 Stock Market Prediction using Artificial Neural Networks. Case Study of TAL1T, Nasdaq OMX Baltic StockHakob Grigoryan ()
Database Systems Journal, 2015, vol. 6, issue 2, 14-23 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. Keywords: artificial neural networks; NARX; principal component analysis; financial time series; stock prediction (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:aes:dbjour:v:6:y:2015:i:2:p:14-23 Access Statistics for this article Database Systems Journal is currently edited by Ion Lungu More articles in Database Systems Journal from Academy of Economic Studies - Bucharest, Romania Contact information at EDIRC. |
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