Predicting stock prices are difficult as they are affected by diverse and complex factors. Therefore, among the various indicators that affect stock prices, key indicators must be selected. Hence, we present a new feature selection method using a multivariate convolutional neural network model to select key indicators that affect stock prices. In addition, we used data of daily net buying and net selling amounts based on investor type, unlike technical indicators or financial data used in other studies. The proposed feature selection method is validated by comparing the predicted accuracy of the stock price using selected and overall indicators. Furthermore, we compare the data and verify the sector using the more efficient data by analyzing industrial sectors.
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J. Kim et. al, "Trends in Lightweight Kernel for Many core Based High-Performance Computing", Electronics and Telecommunications Trends. Vol. 32, No. 4, 2017, KOGL Type 4: Source Indication + Commercial Use Prohibition + Change Prohibition
J. Sim et.al, “the Fourth Industrial Revolution and ICT – IDX Strategy for leading the Fourth Industrial Revolution”, ETRI Insight, 2017, KOGL Type 4: Source Indication + Commercial Use Prohibition + Change Prohibition
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