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- W4285477427 abstract "This research aims to predict the stock market using the information in news titles published on influential news websites. Term Frequency Inverse Document Frequency has been used as the encoding method and compares the predicting power of several different machine learning algorithms: Two-Class Averaged Perceptron, Two-Class Bayes Point Machine, Two-Class Boosted Decision Tree, Two-Class Decision Forest, Two-Class Decision Jungle, Two-Class Locally Deep Support Vector Machine, Two-Class Logistic Regression and Two-Class Neural Network. Through this approach, 55,494 Chinese news titles and 49,725 U.S. news titles covering the Dow Jones Industrial Average (Range: 2008-08-08 to 2016-07-01) and the CSI Index price (Range: 2005-04-08 to 2021-1-25) have been investigated. After transforming the stock price into the dummy variable, the Two-Class Neural Network model is found to be the best predicting model for the Chinese market and the Two-Class Decision Jungle model is the best choice for the U.S. market." @default.
- W4285477427 created "2022-07-15" @default.
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- W4285477427 date "2021-04-01" @default.
- W4285477427 modified "2023-09-27" @default.
- W4285477427 title "Stock Market Prediction by Daily News via Natural Language Processing and Machine Learning" @default.
- W4285477427 doi "https://doi.org/10.1109/cbfd52659.2021.00044" @default.
- W4285477427 hasPublicationYear "2021" @default.
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