Journal Press India^®

Author Details ( * ) denotes Corresponding author

Artificial neural networks (ANNs) have revolutionized financial market forecasting by yielding high precision in predictive results owing to their abilities to learn from a complex set of non-linear and unstructured financial data. The current study is a novel work that attempts to systematically investigate the existing literature on employing ANNs for financial market forecasting through a bibliometric analysis of 235 articles and a keyword-based content analysis of influential studies published during the past two decades (2002-2022). The study contributes to the extant body of knowledge through unveiling research trends, prominent contributors, research collaborations, and prominent and emerging research areas in the concerned domain. Furthermore, the study uncovered challenges in designing and implementing ANNs, and recommended feasible solutions, also. Thus, the results of the current study can be taken as reference points by novice researchers and various financial market practitioners to direct their endeavours and advance in this field.

Keywords

Neural Networks; Financial Markets; Forecasting; Bibliometric

1. Andreou, P. C., Charalambous, C., & Martzoukos, S. H. (2008). Pricing and trading European options by combining artificial neural networks and parametric models with implied parameters. European Journal of Operational Research, 185(3), 1415–1433.
2. Arasu, B. S., Jeevananthan, M., Thamaraiselvan, N., & Janarthanan, B. (2014). Performances of data mining techniques in forecasting stock index – evidence from India and US. Journal of the National Science Foundation of Sri Lanka, 42(2), 177.
3. Atsalakis, G. S., & Valavanis, K. P. (2009). Surveying stock market forecasting techniques – Part II: Soft computing methods. Expert Systems with Applications, 36(3), 5932–5941.
4. Broadus, R. N. (1987). Toward a definition of “bibliometrics.” Scientometrics, 12(5–6), 373–379.
5. Chung, H., & Shin, K. (2018). Genetic algorithm-optimized long short-term memory network for stock market prediction. Sustainability, 10(10), 3765.
6. de Oliveira C. A. E., Coelho, G. P., & da Silva, A. E. A. (2021). Investment strategies applied to the Brazilian stock market: A methodology based on sentiment analysis with deep learning. Expert Systems with Applications, 184(C). Retrieved from https://dl.acm.org/doi/10.1016/j.eswa.2021.115470
7. Fedorova, E., Gilenko, E., & Dovzhenko, S. (2013). Bankruptcy prediction for Russian companies: Application of combined classifiers. Expert Systems with Applications, 40(18), 7285–7293.
8. Gandhmal, D. P., & Kumar, K. (2019). Systematic analysis and review of stock market prediction techniques. Computer Science Review, 34(14), 100190. Retrieved from DOI:10.1016/j.cosrev.2019.08.001.
9. García, V., Marqués, A. I., & Sánchez, J. S. (2015). An insight into the experimental design for credit risk and corporate bankruptcy prediction systems. Journal of Intelligent Information Systems, 44(1), 159–189.
10. Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D. (2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32(C), 100577. Retrieved from https://doi.org/10.1016/j.jbef.2021.100577
11. Guresen, E., Kayakutlu, G., & Daim, T. U. (2011). Using artificial neural network models in stock market index prediction. Expert Systems with Applications, 38(8), 10389–10397.
12. Hadavandi, E., Shavandi, H., & Ghanbari, A. (2010). Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting. Knowledge-Based Systems, 23(8), 800–808.
13. Huang, W., Lai, K. K., Nakamori, Y., Wang, S., & Yu, L. (2007). Neural networks in finance and economics forecasting. International Journal of Information Technology & Decision Making, 06(01), 113–140.
14. Huh, J. (2019). Pricing options with exponential Lévy neural network. Expert Systems with Applications, 127, 128–140.
15. Jiang, W. (2021). Applications of deep learning in stock market prediction: Recent progress. Expert Systems with Applications, 184(July), 115537.
16. Kara, Y., Acar Boyacioglu, M., & Baykan, Ö. K. (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert Systems with Applications, 38(5), 5311–5319.
17. Kim, H.-J., Jo, N.-O., & Shin, K.-S. (2016). Optimization of cluster-based evolutionary under sampling for the artificial neural networks in corporate bankruptcy prediction. Expert Systems with Applications, 59, 226–234.
18. Kim, K. (2003). Financial time series forecasting using support vector machines. Neurocomputing, 55(1–2), 307–319.
19. Korol, T. (2013). Early warning models against bankruptcy risk for Central European and Latin American enterprises. Economic Modelling, 31(1), 22–30.
20. Lahmiri, S. (2018). A technical analysis information fusion approach for stock price analysis and modeling. Fluctuation and Noise Letters, 17(01), 1850007.
21. Lajbcygier, P. (2004). Improving option pricing with the product constrained hybrid neural network. IEEE Transactions on Neural Networks, 15(2), 465–476.
22. Li, Y., & Ma, W. (2010). Applications of artificial neural networks in financial economics: A survey. 2010 International Symposium on Computational Intelligence and Design, 1, 211–214.
23. Liu, X., Cao, Y., Ma, C., & Shen, L. (2019). Wavelet-based option pricing: An empirical study. European Journal of Operational Research, 272(3), 1132–1142.
24. Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems with Applications, 42(1), 259–268.
25. Pendharkar, P. C. (2005). A threshold-varying artificial neural network approach for classification and its application to bankruptcy prediction problem. Computers & Operations Research, 32(10), 2561–2582.
26. Sharma, P., & Sharma, S. (2023). Mapping the intellectual structure of mobile payment research: A bibliometric analysis. SAGE Open, 13(3), 1-18.
27. Sharma, P., Agrawal, G., Arora, G., Sharma, D. K., & Chotia, V. (2023). Research on price discovery in financial securities: Trends and directions for future research. Journal of Risk and Financial Management, 16(9), 416.
28. Sharma, P., Sharma, D. K., & Gupta, P. (2023). Review of research on option pricing: A bibliometric analysis. Qualitative Research in Financial Markets. Emerald.
29. Tkáč, M., & Verner, R. (2016). Artificial neural networks in business: Two decades of research. Applied Soft Computing, 38, 788–804.
30. Tsai, C.-F., & Cheng, K.-C. (2012). Simple instance selection for bankruptcy prediction. Knowledge-Based Systems, 27, 333–342.
31. Tseng, C.-H., Cheng, S.-T., Wang, Y.-H., & Peng, J.-T. (2008). Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices. Physica A: Statistical Mechanics and Its Applications, 387(13), 3192–3200.
32. van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.
33. Verikas, A., Kalsyte, Z., Bacauskiene, M., & Gelzinis, A. (2010). Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey. Soft Computing, 14(9), 995–1010.
34. von Spreckelsen, C., von Mettenheim, H.-J., & 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.
35. Wang, Y.-H. (2009). Nonlinear neural network forecasting model for stock index option price: Hybrid GJR–GARCH approach. Expert Systems with Applications, 36(1), 564–570.
36. Wong, B. K., & Selvi, Y. (1998). Neural network applications in finance: A review and analysis of literature (1990–1996). Information & Management, 34(3), 129–139.