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Conference Paper Forecasting Daily Stock Trends Using Random Forest Optimization
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Authors
Ji Sang Park, Hyeon Sung Cho, Ji Sung Lee, Kyo Il Chung, Jeong Min Kim, Dong Jin Kim
Issue Date
2019-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2019, pp.1152-1155
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC46691.2019.8939729
Abstract
This paper introduces a new approach to forecast daily stock trends using the random forest technique. This study intends to include as many features as possible to hopefully describe various aspects of stock market trends. A number of features are selected for forecasting the trends of stock prices. The new algorithm adjusts optimal learning parameters during the data training process. The usefulness of the proposed algorithm is demonstrated by processing two stock datasets while analyzing its forecasting accuracy. Additional several technical issues for future implementations and analysis are suggested.
KSP Keywords
Learning parameters, Market trends, New approach, Optimal learning, Random forest, Stock Market, Stock prices, forecasting accuracy, new algorithm, technical issues, training process