Provide intelligent classification model based on perceptron artificial neural network (MLP) and hierarchical analysis (AHP) in digital marketing services to prioritize liquidity and investment risk

Subject Areas : Journal of Investment Knowledge

Alireza Ashouri Roudposhti ¹ , Hormoz Mehrani ² , Karim Hamdi ³

1 - Department of Business Management, Faculty of Management and Economics, University of Science and Research, Islamic Azad University, Tehran, Iran
2 - assistant profesoor
3 - Associate professor

Received: 2020-12-16 Accepted : 2021-01-10 Published : 2021-03-21

Keywords: "Perceptron Neural Network (MLP)", "Artificial Intelligence", "AHP", "Digital Marketing Services", "Liquidity Risk",

Abstract :

The present study, using machine learning and polling techniques, attempts to examine the automated strategic model in order to classify and explore the ideas presented about specific services that have been studied in this area in the field of investment. Provide results in digital marketing services. The neural network-based model, by identifying related opinions, measures different characteristics at different levels of evaluation and automatically categorizes opinions depending on the quality of the presentation. Financial crises in the banking system are usually due to the inability to manage financial risks and liquidity, which is a factor in the lack of transparency and ability to manage capital. Thus, the existence of such uncertainties has reduced the interest of investors in industrial and executive partnerships. This article has been established with the aim of identifying the factors affecting liquidity risk and also providing an intelligent model for predicting and classifying liquidity risk factors, identifying and prioritizing the factors involved. For this purpose, the method of intelligent measurement using perceptron neural network (MLP) has been used, which is considered as a practical approach to artificial intelligence. For this purpose, the necessary studies on financial information and liquidity in Bank Mellat branches in Tehran (consisting of 36 branches) have been considered and for the sample population, a random cluster set of 374 selected customers and investors has been used.

References:

• Abolhassani, A., Hassani Moghadam, R. (2006). Investigating the types of risk and its management methods in the interest-free banking system of Iran. Islamic Economics, 30 (2): 145-172. [In Persian]


• Aggarwal C.C. (2018). Neural Networks and Deep Learning: A Textbook. Springer, 520 p.


• Ahmadi, A., Ahmadi Jashfaqani, H., Hastiani, A. (2016). The effect of credit risk on the performance of the banking system: An interbank study with Panel VAR approach. Financial Economics Quarterly, 10 (34): 131-152. [In Persian]


• Alaraj M., Abbod F. (2016). A New Hybrid Ensemble Credit Scoring Model Based on Classifiers Consensus System Approach. Expert Systems with Applications, 64: 36-55.


• Arabi, S.H., Shahjamali, M. (2019). Ranking of credit risk management tools in interest-free banking using AHP technique. Scientific Quarterly of Islamic Economics and Banking, 28 (3): 7-39. [In Persian]


• Bagheri N., Haghshenaskashani, F. (2019). Credit risk assessment of urban cooperatives using neural network method. Journal of Economics and Urban Management, 6(4): 17-33. [In Persian]


• Bidgoli, M., Taghavi, M., Ismailzadeh Moghari, A., Damankeside, M. (2018). Experimental test of the effect of business climate risk on the relationship between credit risk and financial performance in the Iranian banking industry. Financial Economics Quarterly, 13 (48): 1-35. [In Persian]


• Brunelli M. (2014). Introduction to the Analytic Hierarchy Process. Springer, 192 p.


• Cucinelli D., (2013). The Determinants of Bank Liquidity Risk within the Context of Euro Area. Interdisciplinary Journal of Research in Business, 2(10): 51-64.


• Ebrahimi M., Daryabar E. (2012). Credit risk management in the banking system - data envelopment analysis approach and Logistic regression and neural network. Investment Knowledge Quarterly, 1(2): 35-67. [In Persian]


• El Shahat A. (2014). Artificial Neural Network (ANN): Smart & Energy Systems Applications. Scholars' Press, 192 p.


• Eletter S.F., Yaseen S.G., Elrefae G.A. (2010). Neuro-based artificial intelligence model for loan decisions. American Journal of Economics and Business Administration, 2(1): 27-36.


• Eyüboğlu, K., Çelik, P. (2016).Financial Performance Evaluation of Turkish Energy Companies with Fuzzy AHP and Fuzzy TOPSIS Methods. Business and Economics Research Journal, 7(3): 21-38.


• Gunduz, M., Alfar, M. (2019). Integration of innovation through analytical hierarchy process (AHP) in project management and planning. Technological and Economic Development of Economy, 25: 258-276.


• Hacioglu, U., Dincer, H. (2015). A Comparative Performance Evaluation on Bipolar Risks in Emerging Capital Markets Using Fuzzy AHP-TOPSIS and VIKOR Approaches. Inzinerine Ekonomika-Engineering Economics, 26(2): 118-129.


• Hassoun H.M. (2003). Fundamentals of Artificial Neural Networks. A Bradford Book, 540 p.


• Iqbal, Anjum,(2012), “Liquidity Risk Management : A Comparative Study between Conventional and Islamic Banks of Pakistan”, Global Journal of Management and Business Research,Vol.12, Issue.5, 55-64.


• Ismailzadeh Moghari, A., Javanmardi, H. (2017). Designing a suitable model for liquidity management and risk forecasting in Bank Saderat Iran. Financial Economics Quarterly, 11 (39): 171-191. [In Persian]


• Khashei M., Torbat Sh. (2019). A Hybrid Intelligent Classification Model Based on Multilayer Perceptron Neural Networks and Fuzzy Regression for Credit Scoring. Computational Methods in Engineering, 37(2): 97-111. [In Persian]


• Koutanaei, F. N., Sajedi, H., Khanbabaei, M. (2015). A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring. Journal of Retailing and Consumer Services, 27, 11-23.


• Mahmoudi, Z., Kheirandish, M., Ahmadi, M. (2017). Identifying and ranking the factors affecting the credit risk of legal customers from the perspective of managers and experts of Bank Mellat in Bandar Abbas using the AHP model. Journal of Strategic Studies in Humanities and Islamic Sciences, 7 (1): 59-89. [In Persian]


• Mehrara, M., Mehranfar, M. (2013). Banking performance and macroeconomic factors in risk management. Economic Modeling Quarterly, 7 (1): 21-37. [In Persian]


• Moazeni, G. (2011). Comparison of growth and price stocks risk in Tehran Stock Exchange. Master Thesis, Faculty of Management and Accounting, University of Isfahan, Isfahan, Iran. [In Persian]


• Nazarpour, M., Rezaei, A. (2013). Credit risk management in Islamic banking with the approach of reviewing contracts and the payment pattern of facilities. Islamic Financial Research, 4: 123-156. [In Persian]


• Rahmani, G., Ismaili, A. (2010). Manage credit risk coverage using credit default swaps. Presented at the Third International Conference on the Development of the Financing System in Iran, Al-Zahra University, Tehran, Iran. [In Persian]


• Shou, H. (2018). Research on Measurement of China's Systemically Important Banks Based on AHP-Entropy Model. Advances in Social Science, Education and Humanities Research, 182: 618-621.


• Stojčić, M., Zavadskas, E.K., Pamučar, D., Stević, Ž. and Mardani, A. (2019), Application of MCDM Methods in Sustainability Engineering: A Literature Review 2008–2018, Symmetry, 11(3) 350.


• Sun, C. (2010). A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Systems with Applications, 37: 7745-7754.


• Wang, S. (2019). Research on liquidity risk measurment method of financial block chain based on Sand box. In: 2019 International Conference on Smart Grid and Electrical Automation, doi: 10.1109/ICSGEA.2019.00111.


• Wu, W., Kou, G., Peng, Y., Ergu, D. (2012). Improved AHP-group decision making for investment strategy selection, Technological and Economic Development of Economy, 18(2): 299-316.


• Yadav, R., Ananad, S. (2020). Measuring the financial performance of MFIs in India using fuzzy logic AHP approach. Studies in Indian Place Names, 40(40): 2670-2678.


• Yu, A., Jia, Z., Zhang, W., Deng, K., Herrera, F. (2020). A Dynamic Credit Index System for TSMEs in China Using the Delphi and Analytic Hierarchy Process (AHP) Methods. Sustainability, 12: 1715.


• Zopounidis C., Doumpos M. (2002). Multi-criteria decision aid in financial decision making: methodologies and literature review. Journal of Multi-Criteria Decision Analysis, 11(4-5): 167-186.


• Zopounidis C., Doumpos M. (2017). Multiple Criteria Decision Making: Applications in Management and Engineering. Springer, 211 p.