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http://dx.doi.org/10.3745/KTSDE.2019.8.3.123

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

Kim, HyunJin (단국대학교 전자전기공학부)
Jung, Yeon Sung (단국대학교 경영학부)
Publication Information
KIPS Transactions on Software and Data Engineering / v.8, no.3, 2019 , pp. 123-128 More about this Journal
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%.
Keywords
Artificial Intelligence; Recurrent Convolution Neural Network; Stock Price Prediction; Weighted Loss Function;
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