Ibrahim, A., Kashef, R. and Corrigan, L. 2021. Predicting market movement direction for bitcoin: A comparison of time series modeling methods. Computers and Electrical Engineering. 89 106905. https://doi.org/10.1016/j.compeleceng.2020.106905
| Title | Predicting market movement direction for bitcoin: A comparison of time series modeling methods |
|---|---|
| Type | Journal article |
| Authors | Ibrahim, A., Kashef, R. and Corrigan, L. |
| Abstract | Many traders participate in activities known as "day-trading", trading Bitcoin against the dollar bill as the United States Dollar (USD) on very short timeframes to squeeze out profits from small market fluctuations. This paper aims to help traders decide how to best act by creating a model that can predict price movement’s direction for the next 5-min time frame. Several machine- learning models have been tested for this Up/Down binary-classification problem. In this paper, we provide a comparison of the state-of-art strategies in predicting the movement direction for bitcoin, including Random Guessing and a Momentum-Based Strategy. The tested models include Autoregressive Integrated Moving Average (ARIMA), Prophet (by Facebook), Random Forest, Random Forest Lagged-Auto-Regression, and Multi-Layer Perceptron (MLP) Neural Net- works. The MLP deep neural network has achieved the highest accuracy of 54% compared to other time-series prediction models. Also, in this paper, various data transformation and feature engineering have been applied in the comparison. |
| Keywords | Day-trading+Bitcoin+Price movement's direction+Momentum-Based Strategy+Autoregressive Integrated Moving Average+Random Forest+Lagged-Auto-Regression+Multi-Layer Perceptron Neural Networks+Feature engineering |
| Article number | 106905 |
| Journal | Computers and Electrical Engineering |
| Journal citation | 89 |
| ISSN | 0045-7906 |
| Year | 2021 |
| Publisher | Elsevier |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.compeleceng.2020.106905 |
| Web address (URL) | https://www.sciencedirect.com/science/article/pii/S0045790620307576 |
| Publication dates | |
| Published in print | 19 Nov 2020 |
| Published in print | Jan 2021 |