Accurately Forecasting Stock Prices using LSTM and GRU Neural Networks
A Deep Learning approach for forecasting stock price time-series data in groups
(Sprache: Englisch)
Stocks or shares are securities that confirm the participation or ownership of a person or entity in a company. Stocks are an attractive investment option because they can generate large profits compared to other businesses, however, the risk can also...
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Stocks or shares are securities that confirm the participation or ownership of a person or entity in a company. Stocks are an attractive investment option because they can generate large profits compared to other businesses, however, the risk can also result in large losses in a short time. Thus, minimizing the risk of loss in stock buying and selling transactions is very crucial and important, and it requires careful attention to stock price movements. Technical factors are one of the methods that are used in learning the prediction of stock price movements through past historical data patterns on the stock market. Therefore, forecasting models using technical factors must be careful, thorough, and accurate, to reduce risk appropriately. This book presents the LSTM and GRU Neural Networks to build stock price forecasting models in groups using technical factors. The investigation uses seven years of benchmark time-series data on daily stock price movements with the same featuresas several previous related works to show differences in results. Time-series data on stock prices are grouped to follow the general pattern of stock price movements in the stock exchange market.
Autoren-Porträt von Armin Lawi, Eka Kurnia
Lawi, ArminArmin Lawi is an Associate Professor of Computer Science at Hasanuddin University, Indonesia where he obtained his Bachelor of Science (B.Sc.) in Mathematics. His Master of Engineering (M.Eng.) and Doctor of Engineering (Dr.Eng.) degrees in Computer Science were pursued at Kyushu University and Kyushu Institute of Technology, respectively.
Bibliographische Angaben
- Autoren: Armin Lawi , Eka Kurnia
- 2021, 52 Seiten, Maße: 22 cm, Kartoniert (TB), Englisch
- Verlag: LAP Lambert Academic Publishing
- ISBN-10: 620419092X
- ISBN-13: 9786204190921
Sprache:
Englisch
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