4.2.3. Category-Based Comparative Analysis

This section applies the comparative analysis to different datasets containing different categories such as macroeconomic, microeconomic, and technical indicators. Comparison is conducted under the *T*-test at the significance level of 0.05 by WEKA software. To evaluate the predictive machine learning models' performance and have robust results, the 10-fold cross-validation on a rolling basis evaluation technique is used, and each model is repeated ten times. Therefore, the average results of 100 prediction trials, including the forecasting ability of models, namely RMSE and Pearson's *r*, are shown in Tables 8 and 9. The standard deviation is represented in parenthesis.

**Table 8.** RMSE of different models on different indicators.


**Table 9.** Pearson's *r* of different models on different indicators.


According to Tables 8 and 9, technical indicators impact prediction results in OLS and SVR models. The Ensemble methods and MLP models have the best accuracy on the data, including all variables. Prediction using technical indicators or using all indicators has nearly the same accuracy. In addition, all models applied on the macroeconomic and microeconomic indicators have bad accuracy with a very low Pearson's *r* and high RMSE. Therefore, it is not recommended to be used. The order of indicators according to their impact on prediction is shown in Table 10. Models applied to all attributes, and technical indicators are compared in Table 11. In both cases, the SVR model outperforms other models. Also, MLP is considered the worst model.


**Table 10.** The order of indicators according to their impact on prediction.

**Table 11.** The order of the models in terms of accuracy.


The category-based comparative analysis showed that macroeconomic indicators (trade-weighted US dollar index, gold fixing price, DJIA index, Brent crude oil price, and WTI) are not significant predictors for short-term BTC price. Microeconomic indicators are also not significant except for the MLP model. In addition, technical indicators, namely volume, MTM, CCI, and SMA, predict the price with nearly the same accuracy as the prediction model using all indicators. Therefore, the recommendation is to use technical analysis to predict the short-term BTC price. These empirical results answer the first and second research questions. (1) What are the significant variables as short-term or longterm BTC price predictors? (2) What are the underlying economic theories of BTC price predictors? To answer the third research question (What machine learning model performs better? What are the best feature selection techniques?), empirical results showed that the SVR model in feature-based and category-based comparative analyses outperform other models. Also, in terms of data preparation, no feature selection improved the model, and VIF turned out to be the worst feature selection.
