Stock Market Prediction Performance of Neural Networks: A Literature Review

Özgür İcan () and Taha Bugra Celik

International Journal of Economics and Finance, 2017, vol. 9, issue 11, 100-108

Abstract: In this paper, previous studies featuring an artificial neural networks based prediction model have been reviewed. The main purpose of this review is to examine studies which use directional prediction accuracy (also known as hit ratio) or profitability of the model as a benchmark since other forecast error measures - namely mean absolute deviation (MAD), root mean squared error (RMSE), mean absolute error (MAE) and mean squared error (MSE) - have been criticized for the argument that they are not able to actually show how useful the prediction model is, in terms of financial gains (i.e. for practical usage). In order to meet the publication selection criteria mentioned above, a large number of publications have been examined and 25 of papers satisfying the criteria are selected for comparison. Classification of the eligible papers are summarized in a table format for future studies.

Keywords: ANN (Artificial Neural Networks); financial times series forecasting; stock markets prediction; review (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:ibn:ijefaa:v:9:y:2017:i:11:p:100-108

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