4.1.3. OLS Regression for BTC Price Prediction

Table A3 in the Appendix A.1 shows the results of nine regression models built to avoid multicollinearity. The variables in quotes are the variables with a high correlation. They are added to the rest of the variables to build a new regression model. The response variable in each model is the BTC price. The value in parentheses represents the results of the *t*-test for the null hypothesis-rejecting variables, based on a *p*-value of 0.05. The *R*<sup>2</sup> from regression models is relatively high, suggesting that, for example, approximately 73% of the variation in BTC prices in model "9" is determined by the variables in the model. Due to the t-statistics and p-value, all models are statistically significant. By looking at the coefficients, which are not tiny, it is evident that all variables are economically significant for the models.

The regression analysis showed that the significant macroeconomic indicators in all models for monthly BTC price are market capitalization, Nasdaq Composite, Dow Jones Industrial Average, and S&P500. Therefore, macroeconomic indicators have longterm predictive power on BTC prices as expected a priori and the t-statistic indicates the significance of the results. Also, blockchain information indicators, including the block size, cost per transaction, mining difficulty, hash rate, transaction fees, and estimated transaction value, verify that the supply and demand theory is the underlying theory of predictors. Therefore, blockchain information indicators have a long-term predictive power on BTC prices as expected a priori. The t-statistic indicates that it is highly statistically significant that blockchain information indicators influence the price confirming that the cost-based pricing theory is underlying the predictors. 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?
