Machine Learning-Based Approach for Predicting the Altcoins Price Direction Change from a High-Frequency Data of Seven Years Based on Socio-Economic Factors, Bitcoin Prices, Twitter and News Sentiments

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AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-10-05T16:33:18Z
Fecha de publicación2024-01-01
ResumenAltcoins are alternative types of coins under cryptocurrency, apart from traditional Bitcoins, for which predicting the price movement presents a multifaceted challenge deeply rooted in the volatile nature of the cryptocurrency market. This study compares and analyzes different Machine Learning (ML) and Deep Learning (DL) models for price movement prediction through diverse data sources like Bitcoin prices, social media sentiments, and news sentiments, apart from different socio-economic factors specific to USA geography due to its maturity on use of Altcoins, with temporal scope spanning from 2016 to 2022 collating over 77 M tweets and news items. Ethereum, Binance, XRP, Cardano, Monero, Tron, Stellar, and Litecoin, were considered for experimentation across widely used algorithms like Gradient Boosting, Naive Bayes, Decision Trees, Neural Networks, and the like, with different day-length lags ranging up to 4 days. Highly relevant features were selected using Random Forest selection method and highly correlated features have been removed before the modeling. Accuracy for price movement prediction models varied from 71.03\% for Ethereum to 66.14\% for Stellar, which were better by 15-20\% as compared to percentage benchmarking done by literature to be ranging around 50 s and 60 s. For the model validation, sensitivity analysis involving day-wise lag analysis, and different data splits (based on size and months) were considered, which was stable for the high performing models. Further, an interesting result was observed during the study. In order of priority, Bitcoin prices, social media sentiments, and news sentiments significantly impact altcoin price movement. This implies that by studying the Bitcoin price movement and market sentiments, investors can make wise decisions towards altcoin investments. This study holds significance for researchers and practitioners to understand the impact in the trading market of cryptocurrency and help an investor diversify their portfolio. The findings will be helpful for Algo Trading Platforms, Financial Advisors, Trading Experts, Industry Experts, Researchers, and Scholars.es_ES
Doihttps://doi.org/10.1007/s10614-023-10538-5es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/5108
Idiomaenes_ES
EditorialSPRINGERes_ES
RelaciónComputational Economicses_ES
URL relacionadohttps://doi.org/10.1007/s10614-023-10538-5es_ES
DerechosAcceso restringido / Suscripción (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_16eces_ES
FuenteComputational Economics
Palabra claveHigh-frequencyes_ES
Palabra claveAltcoinses_ES
Palabra claveSocio-economic factorses_ES
Palabra claveNews sentimentses_ES
Palabra claveMachine learninges_ES
Palabra claveTwitter sentimentses_ES
Palabra claveLages_ES
TítuloMachine Learning-Based Approach for Predicting the Altcoins Price Direction Change from a High-Frequency Data of Seven Years Based on Socio-Economic Factors, Bitcoin Prices, Twitter and News Sentimentses_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
AutorGupta, Anamika
AutorPandey, Gaurav
AutorGupta, Rajan
AutorDas, Smaran
AutorPrakash, Ajmera
AutorGarg, Kartik
AutorSarkar, Shreyan
AutorGupta, Anamikaes_ES
AutorPandey, Gauraves_ES
AutorGupta, Rajanes_ES
AutorDas, Smaranes_ES
AutorPrakash, Ajmeraes_ES
AutorGarg, Kartikes_ES
AutorSarkar, Shreyanes_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
Número5es_ES
Rango de páginas2981-3026es_ES
URL relacionadahttps://doi.org/10.1007/s10614-023-10538-5
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen64es_ES

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