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Revisiting spillovers between investor attention and cryptocurrency markets using noisy independent component analysis and transfer entropy

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  • Neto, David

Abstract

The present paper aims at revisiting the information transmission between cryptocurrency markets and investor attention in these assets. For this purpose, we use transfer entropy rather than the conventional Granger causality approach. The resort to transfer entropy is an interesting route to overcome the limitations of Granger’s concept of causality related to the linearity (and Gaussianity) assumption. In addition, a non-gaussian factor model is estimated to extract a proxy of investor attention from Google search volumes of a set of keywords. Whilst empirical studies commonly report a one-way causal effect between investor attention and price movements, our results shed light on a bidirectional spillover which can be attributed to the self-sustaining nature of price dynamics in speculative markets.

Suggested Citation

  • Neto, David, 2022. "Revisiting spillovers between investor attention and cryptocurrency markets using noisy independent component analysis and transfer entropy," The Journal of Economic Asymmetries, Elsevier, vol. 26(C).
    • Handle: RePEc:eee:joecas:v:26:y:2022:i:c:s1703494922000299
    DOI: 10.1016/j.jeca.2022.e00269
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    References listed on IDEAS

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    1. Maiti, Moinak & Vukovic, Darko B. & Frömmel, Michael, 2023. "Quantifying the asymmetric information flow between Bitcoin prices and electricity consumption," Finance Research Letters, Elsevier, vol. 57(C).

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    More about this item

    Keywords

    Cryptocurrency; Investor attention; Independent component analysis; Quasi-JADE algorithm; Transfer entropy;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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