An application of neural networks in microeconomics: Input–output mapping in a power generation subsector of the US electricity industry

Erbas, Bahar Çelikkol; Stefanou, Spiro E.

doi:10.1016/j.eswa.2007.12.062

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…The neural network models have the highest average correct classification (ACC) rate when compared with other traditional techniques, such as DA and LR, taking into accounts that results were very close. A few credit-scoring models have investigated the use probabilistic neural nets (PNNs) (Ganchev et al, 2007;Zekic-Susac et al, 2004;Masters, 1995), whilst many scoring models applying multi-layer feed-forward nets (MLFNs) have been used (Erbas and Stefanou, 2008;Dimla and Lister, 2000;West, 2000;Reed and Marks, 1999;Desai et al, 1996;Bishop, 1995).…”

Section: Introductionmentioning

confidence: 99%

An evaluation of alternative scoring models in private banking

Abdou

2009

The Journal of Risk Finance

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Purpose -This paper aims to investigate the efficiency and effectiveness of alternative credit-scoring models for consumer loans in the banking sector. In particular, the focus is upon the financial risks associated with both the efficiency of alternative models in terms of correct classification rates, and their effectiveness in terms of misclassification costs (MCs). Design/methodology/approach -A data set of 630 loan applicants was provided by an Egyptian private bank. A two-thirds training sample was selected for building the proposed models, leaving a one-third testing sample to evaluate the predictive ability of the models. In this paper, an investigation is conducted into both neural nets (NNs), such as probabilistic and multi-layer feed-forward neural nets, and conventional techniques, such as the weight of evidence measure, discriminant analysis and logistic regression. Findings -The results revealed that a best net search, which selected a multi-layer feed-forward net with five nodes, generated both the most efficient classification rate and the most effective MC. In general, NNs gave better average correct classification rates and lower MCs than traditional techniques. Practical implications -By reducing the financial risks associated with loan defaults, banks can achieve a more effective management of such a crucial component of their operations, namely, the provision of consumer loans. Originality/value -The use of NNs and conventional techniques in evaluating consumer loans within the Egyptian private banking sector utilizes rigorous techniques in an environment which merits investigation.

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Section: Introductionmentioning

confidence: 99%

An evaluation of alternative scoring models in private banking

Abdou

2009

The Journal of Risk Finance

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“…This volatility is a product of speculative activity, and is also strongly influenced by abrupt changes in supply and demand. According to Erbas and Stefanou (2009), the literature indicates that while the potential economic benefits of neural networks cover a wide spectrum of fields, the most prevalent applications in economics focus in their predictive power. In this context, Hadavandi et al (2010) highlighted the critical importance of accurate stock price forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

Neural Network-Based Predictive Models for Stock Market Index Forecasting

Chahuán-Jiménez

2024

JRFM

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The stock market, characterised by its complexity and dynamic nature, presents significant challenges for predictive analytics. This research compares the effectiveness of neural network models in predicting the S&P500 index, recognising that a critical component of financial decision making is market volatility. The research examines neural network models such as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Artificial Neural Network (ANN), Recurrent Neural Network (RNN), and Gated Recurrent Unit (GRU), taking into account their individual characteristics of pattern recognition, sequential data processing, and handling of nonlinear relationships. These models are analysed using key performance indicators such as the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Directional Accuracy, a metric considered essential for prediction in both the training and testing phases of this research. The results show that although each model has its own advantages, the GRU and CNN models perform particularly well according to these metrics. GRU has the lowest error metrics, indicating its robustness in accurate prediction, while CNN has the highest directional accuracy in testing, indicating its efficiency in data processing. This study highlights the potential of combining metrics for neural network models for consideration when making decisions due to the changing dynamics of the stock market.

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“…Applications of credit scoring have been widely used in different fields, including accounting and finance (Altman and Narayman, 1997;Pendharkar, 2005;Landajo et al, 2007), marketing (Kumar et al, 1995;Thieme et al, 2000;Chiang et al, 2006) IMEFM 7,1 and general applications (Walczak and Sincich, 1999;Usha, 2005;Nikolopoulos et al, 2007). In the area of accounting and finance, credit scoring applications have been used for different purposes, including bankruptcy prediction (Zhang et al, 1999;Tsai and Wu, 2008;Etemadi et al, 2009;Nanni and Lumini, 2009), scoring applications (Huang et al, 2006;Crook et al, 2007;Erbas and Stefanou, 2008) and classification problems (Ong et al, 2005;Laha, 2007;Ben-David and Frank, 2009).…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the area of accounting and finance, credit scoring applications have been used for different purposes including: bankruptcy prediction (Zhang et al 1999;Tsai and Wu, 2008;Etemadi et al, 2009;Nanni and Lumini, 2009); scoring applications (Huang et al, 2006;Crook et al, 2007;Huang et al, 2007;Erbas and Stefanou, 2008)) and classification problems (Ong et al, 2005;Laha, 2007;Ben-David and Frank, 2009). …”

Section: Literature Reviewmentioning

confidence: 99%

Would credit scoring work for Islamic finance? A neural network approach

Abdou

Alam

Mulkeen

2014

International Journal of Islamic and Middle Eastern Finance and Management

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Purpose -The main aim of this paper is to distinguish whether the decision making process of the Islamic financial houses in the UK can be improved through the use of credit scoring modeling techniques as opposed to the currently used judgmental approaches. Subsidiary aims are to identify how scoring models can reclassify accepted applicants who later are considered as having bad credit and how many of the rejected applicants are later considered as having good credit; and highlight significant variables that are crucial in terms of accepting and rejecting applicants which can further aid the decision making process.Design/methodology/approach -A real data-set of 487 applicants are used consisting of 336 accepted credit applications and 151 rejected credit applications make to an Islamic finance house in the UK. In order to build the proposed scoring models, the data-set is divided into training and hold-out sub-set. The training sub-set is used to build the scoring models and the hold-out sub-set is used to test the predictive capabilities of the scoring models.70 percent of the overall applicants will be used for the training sub-set and 30 percent will be used for the testing sub-set. Three statistical modeling techniques namely Discriminant Analysis (DA), Logistic Regression (LR) and Multi-layer Perceptron (MP) neural network are used to build the proposed scoring models. Findings -Our findings reveal that the LR model has the highest Correct Classification (CC) rate in the training sub-set whereas MP outperforms other techniques and has the highest CC rate in the hold-out sub-set. MP also outperforms other techniques in terms of predicting the rejected credit applications and has the lowest Misclassification Cost (MC) above other techniques. In addition, results from MP models show that monthly expenses, age and marital status are identified as the key factors affecting the decision making process. Research limitations/implications -Although our sample is small and restricted to an Islamic Finance house in the UK the results are robust. Future research could consider enlarging the sample in the UK and also internationally allowing for cultural differences to be identified. The results indicate that the scoring models can be of great benefit to Islamic finance houses in regards to their decision making processes of accepting and rejecting new credit applications and thus improve their efficiency and effectiveness. Originality/value -Our contribution is the first to apply credit scoring modeling techniques in Islamic Finance. Also in building a scoring model our application applies a different approach by using accepted and rejected credit applications instead of good and bad credit histories. This identifies opportunity costs of misclassifying credit applications as rejected.

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An application of neural networks in microeconomics: Input–output mapping in a power generation subsector of the US electricity industry

Cited by 7 publications

References 20 publications

An evaluation of alternative scoring models in private banking

An evaluation of alternative scoring models in private banking

Neural Network-Based Predictive Models for Stock Market Index Forecasting

Would credit scoring work for Islamic finance? A neural network approach

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