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Stock Market Prediction using Artificial Neural Networks. Case Study of TAL1T, Nasdaq OMX Baltic Stock

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  • Hakob GRIGORYAN

    (University of Economic Studies, Bucharest, Romania)

Abstract

Predicting financial market changes is an important issue in time series analysis, receiving an increasing attention in last two decades. The combined prediction model, based on artificial neural networks (ANNs) with principal component analysis (PCA) for financial time series forecasting is presented in this work. In the modeling step, technical analysis has been conducted to select technical indicators. Then PCA approach was applied to extract the principal components from the variables for the training step. Finally, the ANN-based model called NARX was used to train the data and perform the time series forecast. TAL1T stock of Nasdaq OMX Baltic stock exchange was used as a case study. The mean square error (MSE) measure was used to evaluate the performances of proposed model. The experimental results lead to the conclusion that the proposed model can be successfully used as an alternative method to standard statistical techniques for financial time series forecasting.

Suggested Citation

  • Hakob GRIGORYAN, 2015. "Stock Market Prediction using Artificial Neural Networks. Case Study of TAL1T, Nasdaq OMX Baltic Stock," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 6(2), pages 14-23, October.
    • Handle: RePEc:aes:dbjour:v:6:y:2015:i:2:p:14-23
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    References listed on IDEAS

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    Cited by:

    1. Dinesh K. Sharma & H. S. Hota & Kate Brown & Richa Handa, 2022. "Integration of genetic algorithm with artificial neural network for stock market forecasting," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 828-841, June.
    2. Edson Kambeu, 2019. "Trading volume as a predictor of market movement: An application of Logistic regression in the R environment," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 8(2), pages 57-69, April.
    3. Anders Nõu & Darya Lapitskaya & Mustafa Hakan Eratalay & Rajesh Sharma, 2021. "Predicting Stock Return And Volatility With Machine Learning And Econometric Models: A Comparative Case Study Of The Baltic Stock Market," University of Tartu - Faculty of Economics and Business Administration Working Paper Series 135, Faculty of Economics and Business Administration, University of Tartu (Estonia).
    4. Jia LU & Noor Muhammad SHAZEMEEN & Raimonda MARTINKUTE-KAULIENE, 2020. "Portfolio Decision Using Time Series Prediction and Multi-objective Optimization," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 118-130, December.

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