Use GBDT to Predict the Stock Market

Jin Shan Yang, Chen Yue Zhao, Hao Tong Yu, He Yang Chen

Research output: Contribution to journalConference articlepeer-review

39 Citations (Scopus)

Abstract

Prediction of stock market is important and popular for investors, to decrease the loss and increase the profit, how to prevent the risk becomes a special field and there is amount of solutions of the problem. Previous researches were focus on the factors that may influence the emotions of investors, the researchers finished researches based on the social media, the period of stock market and applied different models to extract the feature of stock. We first used natural language processing to deal with the text, including TF-IDF, Word2Vec, CountVectorizer and Doc2Vec. Then, we reduce dimension by PCA, finally, we used machine learning to deal with data, including Adaboost, XGboost and GBDT, decision tree, logistics regression. After voting process, we find GBDT is the most accurate algorithm to predict the stock. Comparing with previous work, we focus on the emotion and select information from news, which is more accurate than other researches.

Original languageEnglish
Pages (from-to)161-171
Number of pages11
JournalProcedia Computer Science
Volume174
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event8th International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2019 - Jinan, China
Duration: 25 Oct 201927 Oct 2019

Keywords

  • GBDT
  • Natural language processing
  • Stock market
  • XGboost

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