Document Type : Research Article
Authors
1 Department of Industrial Engineering, SR.C., Islamic Azad University, Tehran, Iran.
2 Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran.
Abstract
This paper empirically investigates the performance of a factor-based "risk momentum" strategy within the cryptocurrency market, utilizing a K-Means clustering approach to identify homogeneous risk groups. We apply a multi-stage methodology to hourly cryptocurrency data, encompassing variable extraction, cross-sectional regressions to derive risk components, and K-Means clustering to group assets based on their historical risk profiles. Market-capitalization-weighted long-short portfolios are constructed by aggregating cryptocurrencies from the lowest and highest risk deciles across all identified clusters. Empirical analysis reveals that the overall clustered risk momentum strategy yielded statistically significant negative returns during the study period. This suggests a "risk reversal" phenomenon in the aggregated portfolio rather than positive risk momentum. Furthermore, we provide a theoretical explanation for this phenomenon using a stochastic Ornstein-Uhlenbeck mean-reversion model, mathematically demonstrating how the speed of price correction outweighs momentum persistence in high-frequency cryptocurrency data.
Keywords
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