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Can We Apply Traditional Forecasting Models to Predicting Bitcoin?

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City, Society, and Digital Transformation (INFORMS-CSS 2022)

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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.

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Abbreviations

ADF:

Augmented Dickey Fuller Test

AIC:

Akaike’s Information Criteria score

ANN:

Artificial Neural Networks

ARIMA:

AutoRegressive Integrated Moving Average

BIC:

Bayesian Information Criteria

BTC:

Bitcoin

KNN:

K-Nearest Neighbor

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Acknowledgements

I would like to acknowledge my advisor, Professor Szu-way Shu, whom helped guide me through the strategic aspects of this research. Additionally, I would like to acknowledge the contributions of Di He, Ph.D. and Songquan Pang for their support in this paper.

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Correspondence to Wesley Szuway Shu .

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Bobea, M., Shu, W.S. (2022). Can We Apply Traditional Forecasting Models to Predicting Bitcoin?. In: Qiu, R., Chan, W.K.V., Chen, W., Badr, Y., Zhang, C. (eds) City, Society, and Digital Transformation. INFORMS-CSS 2022. Lecture Notes in Operations Research. Springer, Cham. https://doi.org/10.1007/978-3-031-15644-1_9

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