From On-chain to Macro: Assessing the Importance of Data Source Diversity in Cryptocurrency Market Forecasting
This work addresses the problem of improving forecasting accuracy for cryptocurrency markets, which is incremental as it builds on existing methods by emphasizing data diversity.
The study tackled cryptocurrency market forecasting by integrating diverse data sources, finding that data source diversity significantly enhances predictive performance across time horizons, with on-chain metrics being crucial for both short- and long-term predictions and traditional market indices and macroeconomic indicators gaining relevance for longer-term forecasts.
This study investigates the impact of data source diversity on the performance of cryptocurrency forecasting models by integrating various data categories, including technical indicators, on-chain metrics, sentiment and interest metrics, traditional market indices, and macroeconomic indicators. We introduce the Crypto100 index, representing the top 100 cryptocurrencies by market capitalization, and propose a novel feature reduction algorithm to identify the most impactful and resilient features from diverse data sources. Our comprehensive experiments demonstrate that data source diversity significantly enhances the predictive performance of forecasting models across different time horizons. Key findings include the paramount importance of on-chain metrics for both short-term and long-term predictions, the growing relevance of traditional market indices and macroeconomic indicators for longer-term forecasts, and substantial improvements in model accuracy when diverse data sources are utilized. These insights help demystify the short-term and long-term driving factors of the cryptocurrency market and lay the groundwork for developing more accurate and resilient forecasting models.