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학술대회 Feature Selection for Stock forecasting using Multivariate Convolution Neural Network
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이지성, 조현성, 정교일, 박지상
International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1270-1272
20PS1100, 빅데이터 및 AI 기반의 투자 및 자산관리 지원 서비스 시스템 개발, 박지상
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.
KSP 제안 키워드
Complex factors, Convolution neural network(CNN), Feature selection(FS), Financial data, Key indicators, Stock forecasting, Stock prices, feature selection method, industrial sector, neural network model, technical indicators