计量经济学
均方误差
波动性(金融)
系列(地层学)
平均绝对百分比误差
索引(排版)
统计
经济
时间序列
随机森林
回归
已实现方差
数学
计算机科学
机器学习
万维网
生物
古生物学
出处
期刊:Studies in Economics and Finance
[Emerald (MCB UP)]
日期:2021-11-01
卷期号:39 (3): 458-470
被引量:11
标识
DOI:10.1108/sef-06-2021-0217
摘要
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.
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