Design of a Financial Decision Support System based on Artificial Neural Networks for Stock Price Prediction


Sandeep Patalay,B. MadhusudhanRao,



Decision Support Systems (DSS),Stock Markets,Artificial Intelligence (AI),Machine Learning (ML),Mathematical Modeling (MM),


Stock markets are highly volatile by nature and difficult to predict due to the non-linear and complex nature of the market. A system that can forecast and predict the stock prices is of great value to individual investors who do not have sufficient knowledge to understand the complex dynamics involved in evaluating and predicting stock prices. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. Machine learning is widely being used in the financial domain including prediction of stock prices. Based on the extensive literature review in this domain, traditional methods of using Machine Learning techniques including Artificial Neural Networks (ANN) for stock price prediction have taken in to account only the Technical Features. The current machine learning models do not take in to account the Intrinsic or fundamental features of the stock; the results of such prediction models are not accurate and at best could predict an intraday price of stocks with high levels of Variance. Literature review in the domain of stock predictions has shown that future stock prices are seldom dependent on the past performance and technical indicators and they invariably depend on the fundamental value and macro-economic factors.In this paper, we propose development of anArtificial Intelligence based decision support system (DSS) for guiding individual investors to buy and sell stocks. The Financial decision support shall be based on mathematical modeling of the various financial parameters to predict stock prices on a long term basis with a reasonable degree of accuracy and eliminate the behavioral biases of human decisions.The ANNs in this study were trained using open source financial data of select stocks listed on the BSE/NSE. The results of this study are quite encouraging as the stock prices can be predicted at least one month in advance and are closer to the real-time market prices. This DSS has the potential to help millions of Individual Investors who can make their financial decisions on stocks using this system for a fraction of cost paid to corporate financial consultants and value eventually may contribute to a more efficient financial system.


I. Anbalagan, T., & Maheswari, S. U. (2014). Classification and prediction of stock
market index based on Fuzzy Metagraph. Procedia Computer Science, 47(C),
II. Banik, S., Khodadad Khan, A. F. M., & Anwer, M. (2014). Hybrid machine
learning technique for forecasting dhaka stock market timing decisions.
Computational Intelligence and Neuroscience, 2014.
III. Dase, R. K., & Pawar, D. D. (2010). Application of Artificial Neural Network for
stock market predictions: A review of literature. International Journal of
Machine Intelligence, 2(2), 14–17.
IV. Dunne, M. (2017). Stock Market Prediction Declaration of Originality. Dept of
Computer Science, University College Cork, 1(1), 10.
V. I. E. Diakoulakis , D. E. Koulouriotis, D. M. E. (2018). A Review of Stock
Market Prediction Using Computational Methods. SpringerLink, 1–9.
VI. Ican, Ö., & Çelik, T. B. (2017). Stock Market Prediction Performance of Neural
Networks: A Literature Review. International Journal of Economics and
Finance, 9(11), 100.
VII. Qiu, M., & Song, Y. (2016). Predicting the direction of stock market index
movement using an optimized artificial neural network model. PLoS ONE, 11(5),
VIII. Rudin, C. (2012). A Profitable Approach to Security Analysis Using Machine
Learning: An Application to the Prediction of Market Behavior Following
Earnings Reports. 15.097 Prediction: Machine Learning and Statistics (MITOCW),
1–22. Retrieved from
IX. Value and Satisfaction.Indian Journal of Science and Technology, Vol 8(33),
DOI: 10.17485/ijst/2015/v8i33/78280, December 2015.1-10.
X. Yong, C. C., & Taib, S. M. (2009). Designing a Decision Support System Model
for Stock Investment Strategy. October,

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