IDEAS home Printed from https://ideas.repec.org/a/aes/dbjour/v6y2015i2p14-23.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.dbjournal.ro/archive/20/20_2.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    2. Kuan, Chung-Ming & Liu, Tung, 1995. "Forecasting Exchange Rates Using Feedforward and Recurrent Neural Networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(4), pages 347-364, Oct.-Dec..
    3. Olson, Dennis & Mossman, Charles, 2003. "Neural network forecasts of Canadian stock returns using accounting ratios," International Journal of Forecasting, Elsevier, vol. 19(3), pages 453-465.
    4. Ghiassi, M. & Saidane, H. & Zimbra, D.K., 2005. "A dynamic artificial neural network model for forecasting time series events," International Journal of Forecasting, Elsevier, vol. 21(2), pages 341-362.
    5. Wun-Hua Chen & Jen-Ying Shih & Soushan Wu, 2006. "Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets," International Journal of Electronic Finance, Inderscience Enterprises Ltd, vol. 1(1), pages 49-67.
    6. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    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. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jordan French, 2016. "Back to the Future Betas: Empirical Asset Pricing of US and Southeast Asian Markets," IJFS, MDPI, vol. 4(3), pages 1-13, July.
    2. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    3. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    4. Kizilaslan, Recep & Freund, Steven & Iseri, Ali, 2016. "A data analytic approach to forecasting daily stock returns in an emerging marketAuthor-Name: Oztekin, Asil," European Journal of Operational Research, Elsevier, vol. 253(3), pages 697-710.
    5. García Ruiz Reyna Susana & Cruz Aké Salvador & Venegas Martínez Francisco, 2014. "Una medida de eficiencia de mercado: Un enfoque de teoría de la información," Contaduría y Administración, Accounting and Management, vol. 59(4), pages 137-166, octubre-d.
    6. Alagidede, Paul & Panagiotidis, Theodore, 2009. "Modelling stock returns in Africa's emerging equity markets," International Review of Financial Analysis, Elsevier, vol. 18(1-2), pages 1-11, March.
    7. Aggarwal, Raj & Mougoue, Mbodja, 1998. "Common Stochastic Trends among Asian Currencies: Evidence for Japan, ASEANs, and the Asian Tigers," Review of Quantitative Finance and Accounting, Springer, vol. 10(2), pages 193-206, March.
    8. Emmanuel O. Nwosu & Anthony Orji & Ogomegbunam Anagwu, 2013. "African Emerging Equity Markets Re-examined: Testing the Weak Form Efficiency Theory," African Development Review, African Development Bank, vol. 25(4), pages 485-498.
    9. Chia-Lin Chang & Shu-Han Hsu & Michael McAleer, 2018. "An Event Study Analysis of Political Events, Disasters, and Accidents for Chinese Tourists to Taiwan," Sustainability, MDPI, vol. 10(11), pages 1-77, November.
    10. Kapil Gupta & Balwinder Singh, 2009. "Information Memory and Pricing Efficiency of Futures Contracts," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 8(2), pages 191-250, May.
    11. Nathan Jensen, 2007. "International institutions and market expectations: Stock price responses to the WTO ruling on the 2002 U.S. steel tariffs," The Review of International Organizations, Springer, vol. 2(3), pages 261-280, September.
    12. Quynh-Trang Nguyen & John Francis Diaz & Jo-Hui Chen & Ming-Yen Lee, 2019. "Fractional Integration in Corporate Social Responsibility Indices: A FIGARCH and HYGARCH Approach," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 9(7), pages 836-850, July.
    13. Yok-Yong Lee & M. H. Yahya & A. M. Bany-Ariffin & S. Aslam, 2018. "Leverage Effect and Switching of Market Efficiency Post Goods and Services Tax (GST) Imposition," International Business Research, Canadian Center of Science and Education, vol. 11(3), pages 162-178, March.
    14. Chang, Chia-Lin & McAleer, Michael & Wang, Yanghuiting, 2018. "Testing Co-Volatility spillovers for natural gas spot, futures and ETF spot using dynamic conditional covariances," Energy, Elsevier, vol. 151(C), pages 984-997.
    15. Rounaghi, Mohammad Mahdi & Nassir Zadeh, Farzaneh, 2016. "Investigation of market efficiency and Financial Stability between S&P 500 and London Stock Exchange: Monthly and yearly Forecasting of Time Series Stock Returns using ARMA model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 10-21.
    16. Christian Pierdzioch & Andrea Schertler, 2007. "Sources of Predictability of European Stock Markets for High-technology Firms," The European Journal of Finance, Taylor & Francis Journals, vol. 13(1), pages 1-27.
    17. Dutta, Shantanu & Essaddam, Naceur & Kumar, Vinod & Saadi, Samir, 2017. "How does electronic trading affect efficiency of stock market and conditional volatility? Evidence from Toronto Stock Exchange," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 867-877.
    18. Shi, Leilei, 2006. "Does security transaction volume–price behavior resemble a probability wave?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 366(C), pages 419-436.
    19. Gartner, Manfred & Wellershoff, Klaus W., 1995. "Is there an election cycle in American stock returns?," International Review of Economics & Finance, Elsevier, vol. 4(4), pages 387-410.
    20. Uctum, Remzi & Renou-Maissant, Patricia & Prat, Georges & Lecarpentier-Moyal, Sylvie, 2017. "Persistence of announcement effects on the intraday volatility of stock returns: Evidence from individual data," Review of Financial Economics, Elsevier, vol. 35(C), pages 43-56.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aes:dbjour:v:6:y:2015:i:2:p:14-23. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Adela Bara (email available below). General contact details of provider: https://edirc.repec.org/data/aseeero.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.