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Stock trend prediction using sentiment analysis.

Qianyi Xiao1, Baha Ihnaini1

  • 1Department of Computer Science, Wenzhou Kean University, Wenzhou, Zhejiang, China.

Peerj. Computer Science
|August 7, 2023
PubMed
Summary
This summary is machine-generated.

Analyzing stock market trends using text mining and sentiment analysis reveals that dividing data by trading hours, not natural days, improves stock prediction accuracy. This method better captures investor sentiment for more informed investment decisions.

Keywords:
FinBERTMachine learningSentiment analysisStock predictionTweets

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Area of Science:

  • Computational finance
  • Natural Language Processing (NLP)
  • Financial market analysis

Background:

  • The increasing volume of online data offers valuable insights for investors.
  • Text mining and sentiment analysis can gauge investor confidence in stocks.
  • Previous studies often aggregate daily sentiment, potentially limiting predictive accuracy.

Purpose of the Study:

  • To investigate the impact of different time divisions on stock trend prediction.
  • To compare the predictive power of sentiment analysis using natural day versus trading hour data divisions.
  • To analyze how tweets and news sentiment within specific periods influence next-day stock movements.

Main Methods:

  • Collected 260,000 tweets and 6,000 news articles for Service (Amazon, Netflix) and Technology (Apple, Microsoft) stocks.
  • Implemented two distinct time division strategies: natural day (0:00t–0:00t+1) and trading hours (9:30t–9:30t+1).
  • Applied text mining and sentiment analysis techniques to assess investor confidence.

Main Results:

  • Sentiment analysis based on the trading hour division (9:30t–9:30t+1) demonstrated superior performance.
  • The trading hour division significantly outperformed the natural day division (0:00t–0:00t+1) in predicting stock trends.
  • Specific time-based sentiment aggregation is more effective for stock market prediction.

Conclusions:

  • Dividing online data by stock market trading hours enhances the accuracy of next-day stock trend prediction.
  • The timing of data collection and analysis is crucial for effective sentiment-based financial forecasting.
  • This research provides a refined methodology for leveraging financial news and social media data in investment strategies.