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Conference Paper
Feature Selection for Stock forecasting using Multivariate Convolution Neural Network
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- Authors
- Ji Sung Lee, Hyeon Sung Cho, Kyo Il Chung, Ji Sang Park
- Issue Date
- 2020-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1270-1272
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- 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 Keywords
- Complex factors, Convolution neural network(CNN), Financial data, Industrial sector, Key indicators, Neural network model, Stock forecasting, Stock prices, feature selection method, neural network(NN), technical indicators