*2.5. Related Work and Research Gap*

Thus far, empirical studies do not demonstrate a clear advantage for the emerging techniques of using machine learning algorithms to predict the BTC price. Research in this area is insufficient [34,35]. Therefore, this study will help to show the significance of machine learning methods in BTC price prediction problems. Also, some research shows machine learning outperforms statistical analysis, and some still believe in the superiority of conventional statistical analysis. Table 1 presents some related work on the BTC price prediction problem. The current research differs from previous studies in terms of completeness and comprehensiveness, and the comparative analysis in the current study has not been conducted before. In addition, a variety of indicators, including macroeconomic indicators, microstructure indicators, blockchain information, and technical indicators, have been used to analyze the significant variables as BTC price predictors.


**Table 1.** Overview of research published on BTC price prediction.


**Table 1.** *Cont.*

In the existing literature, there is no comprehensive work in which almost all categories of indicators are investigated. Most of the works regarding BTC price prediction are empirical analyses. However, the current study first looks at the BTC price prediction problem from the perspective of economic theories, including demand and supply theory, microstructure theory, and Cost-based pricing theory. It then identifies the associated variables affecting the BTC price. After that, we empirically prove the predictability power of the attributes through emerging machine learning models and traditional methods.
