Time series prediction using machine learning: a case of Bitcoin returns
Studies in Economics and Finance
; 39(3):458-470, 2022.
Article
in English
| ProQuest Central | ID: covidwho-1806877
ABSTRACT
Purpose>The purpose of this study is to compare five data-driven-based ML techniques to predict the time series data of Bitcoin returns, namely, alternating model tree, random forest (RF), multiple linear regression, multi-layer perceptron regression and M5 Tree algorithms.Design/methodology/approach>The data used to forecast time series data of Bitcoin returns ranges from 8 July 2010 to 30 Aug 2020. This study used several predictors to predict bitcoin returns including economic policy uncertainty, equity market volatility index, S&P returns, USD/EURO exchange rates, oil and gold prices, volatilities and returns. Five statistical indexes, namely, correlation coefficient, mean absolute error, root mean square error, relative absolute error and root relative squared error are determined. The results of these metrices are used to develop colour intensity ranking.Findings>Among the machine learning (ML) techniques used in this study, RF models has shown superior predictive ability for estimating the Bitcoin returns.Originality/value>This study is first of its kind to use and compare ML models in the prediction of Bitcoins. More studies can be carried out by using further cryptocurrencies and other ML data-driven models in future.
Business And Economics--Economic Systems And Theories, Economic History; Timeseries modelling; Machine learning; Cryptocurrency; Bitcoin; Predictive ability; Currency; Regression analysis; Investments; Prices; Economic policy; Time series; Digital currencies; Volatility; COVID-19; Uncertainty; Learning; Foreign exchange rates; Pandemics; Neural networks; Medical research; Literature reviews; Algorithms; Coronaviruses; Statistical methods; Profitability
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Type of study:
Experimental Studies
/
Prognostic study
Language:
English
Journal:
Studies in Economics and Finance
Year:
2022
Document Type:
Article
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