4.3.3. Category-Based Comparative Analysis

OLS, Ensemble methods, SVR, and MLP are applied to economic and technical indicators. The aim is to see which indicators can be selected as better predictive indicators. Also, different models are compared on the same data to find a more accurate model. 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 16 and 17.


**Table 16.** RMSE of different models on different indicators.

**Table 17.** Pearson's *r* of different models on different indicators.


According to Tables 16 and 17, all models applied to all indicators have the best accuracy. Therefore, it is recommended that the combination of technical, microeconomics, macroeconomic, and Blockchain information indicators work better for price prediction than each indicator category alone. Moreover, technical indicators are also considered good predictors. However, prediction slightly improves by combining with other variables. Blockchain information and macroeconomic indicators are considered bad predictive indicators due to the very low Pearson's *r* and high RMSE. The order of indicators according to their impact on prediction is shown in Table 18. Models applied on all indicators and technical indicators are compared in Table 19. In both cases, the SVR model outperforms other models. Also, MLP is considered the worst model.


**Table 18.** The order of indicators according to their impact on prediction.

**Table 19.** The order of the models in terms of accuracy.


The results of 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. Also, the Blockchain information indicators, including hash rate, mining difficulty, number of transactions per block, and block time, are not significant predictors for short-term BTC price. Also, microeconomic indicators, including trades per minute, bid/ask sum, bid–ask spread, and buy/sell signals, are not significant for the BTC price prediction except for the MLP model. Since the technical indicators have nearly the same results as all indicators, the recommendation is to use the 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 long-term BTC price predictors? (2) What are the underlying economic theories of BTC price predictors? To answer the third research questions (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 the other models. In terms of data preparation, no feature selection improved the model, and PCA dimension reduction turned out to be the worst feature selection.
