From On-chain to Macro: Assessing the Importance of Data Source Diversity in Cryptocurrency Market Forecasting
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916844437241856 |
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| author | Demosthenous, Giorgos Georgiou, Chryssis Polydorou, Eliada |
| author_facet | Demosthenous, Giorgos Georgiou, Chryssis Polydorou, Eliada |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21246 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From On-chain to Macro: Assessing the Importance of Data Source Diversity in Cryptocurrency Market Forecasting Demosthenous, Giorgos Georgiou, Chryssis Polydorou, Eliada Portfolio Management Artificial Intelligence Emerging Technologies Machine Learning Statistical Finance 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. |
| title | From On-chain to Macro: Assessing the Importance of Data Source Diversity in Cryptocurrency Market Forecasting |
| topic | Portfolio Management Artificial Intelligence Emerging Technologies Machine Learning Statistical Finance |
| url | https://arxiv.org/abs/2506.21246 |