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Financial Returns, Sentiment and Market Volatility: a Dynamic Assessment

Author

Listed:
  • G.M. Gallo
  • C.Ongari
  • S. Borgioli

Abstract

In 1936, John Maynard Keynes proposed that emotions and instincts are pivotal in decisionmaking, particularly for investors. Both positive and negative moods can influence judgments and decisions, extending to economic and financial choices. Intuitions, emotional states, and biases significantly shape how people think and act. Measuring mood or sentiment is challenging, but surveys and data collection methods, such as confidence indices and consensus forecasts, offer some solutions. Recently, the availability of web data, including search engine queries and social media activity, has provided high-frequency sentiment measures. For example, the Italian National Statistical Institute's Social Mood on Economy Index (SMEI) uses Twitter data to assess economic sentiment in Italy. The relationship between SMEI and financial market activity, specifically the FTSE MIB index and its volatility, is examined using a trivariate Vector Autoregressive model, taking into account the impact of the COVID-19 pandemic.

Suggested Citation

  • G.M. Gallo & C.Ongari & S. Borgioli, 2024. "Financial Returns, Sentiment and Market Volatility: a Dynamic Assessment," Working Paper CRENoS 202415, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    • Handle: RePEc:cns:cnscwp:202415
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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G4 - Financial Economics - - Behavioral Finance

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