A comparison of SVR and NARX in financial time series forecasting

Engin Tas and Ayca Hatice Atli

International Journal of Computational Economics and Econometrics, 2022, vol. 12, issue 3, 303-320

Abstract: Machine learning techniques have become attractive due to their robustness and superiority in predicting future behaviour in various areas. This paper is aimed to predict future stock prices by applying a nonlinear autoregressive network with exogenous inputs (NARX) and support vector regression (SVR). For this aim, we use the daily trade data, including highest price, lowest price, closing price, and trade volume for the stocks with the highest transaction volumes from Borsa Istanbul (BIST). In order to evaluate the performance of the prediction models, various statistical measures are used. The experimental results indicate that the techniques used are quite capable of predicting the future price of a stock. Moreover, both methods are competitive with each other and have superiorities in different aspects.

Keywords: artificial learning; artificial neural networks; financial time series forecasting; nonlinear autoregressive network with exogenous inputs; NARX; support vector regression; SVR. (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijcome:v:12:y:2022:i:3:p:303-320

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