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http://dx.doi.org/10.30693/SMJ.2020.9.4.26

Financial Market Prediction and Improving the Performance Based on Large-scale Exogenous Variables and Deep Neural Networks  

Cheon, Sung Gil (인하대학교 컴퓨터공학과 대학원)
Lee, Ju Hong (인하대학교 컴퓨터공학과)
Choi, Bum Ghi (인하대학교 컴퓨터공학과)
Song, Jae Won (밸류파인더스)
Publication Information
Smart Media Journal / v.9, no.4, 2020 , pp. 26-35 More about this Journal
Abstract
Attempts to predict future stock prices have been studied steadily since the past. However, unlike general time-series data, financial time-series data has various obstacles to making predictions such as non-stationarity, long-term dependence, and non-linearity. In addition, variables of a wide range of data have limitations in the selection by humans, and the model should be able to automatically extract variables well. In this paper, we propose a 'sliding time step normalization' method that can normalize non-stationary data and LSTM autoencoder to compress variables from all variables. and 'moving transfer learning', which divides periods and performs transfer learning. In addition, the experiment shows that the performance is superior when using as many variables as possible through the neural network rather than using only 100 major financial variables and by using 'sliding time step normalization' to normalize the non-stationarity of data in all sections, it is shown to be effective in improving performance. 'moving transfer learning' shows that it is effective in improving the performance in long test intervals by evaluating the performance of the model and performing transfer learning in the test interval for each step.
Keywords
Market prediction; Deep learning; AutoEncoder; Trainsfer learning; Time series normalization;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
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