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Informatics 2018, 5(3), 36; https://doi.org/10.3390/informatics5030036

An Empirical Study on Importance of Modeling Parameters and Trading Volume-Based Features in Daily Stock Trading Using Neural Networks

School of IT Convergence, University of Ulsan, 93 Daehak-ro, Namgu, Ulsan 44610, Korea
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Received: 20 June 2018 / Revised: 2 August 2018 / Accepted: 14 August 2018 / Published: 17 August 2018
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Abstract

There have been many machine learning-based studies to forecast stock price trends. These studies attempted to extract input features mostly from the price information with little focus on the trading volume information. In addition, modeling parameters to specify a learning problem have not been intensively investigated. We herein develop an improved method by handling those limitations. Specifically, we generated input variables by considering both price and volume information with even weight. We also defined three modeling parameters: the input and the target window sizes and the profit threshold. These specify the input and target variables, between which the underlying functions are learned by multilayer perceptrons and support vector machines. We tested our approach over six stocks and 15 years and compared with the expected performance over all considered parameter specifications. Our approach dramatically improved the prediction accuracy over the expected performance. In addition, our approach was shown to be stably more profitable than both the expected performance and the buy-and-hold strategy. On the other hand, the performance was degraded when the input variables generated from the trading volume were excluded from learning. All these results validate the importance of the volume and the modeling parameters in stock trading prediction. View Full-Text
Keywords: daily stock prediction; multilayer perceptrons; support vector machines; trading volume; model parameters daily stock prediction; multilayer perceptrons; support vector machines; trading volume; model parameters
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Dinh, T.-A.; Kwon, Y.-K. An Empirical Study on Importance of Modeling Parameters and Trading Volume-Based Features in Daily Stock Trading Using Neural Networks. Informatics 2018, 5, 36.

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