arXiv 3 Sep 2019 · Finance — Statistical Finance · 7 citations (OpenAlex)
arXiv:1909.01268 · PDF · DOI · OpenAlex · Extracted main text
The uncertainties in future Bitcoin price make it difficult to accurately predict the price of Bitcoin. Accurately predicting the price for Bitcoin is therefore important for decision-making process of investors and market players in the cryptocurrency market. Using historical data from 01/01/2012 to 16/08/2019, machine learning techniques (Generalized linear model via penalized maximum likelihood, random forest, support vector regression with linear kernel, and stacking ensemble) were used to forecast the price of Bitcoin. The prediction models employed key and high dimensional technical indicators as the predictors. The performance of these techniques were evaluated using mean absolute percentage error (MAPE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R-squared). The performance metrics revealed that the stacking ensemble model with two base learner (random forest and generalized linear model via penalized maximum likelihood) and support vector regression with linear kernel as meta-learner was the optimal model for forecasting Bitcoin price. The MAPE, RMSE, MAE, and R-squared values for the stacking ensemble model were 0.0191%, 15.5331 USD, 124.5508 USD, and 0.9967 respectively. These values show a high degree of reliability in predicting the price of Bitcoin using the stacking ensemble model. Accurately predicting the future price of Bitcoin will yield significant returns for investors and market players in the cryptocurrency market.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | S. A. Gyamerah, P. Ngare, D. Ikpe (2019) On stock market movement prediction via stacking ensemble learning method self | 0.644 | 2 | 2 | 100% |
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| 3 | CoinMarketCap, Top 100 cryptocurrencies by market capitalization, $h… (2019) Top 100 cryptocurrencies by market capitalization | 0.405 | 1 | 1 | 100% |
| 4 | L. Breiman, Classification and regression trees, Routledge (2017) Classification and regression trees | 0.405 | 1 | 1 | 100% |
| 5 | H. Drucker, C. J. Burges, L. Kaufman, A. J. Smola, V. Vapnik Support vector regression machines | 0.405 | 1 | 1 | 100% |
| 6 | J. Friedman, T. Hastie, N. Simon, R. Tibshirani, Lasso and elastic-n… (2016) Lasso and elastic-net regularized generalized linear models. r-package version 2.0-5. 2016 | 0.405 | 1 | 1 | 100% |
| 7 | A. Greaves, B. Au (2015) Using the bitcoin transaction graph to predict the price of bitcoin | 0.405 | 1 | 1 | 100% |
| 8 | H. Jang, J. Lee (2017) An empirical study on modeling and prediction of bitcoin prices with bayesian neural networks based on blockchain information | 0.405 | 1 | 1 | 100% |
| 9 | M. Kuhn, Misc functions for training and plotting classification and… (2017) Misc functions for training and plotting classification and regression models | 0.405 | 1 | 1 | 100% |
| 10 | M. B. Kursa, W. R. Rudnicki, et al (2010) Feature selection with the boruta package | 0.405 | 1 | 1 | 100% |
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