Can We Apply Traditional Forecasting Models to Predicting Bitcoin?

Matthew Bobea () and Wesley Szuway Shu ()
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Matthew Bobea: National Chengchi University
Wesley Szuway Shu: Xi’an Jiaotong-Liverpool University

Chapter Chapter 9 in City, Society, and Digital Transformation, 2022, pp 97-112 from Springer

Abstract: Abstract As cryptocurrency becomes more accepted as a valid investment tool within financial markets, and as more financial instruments move on to a decentralized finance platform, demand for more advance methods of modeling cryptocurrency have increased. Having a reliable model will improve investor’s confidence in an otherwise high-risk and highly volatized market. Many researchers attempt to create new models and find new variables to forecasting cryptocurrency, however, developing a model that is consistent, accurate, and nondeterministic is still challenging. Nevertheless, traditional models have been proven over time when used for financial market analysis. In this paper, logistic regression and ARIMA will be the key statistical models investigated for use in forecasting Bitcoin. Each model will be tweaked to optimize performance based on current standing research. Furthermore, each model’s result will be scored and compared based on their ability to predict Bitcoin’s performance.

Keywords: ARIMA; Logistic regression; Cryptocurrency; Bitcoin; Financial market prediction; Machine learning (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-031-15644-1_9

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DOI: 10.1007/978-3-031-15644-1_9

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