Syed Aziz Rasool, Adiqa Kausar Kiani


Artificial neural networks are extensively used to predict the financial time series. This study implements the neural network model for predicting the daily returns of the Pakistan Stock Exchange (PSE). Such an application for PSE is very rare. A multi-layer perception network is used for the model used in this study, while the network is trained using the Error Back Propagation algorithm. The results showed that the predictive power of the network was performed by the return of the previous day rather than the input of the first three days. Therefore, this study showed satisfactory results for PSE. In short, artificial intelligence can be used to give a better picture of stock market operators and can be used as an alternative or additional to predict financial variables.


Stock Market volatility, prediction, Neural network

Full Text:



Aghababaeyan et al., 2011 “Forecasting the Tehran Stock Market by Artificial Neural Network” International Journal of Advanced Computer Science and Applications(IJACSA), Special Issue on Artificial Intelligence, 2011.

Altman, E.I., G. Marco, and F. Varetto, “Corporate Distress Diagnosis: Comparisons Using Linear Discriminant Analysis and Neural Networks (The Italian Experience),” Journal of Banking and Finance, 18(3), May 1994, 505-29.

Cheh, John J; Weinberg, Randy S; Yook, Ken C. "An Application of an Artificial Neural Network Investment System to Predict Takeover Targets", Journal of Applied Business Research, Vol. 15 (4). p 33-45. Fall 1999.

Cinar, E. Mine and Lash, Nicholas A and Salchenberger, Linda M, Neural Networks: A New Tool for Predicting Thrift Failures (July 1, 1992). Decision Sciences, Volume 23, Issue 4, Pages 899–916, July 1992. Available at SSRN:

Cogger, Kenneth O; Koch, Paul D; Lander, Diane M. "A Neural Network Approach to Forecasting Volatile International Equity Markets, Advances in financial economics. Volume 3. Hirschey, Mark Marr, M. Wayne, eds., Greenwich, Conn. and London: JAI Press. p 117-57, 1997.

Cooper, John C B. "Artificial Neural Networks versus Multivariate Statistics: An Application from Economics", Journal of Applied Statistics, Vol. 26 (8). p 909-21, December 1999.

Collins, Ghosh and Scofield, "An application of a multiple neural network learning system to emulation of mortgage underwriting judgements," IEEE 1988 International Conference on Neural Networks, San Diego, CA, USA, 1988, pp. 459-466 vol.2.

doi: 10.1109/ICNN.1988.23960

Desai, J., A. Trivedi and N.A. Joshi, 2012. Forecasting of stock market indices using artificial neural network. Shri Chimanbhai Patel Institutes, Ahmedabad Working Paper No. CPI/MBA/2013/0003

Friedman, J. and Y. Shachmurove, “Dynamic Linkages Among European Stock Markets” in Advances in International Stock Market Relationships and Interactions, J. Doukas ed., Greenwood Publishing, 1996, forthcoming.

Friedman, J. and Y. Shachmurove, “Co-Movements of Major European Community Stock Markets: A Vector Autoregression Analysis,” Global Finance Journal, 7(2), forthcoming 1997.

Garcia, Rene; Gencay, Ramazan. "Pricing and Hedging Derivative Securities with Neural Networks and a Homogeneity Hint", Journal of Econometrics, Vol. 94 (1-2). p 93-115. Jan.-Feb. 2000.

Gençtürk, M., 2009. Impact of macroeconomic factors on stock prices during the financial crisis period. Suleyman Demirel University Journal of Faculty of Economics & Administrative Sciences, 14 (1): 127-136.

Hamm, Lonnie; Brorsen, B Wade. "Trading Futures Markets Based on Signals from a Neural Network", Applied Economics Letters, Vol. 7 (2). p 137-40, February 2000.

Hawley, D.D., J.D. Johnson, and D. Raina, “Artificial Neural Systems: A New Tool for Financial Decision-Making,” Financial Analysis Journal, Nov/Dec, 1990, 63-72.

Hu, Michael Y; Tsoukalas, Christos. "Combining Conditional Volatility Forecasts Using Neural Networks: An Application to the EMS Exchange Rates", Journal of International Financial Markets, Institutions & Money, Vol. 9 (4). p 407-22. August 1999.

Huang, GB., Wang, D.H. & Lan, Y. Int. J. Mach. Learn. & Cyber. (2011) 2: 107.

Hutchinson, James M. Andrew W.Lo and Tomaso Poggio (1994), “A Non-Parametric Approach to Pricing and Hedging Derivatives Securities Via Learning Networks” – The Journal of Finance, Vol.XLIX, No.3, July.

Kimoto, T., K. Asakawa, M. Yoda and M. Takeoka, 1990. In: Stock market prediction system with modular neural network. Proceedings of the International Joint Conference on Neural Networks. pp: 1-6.

Li, F. and C. Liu, 2009. Application study of BP neural network on stock market prediction. Ninth International Conference on Hybrid Intelligent Systems. IEEE, pp: 174-178.

Mallaris and Linda (1996) Using neural networks to forecast the S&P 100 implied volatility Neurocomputing 10(2); 183-195.

Mizuno, H., M. Kosaka, H. Yajima and N. Komoda, 1998. Application of neural network to technical analysis of stock market prediction. Studies in Informatics and Control, 7(3): 111-120.Financial Risk and Management Reviews, 2015, 1(2):53-67

Moshiri, Saeed; Cameron, Norman E; Scuse, David. "Static, Dynamic, and Hybrid Neural Networks in Forecasting Inflation", Computational Economics, Vol. 14 (3). p 219-35. December 1999.

O’connor, N. and G.M. Michael, 2005. A neural network approach to predicting stock exchange movements using external factors. Knowledge-Based Systems, 19(5): 371-378.

Oh, H., Labianca, G. & Chung, M.-H. (2006), A multilevel model of group social capital. Academy of Management Review, 31(3), 569-82.

Phua, P.K.H., D. Ming and W. Lin, 2000. Neural network with genetic algorithms for stocks prediction. Fifth Conference of the Association of Asian-Pacific Operations Research Societies Proceedings, 5th - 7th July, Singapore.

Shachmurove, Y., “Dynamic Linkages Among Latin American and Other Major World Stcok Markets,” in Research in International Business and Finance: Financial Issues in Emerging Capital Markets, John Doukas and Larry Lang eds., JAI Press Inc., forthcoming 1996.

Shachmurove, Y. and Witkowska, D. (2000). Utilizing artificial neural network model to predict stock markets. Retrieved July 25, 2008, from

Shtub, Avraham; Versano, Ronen. "Estimating the Cost of Steel Pipe Bending, a Comparison between Neural Networks and Regression Analysis", International Journal of Production Economics, Vol. 62 (3). p 201-07, September 1999.

Terna, Pietro. "Neural Network for Economic and Financial Modelling: Summing Up Ideas Emerging from Agent Based Simulation and Introducing an Artificial Laboratory", Cognitive Economics, Viale, Riccardo, ed., LaSCoMES Series, vol. 1. Torino: La Rosa. p 271-309. 1997.

Tkacz, Greg, 2001. "Neural network forecasting of Canadian GDP growth," International Journal of Forecasting, Elsevier, vol. 17(1), pages 57-69.

White, H., “Economic Prediction Using Neural Networks: The Case of IBM Daily Stock Returns,” Proceedings of the IEEE International Conference of Neural Networks, July 1988, II451-II458.

White, H., “Option Pricing in Modern Finance Theory and the Relevance of Artificial Neural Networks,” Discussion Paper, Econometrics Workshop, March 1996.

Yoon, Y. and G. Swales, “Predicting Stock Price Performance,” Proceeding of the 24th Hawaii International Conference on System Sciences, 4, 156-162, 1997.

Yoon, Y. and S. George, 1991. Predicting stock price performance: A neural network approach. Proceedings of the 24th Annual Hawaii International Conference on System Sciences, 4: 156-162.

Yoon, Y., G. Swales and T.M. Margvio, 1993. A comparison of discriminant analysis versus artificial neural networks. Journal of the Operational Research Society, 44(1): 51-60



  • There are currently no refbacks.

Creative Commons License
Sarhad Journal of Management Sciences by Sarhad University of Science & Information Technology is licensed under a Creative Commons Attribution 4.0 International License.
Based on a work at