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Predicting standardized absolute returns using rolling-sample textual modelling.

Ka Kit Tang1, Ka Ching Li1, Mike K P So1

  • 1Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Hong Kong, Hong Kong.

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This study shows that analyzing news topics using Latent Dirichlet Allocation (LDA) improves stock market volatility prediction compared to simple moving averages. Textual data offers valuable insights for financial econometrics.

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Area of Science:

  • Financial econometrics
  • Computational linguistics
  • Data science

Background:

  • Financial market volatility is influenced by textual information.
  • Existing research explores this relationship, but advanced methods are needed.
  • Comparing public and subscription news data is crucial.

Purpose of the Study:

  • To examine the relationship between financial market volatility and textual news information.
  • To compare the performance of public and subscription news datasets.
  • To develop a method for extracting dynamic features from textual data for volatility prediction.

Main Methods:

  • Latent Dirichlet Allocation (LDA) for topic modeling of textual data.
  • Transforming topic popularity and diversity measures into predictors.
  • Utilizing a rolling regression model for out-of-sample analysis.
  • Generalized Autoregressive Conditional Heteroskedasticity (GARCH) modeling for volatility proxy.

Main Results:

  • Topic measures derived from textual data are more effective predictors of volatility than simple moving averages.
  • The proposed method captures statistical properties of textual information over time.
  • Out-of-sample analysis validates the usefulness of textual information.

Conclusions:

  • Textual information, when processed with LDA, significantly enhances stock market volatility prediction.
  • The developed method provides a valuable tool for financial econometric research.
  • This approach offers improved forecasting accuracy for market volatility.