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How well do investor sentiment and ensemble learning predict Bitcoin prices?

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F23%3A39920845" target="_blank" >RIV/00216275:25410/23:39920845 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.sciencedirect.com/science/article/abs/pii/S0275531922002227" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0275531922002227</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.ribaf.2022.101836" target="_blank" >10.1016/j.ribaf.2022.101836</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    How well do investor sentiment and ensemble learning predict Bitcoin prices?

  • Original language description

    Investor sentiment is widely recognized as the major determinant of cryptocurrency prices. Although earlier research has revealed the influence of investor sentiment on cryptocurrency prices, it has not yet generated cohesive empirical findings on an important question: How effective is investor sentiment in predicting cryptocurrency prices? To address this gap, we propose a novel prediction model based on the Bitcoin Misery Index, using trading data for cryptocurrency rather than judgments from individuals who are not Bitcoin investors, as well as bagged support vector regression (BSVR), to forecast Bitcoin prices. The empirical analysis is performed for the period between March 2018 and May 2022. The results of this study suggest that the addition of the sentiment index enhances the predictive performance of BSVR signifi-cantly. Moreover, the proposed prediction system, enhanced with an automatic feature selection component, outperforms state-of-the-art methods for predicting cryptocurrency for the next 30 days.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    50206 - Finance

Result continuities

  • Project

    <a href="/en/project/GA22-22586S" target="_blank" >GA22-22586S: Aspect-based sentiment analysis of financial texts for predicting corporate financial performance</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Research in International Business and Finance

  • ISSN

    0275-5319

  • e-ISSN

    1878-3384

  • Volume of the periodical

    64

  • Issue of the periodical within the volume

    January

  • Country of publishing house

    NL - THE KINGDOM OF THE NETHERLANDS

  • Number of pages

    16

  • Pages from-to

    101836

  • UT code for WoS article

    000919065300001

  • EID of the result in the Scopus database

    2-s2.0-85145720521