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A Multi-Method Survey on the Use of Sentiment Analysis in Multivariate Financial Time Series Forecasting.

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Incorporating social media sentiment analysis can conditionally improve long-term financial time series forecasting. Long short-term memory (LSTM) networks generally outperform other methods in these multivariate prediction models.

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FinBERTTwitterfinancial time seriesmachine learningmultistepmultivariateregressionsentiment analysistime series forecasting

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Area of Science:

  • Computational finance
  • Data science
  • Social media analytics

Background:

  • Financial time series forecasting relies on historical data.
  • Social and environmental factors can influence financial markets.
  • Integrating alternative data sources may enhance prediction accuracy.

Purpose of the Study:

  • To investigate the impact of social media sentiment analysis on financial time series forecasting.
  • To evaluate the performance of multivariate prediction architectures incorporating sentiment data.
  • To identify optimal methods for sentiment-enhanced financial forecasting.

Main Methods:

  • Extensive experimental analysis of 22 input setups across 16 datasets.
  • Utilized 27 different algorithms for multivariate time series prediction.
  • Compared forecasting performance with and without social media sentiment analysis integration.

Main Results:

  • Specific sentiment analysis configurations conditionally improved long-term forecast predictability.
  • Long short-term memory (LSTM) architectures demonstrated a universal performance advantage.
  • The study identified effective sentiment analysis setups for financial forecasting.

Conclusions:

  • Social media sentiment analysis offers a valuable complementary data source for financial forecasting.
  • LSTM networks are highly effective for multivariate financial time series prediction incorporating sentiment data.
  • Further research can refine sentiment analysis integration for enhanced financial market prediction.