Unveiling Key Drivers of Bitcoin Returns
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Unveiling Key Drivers of Bitcoin Returns
A Machine Learning Approach with Dynamic Variable Selection
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Description
The increasing trading volume and regulatory scrutiny of cryptocurrencies, especially Bitcoin, have solidified their pivotal role in today’s financial markets. This study examines various factors influencing future cryptocurrency returns. To overcome multi-collinearity problem and enhance prediction, we employ powershap for variable selection, which is a based on model interpretation (Verhaeghe et al, 2022). Our investigations focus on various periods before and after the Covid-19 pandemic, revealing the importance of technical indicators, oil prices, and exchange rates. To showcase the practical application of our method in predicting BTC returns, we present a trading strategy that demonstrates its potential for generating higher returns and Sharpe ratios.
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Meet the instructors !
I am an associate professor in the Department of Information Management and Finance at National Yang Ming Chiao Tung University, Taiwan. My research focuses on Monte Carlo methods and financial data analytics.