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From Fear to Greed: Analyzing Sentiment Indicators in Bitcoin Price Prediction

In: Mathematical Research for Blockchain Economy

Author

Listed:
  • Stefano Bistarelli

    (Università degli Studi di Perugia)

  • Francesco Santini

    (Università degli Studi di Perugia)

  • Luca Maria Tutino

    (Università degli Studi di Perugia
    Università degli Studi di Firenze)

Abstract

The attention of online investors significantly influences Bitcoin’s price, often resulting in high volatility that is challenging for agents to handle. Although price surges offer opportunities, sharp drops pose risks for portfolio management. This study examines the relationships between Bitcoin price and main sentiment indicators, Google Trends Index, Fear and Greed Index, Wikipedia views, LunarCrush Galaxy Score, IntoTheBlock, and Santiment indices, using classical correlation measures (such as Pearson and Spearman), econometric frameworks such as AutoRegressive Distributed Lag models, and Wavelet Coherence Analysis. We aim to develop tools for assessing the efficiency of sentiment indices in predicting future Bitcoin prices, focusing on their cost-benefit trade-offs when integrated into forecasting models and trading strategies.

Suggested Citation

  • Stefano Bistarelli & Francesco Santini & Luca Maria Tutino, 2026. "From Fear to Greed: Analyzing Sentiment Indicators in Bitcoin Price Prediction," Lecture Notes in Operations Research, in: Stefanos Leonardos & Amir K. Goharshady & William Knottenbelt & Panos Pardalos (ed.), Mathematical Research for Blockchain Economy, pages 205-223, Springer.
    • Handle: RePEc:spr:lnopch:978-3-032-13377-9_10
    DOI: 10.1007/978-3-032-13377-9_10
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