Keywords: Neural Networks, ANN, Analytics, Machine Learning.


Traditional statistical methods pose challenges in data analysis due to irregularity in the financial data. To improve accuracy, financial researchers use machine learning architectures for the past two decades. Neural Networks (NN) are a widely used architecture in financial research. Despite the wider usage, NN application in finance is yet to be well defined. Hence, this descriptive study classifies and examines the NN application in finance into four broad categories i.e., investment prediction, credit evaluation, financial distress, and other financial applications. Likewise, the review classifies the NN methods used under each category into standard, optimized and hybrid NN. Further, accuracy measures used by the research work widely differ, in turn, pose challenges for comparison of a NN under each category and reduces the scope of formalizing a theory to choose optimum network model under each category.

JEL Classification Codes: G1, G17, M150.

Author Biography

K. Riyazahmed, IMD

Assistant Professor, Shri Dharmasthala Manjunatheshwara, Institute for Management Development, Mysore, Karnataka, India


Altman, E., Marco, G., & Varetto, F. (1994). Corporate Distress Diagnosis: Comparisons Using Linear Discriminant Analysis and Neural Networks (the Italian Experience). Journal of Banking and Finance, 18(3), 505-529.

Aiqun, W., Zicong, H., Yilin, W., Kolivand, H., Balas, V. E., Paul, A., & Ramachandran, V. (2020). Risk assessment of logistics finance enterprises based on BP neural network and fuzzy mathematical model. Journal of Intelligent & Fuzzy Systems, 39(4), 5915–5925.

Atiya, A.F. (2001). Bankruptcy prediction for credit risk using neural networks: A survey and new results, IEEE Transactions on Neural Networks, 12(1), 929-935.

Baesens, B., Setiono, R., Mues, C., & Vanthienen, J. (2003). Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation. Management Science, 49(3), 312–329.

Barr, D., & Mani. G. (1994). Using neural nets to manage investments, AI EXPERT, 34(3), 16–22.

Barnes, B, M., & Lee, C, S, V. (2009). Effects of Macroeconomic and Firm-Specific Factors on Shareholder Wealth: Some Australian Evidence. The Journal of wealth management, 12 (1), 41-61.

Brockett, P. L., Golden, L. L., Jang, J., & Yang, C. (2006). A Comparison of Neural Network, Statistical Methods, and Variable Choice for Life Insurers’ Financial Distress Prediction. Journal of Risk & Insurance, 73(3), 397–419.

Brooks, C., Andreas, G, F., Hoepner, McMillan, D., Vivian, A, & Simen, C, W. (2019) Financial data science: the birth of a new financial research paradigm complementing econometrics? The European Journal of Finance, 25(17), 1627-1636,

Blynski, L., & Faseruk, A. (2006). Comparison of the effectiveness of option price forecasting: Black-Scholes vs. simple and hybrid neural networks. Journal of Financial Management & Analysis, 19(2), 46.

Cavalcantea, R, C., Brailerio, C, R., Souza, L, V., Nobrega, P, J., Oliveria, L, A. (2016). Computational Intelligence and financial markets: A survey and future directions. Expert systems with applications, 55, 194 – 211.

Chang, P. C., Liu, C. H., Fan, C. Y., Lin, J. L., & Lai, C. M. (2009). An Ensemble of Neural Networks for Stock Trading Decision Making. In: Huang DS., Jo KH., Lee HH., Kang HJ., Bevilacqua V. (eds) Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence. ICIC 2009. Lecture Notes in Computer Science, 5755.

Chen, F., & Sutcliffe, C. (2012). Pricing and Hedging Short Sterling Options Using Neural Networks. Intelligent Systems in Accounting, Finance & Management, 19(2), 128–149.

Chikolwa, B., & Chan, F. (2008). Determinants of Commercial Mortgage-Backed Securities Credit Ratings: Australian Evidence. International Journal of Strategic Property Management, 12(2), 69–94.

Chiang, W.C., Urban, T. L. & Baldridge, G.W. (1996). A neural network approach to mutual fund net asset value forecasting, Omega - International journal of management science, 24, 205–215.

Cifter, A., Yilmazer, S., & Cifter, E. (2009). Analysis of sectoral credit default cycle dependency with wavelet networks: Evidence from Turkey. Economic Modelling, 26(6), 1382–1388.

Cimpoeru, S. (2017). Using Self organizing maps for assessing systemic risk. Evidences form global economic crisis. The Bucharest University of Economic Studies.

Coakley, J., & Brown. C. (2000). Artificial neural networks in accounting and finance: Modelling issues. Intelligent Systems in Accounting Finance & Management,9(2).

Costnatino, F., Gravio, G, D., & Nonino, F. (2015). Project selection in project portfolio management: An artificial neural network model based on critical success factors. International journal of project management, 33, 1744 – 1754.

Dunis, C. L., Laws, J., Middleton, P. W., & Karathanasopoulos, A. (2015). Trading and hedging the corn/ethanol crush spread using time-varying leverage and nonlinear models. European Journal of Finance, 21(4), 352–375.

Fadlalla, A., & Lin, C. (2001) An analysis of the applications of neural networks in finance. Interfaces, 31(4), 112–122.

Feldman, K, & Kingdon, J. (1995). Neural networks and some applications to finance, Applied Mathematical Finance, 2(1), 17-42.

Gençay, R., & Gibson, R. (2007). Model Risk for European-Style Stock Index Options. IEEE Transactions on Neural Networks, 18(1), 193–202.

Gradojevic, N., Gençay, R., & Kukolj, D. (2009). Option Pricing With Modular Neural Networks. IEEE Transactions on Neural Networks, 20(4), 626–637.

Gupta, P., Chauhan, S. & Jaiswal, M.P. (2019). Classification of Smart City Research - a Descriptive Literature Review and Future Research Agenda. Information System Frontiers, 21, 661–685.

Gustafson, J., L. (2011). Moore’s Law. In: Padua D. Encyclopedia of Parallel Computing. Springer, Boston, MA.

Hutchison A.W, Lo A.W, & Poggio, T. (1994) A nonparametric approach to pricing and hedging derivative securities via learning networks. The journal of Finance, 49(3), 851-889.

Hajek, P. (2011). Municipal credit rating modelling by neural networks. Decision support systems, 51 (1), 108 – 118.

Hariri, R. H., Fredericks, E.M. & Bowers, K. M. (2019). Uncertainty in big data analytics: survey, opportunities, and challenges. Journal of Big Data,6.

Haefke, C., & Helmenstein, C. (2002). Index forecasting and model selection. International Journal of Intelligent Systems in Accounting Finance & Management, 11(2), 119–135.

Horobet, A., Belascu, L., Ionita, I., & Serban - Oprescu, A.-T. (2014). A Neural Networks Perspective on the Financial integration of European Capital Markets. Economic Computation & Economic Cybernetics Studies & Research, 48(1), 1–12

Huang, G., Huang, G, B., Shiji, S., & Youa, K. (2014). Trends in extreme learning machines: A review. Neural Networks. 61, 34 – 45.

Huang, J., Chai, J. & Cho, S. (2020). Deep learning in finance and banking: A literature review and classification. Frontiersof Business Research in China, 14.

Indro, D. C., Jiang, C. X., Patuwo, B. E., & Zhang, G. P. (1999). Predicting mutual fund performance using artificial neural networks. Omega, 27(3), 373-380.

Jan, M. N., & Ayub, U. (2019). Do the Fama and French Five-Factor Model Forecast Well Using Ann? Journal of Business Economics & Management, 20(1), 168–191.

Kaastra, I. & Boyd, M. (1996). Designing a Neural Network for Forecasting Financial and Economic Time Series. Neurocomputing, 10, 215-236.

Karathanasopoulos, A., Dunis, C., & Khalil, S. (2016). Modeling, forecasting, and trading with a new sliding window approach: the crack spread example. Quantitative Finance, 16(12), 1875–1886.

Karunananthan, S., Maxwell, L, J., & Welch, V. (2020) Protocol: When and how to replicate systematic reviews. Campbell Systematic Reviews. 16.

Krishnaswamy, C.R., Gilbert, E.W., & Pashley, M.M. (2000) Neural network applications in finance: A practical introduction. Financial Practice and Education, 10(1), 75-84.

Kriteenawong, C., Virk, H, H., Bangalore, S., Wang, Z., Johnson, W, K., Rachel. (2020). Machine learning prediction in cardiovascular diseases: a meta-analysis. Scientific reports, 10.

Kohler, M., Krzyżak, A., & Todorovic, N. (2010). Pricing of High-Dimensional American Options by Neural Networks. Mathematical Finance, 20(3), 383–410.

Koskivaara, E., & Back, B. (2007). Artificial Neural Network Assistant (ANNA) for Continuous Auditing and Monitoring of Financial Data. Journal of Emerging Technologies in Accounting, 4, 29–45.

Kim, K. (2004). Artificial neural networks with feature transformation based on domain knowledge for the prediction of stock index futures. Intelligent Systems in Accounting, Finance & Management, 12(3), 167–176.

Light, R.J., & Pillemer, D. B. (1984). Summing up: The science of reviewing research. Cambridge, MA: Harvard University Press.

Lin, C.-T., & Yeh, H.-Y. (2009). Empirical of the Taiwan stock index option price forecasting model - applied artificial neural network. Applied Economics, 41(15), 1965–1972.

Loterman, G., Brown, I., Martens, D., Mues, C., & Baesens, B. (2012). Benchmarking regression algorithms for loss given default modeling. International Journal of Forecasting, 28(1), 161–170.

Loukeris, N., & Eleftheriadis, I. (2015). Further Higher Moments in Portfolio Selection and A Priori Detection of Bankruptcy, Under Multi-layer Perceptron Neural Networks, Hybrid Neuro-genetic MLPs, and the Voted Perceptron. International Journal of Finance & Economics, 20(4), 341–361.

LV, D., Wang, D., Li, M., & Xiang, Y. (2020). DNN models based on dimensionality reduction for stock trading. Intelligent data analysis, 24(1), 19-45.

Manel, H. (2012). Prediction of Financial Distress for Tunisian Firms: A Comparative Study between Financial Analysis and Neuronal Analysis. Business Intelligence Journal, 5(2), 374–382.

Moher, D., Liberati, A., Tetzlaff, J., Altman, D, G., & The PRISMA Group. (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med. 6(7).

Moreno, D., & Olmeda, I. (2007). Is the predictability of emerging and developed stock markets really exploitable? European Journal of Operational Research, 182(1), 436–454.

Ngai, E. W. T., & Wat, F. K. T. (2002) A Literature Review and Classification of Electronic Commerce Research. Information & Management, 39, 415-429.

Neves, J. C., & Vieira, A. (2006). Improving bankruptcy prediction with Hidden Layer Learning Vector Quantization. European Accounting Review, 15(2), 253–271.

Omar, N., Johari, Z, A., & Smith, M. (2017). Predicting fraudulent financial reporting using artificial neural network. Journal of Financial Crime, 24(2), 362–387.

Ozbey, F., & Paksoy, S. (2020). Estimation of the XU100 Index Return Volatility with the Integration of GARCH Family Models and ANN. Business and economics research journal, 11(2), 385 – 396.

Petter, S., McLean, E. R. (2009). A meta-analytic assessment of the DeLone and McLean IS success model: An examination of IS success at the individual level. Information Management, 46(3), 159-166.

Pradhan, R., Pathak, K. K., & Singh, V. P. (2011). Z Score Reveals Credit Capacity: A Case Study of SBI. International Journal of Financial Management, 1(3), 72–78.

Qi, M., & Zhao, X. (2011). Comparison of modeling methods for Loss Given Default. Journal of banking and finance, 35(11). 2842-2855.

Roelofs, R., Shankar, V., Recht, B., Fridovich-Keil, S., Hardt, M., Miller, J & Schmidt, L. (2019). A Meta-Analysis of Overfitting in Machine Learning. Advances in Neural Information Processing Systems 32 (Neur IPS 2019), 1-11. Retrieved from

Safer, A. M. (2002). The application of neural networks to predict abnormal stock returns using insider trading data. Applied Stochastic Models in Business & Industry, 18(4), 381–389.

Sapna, O., Botti, V., & Argente, E. (2003). Application of neural networks to stock prediction in “pool” companies. Application of Artificial Intelligence 17(7):661–673.

Sariev, E., & Germano, G. (2020) Bayesian regularized artificial neural networks for the estimation of the probability of default. Quantitative Finance, 20(2), 311–328.

Sermpinis, G., Laws, J., & Dunis, C. L. (2013). Modeling and trading the realized volatility of the FTSE100 futures with higher-order neural networks. European Journal of Finance, 19(3), 165–179.

Shokraneh, F., & Adams, C. E. (2019). Study-based registers reduce waste in systematic reviewing: discussion and case report. Systematic Reviews, 8(1), 129.

Sperckelsen, C., Mettenheim, H., & Breitner, M. H. (2014). Real-Time Pricing and Hedging of Options on Currency Futures with Artificial Neural Networks. Journal of Forecasting, 33(6), 419–432.

Trinkle, B. S., & Baldwin, A. A. (2007). Interpretable credit model development via artificial neural networks. Intelligent Systems in Accounting, Finance & Management, 15(3/4), 123–147.

Vellido A., Lisboa, P. J., Vaughan, J. (1999). Neural networks in business: a survey of applications (1992–1998). Expert Syst Appl. 17(1), 51–70.

Wie Zhang, A., Qing Cao, & Schniederjans, M. J. (2004). Neural Network Earnings Per Share Forecasting Models: A Comparative Analysis of Alternative Methods. Decision Sciences, 35(2), 205–237.

Wanke, P., Kalam Azad, M. A., Barros, C. P., & Hadi, V. A. (2016). Predicting performance in ASEAN banks: an integrated fuzzy MCDM-neural network approach. Expert Systems, 33(3), 213–229.

Wang, A., Liu, Y., Isaeva, E., & Rocha, Á. (2020). Intelligent financial management of company based on neural network and fuzzy volatility evaluation. Journal of Intelligent & Fuzzy Systems, 38(6), 7215–7228.

Wei, K., Nakamori, Y., Wang, S., & Yu, L. (2007). Neural Networks in Finance and Economics Forecasting. International Journal of Information Technology &Decision Making, 6(1), 113-140.

Willer P.J., Giarola, V, A., Cardoso, C, A., & Pinheiro, N., T. (2020). Business Insolvency Forecasting Using Artificial Neural Networks. Management and Development, 17(2), 136–162.

Wong, B. K., & Selvi, Y. (1998). Neural network applications in finance: A review and analysis of literature (1990-1996). Information and Management, 34(3), 129–139.

Yang, Z. R., Platt, M. B., Platt, H. D (1999). Probabilistic neural networks in bankruptcy prediction. Journal of Business Research, 44(2), 67–74.

Ying-Hua, C., & Shih-Chin, W. (2013). Integration of Evolutionary Computing and Equity Valuation Models to Forecast Stock Values Based on Data Mining. Asia Pacific Management Review, 18(1), 63–78.

Zan, H., Chen, H., Hsu, C., Chen, W., & Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: a market comparative study. Decision Support Systems, 37(4), 543–558.

Zapart, C. A. (2003). Beyond Black–Scholes: A Neural Networks-Based Approach to Options Pricing. International Journal of Theoretical & Applied Finance, 6(5), 469.

How to Cite
Riyazahmed, K. (2021). NEURAL NETWORKS IN FINANCE: A DESCRIPTIVE SYSTEMATIC REVIEW. Indian Journal of Finance and Banking, 5(2), 1-27.
Research Paper/Theoretical Paper/Review Paper/Short Communication Paper