Cryptocurrencies, particularly Bitcoin, have emerged as one of the most controversial and complex technological innovations in today's financial system. The ability to forecast the movements of Bitcoin prices with a high degree of accuracy has become an important area of research. This study aims to achieve this objective by applying four different Machine Learning (ML) algorithms simultaneously, namely, Support Vector Machines (SVM), Artificial Neural Network (ANN), Naive Bayes (NB), and Random Forest (RF). Additionally, logistic regression (LR) is used as a benchmark model for comparison purposes. The study uses nine technical input parameters that were previously identified by Armano et al. (2005), Atsalakis & Valavanis (2009), Kara et al. (2011), and Kim (2003). These parameters include closing prices, high and low prices for computing technical indicators such as simple 14-day moving average, simple 14-day weighted moving average, momentum, stochastic K%, stochastic D%, relative strength index(RSI). The study differs from previous studies in three aspects. Firstly, it applies four different ML algorithms simultaneously to compare their performances. Secondly, it uses both existing continuous datasets and discrete datasets created specifically for this study. Finally, the study aims to understand which ML algorithm offers higher forecasting performance in continuous and discrete datasets separately and comparatively. The empirical findings reveal that RF has the highest forecasting performance in the continuous dataset while NB has the lowest performance. On the other hand, ANN has the highest performance while NB has the lowest in the discrete dataset. Furthermore, using a discrete dataset improves overall forecasting performance across all models estimated. To validate these findings further, a validation dataset consisting of 100 Bitcoin series between June 2020–October 2020 was used to compare model performances with benchmark LR statistics using t-tests. Accuracy statistics were calculated for both continuous and discrete datasets. In conclusion, this study provides valuable insights into the forecasting of Bitcoin prices using ML algorithms. The use of both continuous and discrete datasets along with the application of multiple ML algorithms contributes to a better understanding of which algorithm offers higher forecasting performance.
Bitcoin is a type of money that some people use instead of regular money. Some people think it's good, while others don't like it. People try to guess what the price of Bitcoin will be in the future, and this is important research. Scientists used four different computer programs to help them guess what the price of Bitcoin would be. They looked at things like how much Bitcoin was worth in the past and how many people were buying it. One program worked better with one kind of information, while another program worked better with a different kind of information. The scientists found that using a special kind of information made all the programs work better."
Definitions
- Cryptocurrencies: digital or virtual currencies that use cryptography for security and are decentralized.
- Machine Learning (ML): a type of artificial intelligence that allows computers to learn from data without being explicitly programmed.
- Algorithms: a set of instructions given to a computer to perform specific tasks.
- Technical indicators: mathematical calculations based on an asset's price and/or volume history used to forecast future trends.
- Discrete datasets: data that can only take certain values or observations.
- Validation dataset: a dataset used to evaluate the performance of machine learning models during training.
Forecasting Bitcoin Prices Using Machine Learning Algorithms
Cryptocurrencies, particularly Bitcoin, have become one of the most controversial and complex technological innovations in today's financial system. With its volatile nature and decentralized structure, predicting the movements of Bitcoin prices is a difficult task. However, it has become an important area of research to be able to forecast these prices with a high degree of accuracy. This study aims to achieve this objective by applying four different Machine Learning (ML) algorithms simultaneously: Support Vector Machines (SVM), Artificial Neural Network (ANN), Naive Bayes (NB), and Random Forest (RF). Additionally, logistic regression (LR) is used as a benchmark model for comparison purposes.
Input Parameters
The study uses nine technical input parameters that were previously identified by Armano et al. (2005), Atsalakis & Valavanis (2009), Kara et al. (2011), and Kim (2003). These parameters include closing prices, high and low prices for computing technical indicators such as simple 14-day moving average, simple 14-day weighted moving average, momentum, stochastic K%, stochastic D%, relative strength index(RSI).
Novelty Of The Study
This study differs from previous studies in three aspects. Firstly, it applies four different ML algorithms simultaneously to compare their performances. Secondly, it uses both existing continuous datasets and discrete datasets created specifically for this study. Finally, the study aims to understand which ML algorithm offers higher forecasting performance in continuous and discrete datasets separately and comparatively.
Empirical Findings
The empirical findings reveal that RF has the highest forecasting performance in the continuous dataset while NB has the lowest performance. On the other hand, ANN has the highest performance while NB has the lowest in the discrete dataset. Furthermore, using a discrete dataset improves overall forecasting performance across all models estimated when compared with using only continuous data sets alone . To validate these findings further ,a validation dataset consisting of 100 Bitcoin series between June 2020–October 2020 was used to compare model performances with benchmark LR statistics using t-tests . Accuracy statistics were calculated for both continuous and discrete datasets .
Conclusion
In conclusion ,this study provides valuable insights into forecasting Bitcoin prices using ML algorithms . The use of both continuous and discrete datasets along with application of multiple ML algorithms contributes towards understanding which algorithm offers higher forecasting performance .
Cryptocurrencies, such as Bitcoin, are one of the most controversial and complex technological innovations in today's financial system. This study aims to forecast the movements of Bitcoin prices at a high degree of accuracy. To this aim, four different Machine Learning (ML) algorithms are applied, namely, the Support Vector Machines (SVM), the Artificial Neural Network (ANN), the Naive Bayes (NB) and the Random Forest (RF) besides the logistic regression (LR) as a benchmark model. In order to test these algorithms, besides existing continuous dataset, discrete dataset was also created and used. For the evaluations of algorithm performances, the F statistic, accuracy statistic, the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE) and the Root Absolute Error (RAE) metrics were used. The t test was used to compare the performances of the SVM, ANN, NB and RF with the performance of the LR. Empirical findings reveal that, while the RF has the highest forecasting performance in the continuous dataset, the NB has the lowest. On the other hand, while the ANN has the highest and the NB the lowest performance in the discrete dataset. Furthermore, the discrete dataset improves the overall forecasting performance in all algorithms (models) estimated.
