Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?

Basher Syed Abul and Sadorsky Perry

MPRA Paper from University Library of Munich, Germany

Abstract: Bitcoin has grown in popularity and has now attracted the attention of individual and institutional investors. Accurate Bitcoin price direction forecasts are important for determining the trend in Bitcoin prices and asset allocation. This paper addresses several unanswered questions. How important are business cycle variables like interest rates, inflation, and market volatility for forecasting Bitcoin prices? Does the importance of these variables change across time? Are the most important macroeconomic variables for forecasting Bitcoin prices the same as those for gold prices? To answer these questions, we utilize tree-based machine learning classifiers, along with traditional logit econometric models. The analysis reveals several important findings. First, random forests predict Bitcoin and gold price directions with a higher degree of accuracy than logit models. Prediction accuracy for bagging and random forests is between 75% and 80% for a five-day prediction. For 10-day to 20-day forecasts bagging and random forests record accuracies greater than 85%. Second, technical indicators are the most important features for predicting Bitcoin and gold price direction, suggesting some degree of market inefficiency. Third, oil price volatility is important for predicting Bitcoin and gold prices indicating that Bitcoin is a substitute for gold in diversifying this type of volatility. By comparison, gold prices are more influenced by inflation than Bitcoin prices, indicating that gold can be used as a hedge or diversification asset against inflation.

Keywords: forecasting; machine learning; random forests; Bitcoin; gold; inflation (search for similar items in EconPapers)
JEL-codes: C58 E44 G17 (search for similar items in EconPapers)
Date: 2022-06-06
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for, nep-mac and nep-pay
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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