Then, principal components are defined as a linear combination of common factors. The weight of each principal components effected on output parameter is ranked according to the calculation of MIV, and two principal components with minimum weight are eliminated. Ultimately, output parameter (PPV) is explained in a low-dimensional space with four input characteristic parameters. In the prepared database consisting of 108 datasets, 98 datasets are used for the training of the model, while the rest are used for testing performance. The performances of the ANN models are compared with regression analysis, in terms of coefficient of determination (R2) and mean absolute error (MAE). It is found that the performances of ANN models with using FA-MIV are superior to those of models without using FA-MIV in the prediction of PPV. In addition, the abilities of ANN models are all superior to regression analysis in the prediction of PPV. The result obtained from ELM is more accurate than BPNN and MVRA models.

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“…Aside from accuracy of the prediction models, the occurrence of Type I error and Type II error cannot be ignored (O’Leary 1998 ; Kirkos et al 2007a , b ; Tasi and Huang 2010 ; Chen et al 2015 ). A Type II error may especially cause damages and high costs.…”

Section: Introductionmentioning

confidence: 99%

Improving the prediction of going concern of Taiwanese listed companies using a hybrid of LASSO with data mining techniques

2016

Self Cite

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The purpose of this study is to establish rigorous and reliable going concern doubt (GCD) prediction models. This study first uses the least absolute shrinkage and selection operator (LASSO) to select variables and then applies data mining techniques to establish prediction models, such as neural network (NN), classification and regression tree (CART), and support vector machine (SVM). The samples of this study include 48 GCD listed companies and 124 NGCD (non-GCD) listed companies from 2002 to 2013 in the TEJ database. We conduct fivefold cross validation in order to identify the prediction accuracy. According to the empirical results, the prediction accuracy of the LASSO–NN model is 88.96 % (Type I error rate is 12.22 %; Type II error rate is 7.50 %), the prediction accuracy of the LASSO–CART model is 88.75 % (Type I error rate is 13.61 %; Type II error rate is 14.17 %), and the prediction accuracy of the LASSO–SVM model is 89.79 % (Type I error rate is 10.00 %; Type II error rate is 15.83 %).

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Detecting biotechnology industry's earnings management using Bayesian network, principal component analysis, back propagation neural network, and decision tree

Cited by 33 publications

References 25 publications

Energy efficiency drivers in South Africa: 1965–2014

Energy efficiency drivers in South Africa: 1965–2014

Prediction of Peak Velocity of Blasting Vibration Based on Artificial Neural Network Optimized by Dimensionality Reduction of FA-MIV

Improving the prediction of going concern of Taiwanese listed companies using a hybrid of LASSO with data mining techniques

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