**5. Conclusions**

Today, international finance is a multi-trillion-dollar sector that needs a secure and stable mechanism that cryptocurrencies are currently inching. Cryptocurrencies were developed under Blockchain technology. In contrast with the traditional central authority systems wherein the sole control lies under one organization, Blockchain technology has a diversified approach. This paper applied several machine learning models to the BTC price prediction model on different data sets to verify the theoretical analysis and answer the research questions. A multilinear regression model to monthly BTC prices showed that macroeconomic and Blockchain information indicators are significant longterm predictors. That verifies that supply and demand and cost-based pricing theory are underlying BTC price predictors. These empirical results answer the first and second research questions. (1) What are the significant variables as short-term or long-term BTC price predictors? (2) What are the underlying economic theories of BTC price predictors? In addition, the empirical results showed that SVR is the best machine learning model, and

no feature selection technique is proven to be the best, which answers the third research questions (Are machine learning algorithms superior to traditional methods for BTC price prediction? What machine learning model performs better? What are the best feature selection techniques?).

The conclusions are relevant to central bankers, investors, asset managers, etc., who are generally interested in information about which indicators provide reliable, accurate forecasts of BTC price. The study can be used to set asset pricing and improve investment decision-making. Therefore, it provides a significant opportunity to contribute to international finance since the results have significant implications for the future decisions of asset managers. In time series prediction, the correlation between independent variables and dependent variables differs from time to time. Consequently, reestimating prediction models is not unlikely. This study has used many data categories composing macroeconomic, microstructure, Blockchain information, and technical indicators to make a wide-ranging work.

In this study, attributes are selected based on economic theories. Macroeconomic indicators are chosen based on the supply and demand theory. Microstructure theory is the underlying theory of microeconomic indicators. Also, Blockchain information indicators are selected according to the cost-based pricing theory. Previous studies are mostly empirical research in which the focus is on the prediction methods. After describing the price movement from the perspective of economic theories, the empirical results confirmed the theoretical analysis. This study compared methodologies to predict short-term and long-term BTC prices. The conclusion is also helpful for machine learning developers to understand the configuration of machine learning prediction models and use it as benchmarks. According to the literature review, the authors still doubt whether machine learning can beat the traditional methods for BTC price prediction. Therefore, this study is evidence of the superiority of machine learning.

This research has some suggestions for future work, which are as follows. In this research, only a few critical feature selection methods have been applied to data sets. Many other attribute selection techniques, including ranker search, Tabu search, and many more, can be examined to improve the model. Other research can compare trending models, such as recurrent neural networks (RNN) to SVR. According to this research, a correct prediction of BTC prices can be profitable; therefore, it can diversify a portfolio. Further studies can be conducted to examine the portfolio return by adding BTC to a portfolio to determine the right amount of BTC to keep. Future research can predict other cryptocurrencies, including Ethereum and Ripple. In addition, some other indicators, such as "news," can be investigated in other studies.

**Author Contributions:** Conceptualization, S.E. and Y.Z.; methodology, S.E., A.R., A.A. and T.L. software, Y.Z. and A.A.S.; validation, A.R., A.A. and Y.Z.; formal analysis, S.E. and A.A.S.; investigation, A.R., T.L. and A.A.; resources, Y.Z. and T.L.; data curation, A.R. and A.A.S.; writing—original draft preparation, S.E., Y.Z. and T.L.; writing—review and editing, A.A., A.R. and T.L.; visualization, Y.Z., A.R. and A.A.; supervision, Y.Z. and T.L.; project administration, S.E. and A.A.S.; funding acquisition, Y.Z., A.R. and T.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Data Availability Statement:** The datasets used and analyzed during this study are available from the corresponding author on reasonable request.

**Conflicts of Interest:** The authors declare no conflict of interest.
