A Stock Price Prediction Based on Recurrent Convolution Neural Network with Weighted Loss Function


KIPS Transactions on Software and Data Engineering, Vol. 8, No. 3, pp. 123-128, Mar. 2019
https://doi.org/10.3745/KTSDE.2019.8.3.123,   PDF Download:
Keywords: Artificial intelligence, Recurrent Convolution Neural Network, Stock Price Prediction, Weighted Loss Function
Abstract

This paper proposes the stock price prediction based on the artificial intelligence, where the model with recurrent convolution neural network (RCNN) layers is adopted. In the motivation of this prediction, long short-term memory model (LSTM)-based neural network can make the output of the time series prediction. On the other hand, the convolution neural network provides the data filtering, averaging, and augmentation. By combining the advantages mentioned above, the proposed technique predicts the estimated stock price of next day. In addition, in order to emphasize the recent time series, a custom weighted loss function is adopted. Moreover, stock data related to the stock price index are adopted to consider the market trends. In the experiments, the proposed stock price prediction reduces the test error by 3.19%, which is over other techniques by about 19%.


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Cite this article
[IEEE Style]
H. Kim and Y. S. Jung, "A Stock Price Prediction Based on Recurrent Convolution Neural Network with Weighted Loss Function," KIPS Transactions on Software and Data Engineering, vol. 8, no. 3, pp. 123-128, 2019. DOI: https://doi.org/10.3745/KTSDE.2019.8.3.123.

[ACM Style]
HyunJin Kim and Yeon Sung Jung. 2019. A Stock Price Prediction Based on Recurrent Convolution Neural Network with Weighted Loss Function. KIPS Transactions on Software and Data Engineering, 8, 3, (2019), 123-128. DOI: https://doi.org/10.3745/KTSDE.2019.8.3.123.