Sector Rotation Strategies in Cryptocurrency — I

Sector rotation strategies have proven effective in maximizing returns in traditional equity markets. As cryptocurrencies gain prominence, investors and researchers are exploring the viability of applying similar strategies to this emerging asset class. In this entry, I will delve into the concept of sector rotation in equities, examine studies conducted by renowned quant groups, explore the extent of research done for cryptocurrencies, and highlight key differences between these two asset classes. My motivation behind this exercise is to identify trends/narratives in cryptos and use multiple such tools to generate trade ideas.

Sector rotation strategies in equities aim to identify sectors expected to outperform or underperform the broader market over a specific time period. Renowned quant groups have conducted extensive research in this field, providing valuable insights into the effectiveness of such strategies.

Studies such as Fama and French (1989) and Moskowitz and Grinblatt (1999) have highlighted the role of business conditions and industry factors in driving stock returns. Barberis, Shleifer, and Vishny (1998) emphasize the influence of investor sentiment on sector performance. Additionally, Blitz, Huij, and Martens (2011, 2012) have demonstrated the benefits of incorporating momentum and value signals across different sectors for portfolio allocation.

While sector rotation strategies have been extensively explored in equities, their application in the cryptocurrency realm is relatively nascent. However, researchers are increasingly recognizing the potential benefits of sector rotation in the crypto space.

Initial research in the crypto domain has focused on analyzing correlations between different cryptocurrencies to identify potential sector rotations. By classifying cryptocurrencies into sectors based on underlying technology, use cases, or market focus, researchers have sought to identify patterns and trends that can be leveraged for sector rotation strategies. Studies such as Lennartsson and Svec (2018) and Biais, Bisière, Bouvard, and Casamatta (2019) have shown promising results, indicating that sector rotation strategies in cryptocurrencies have the potential to generate excess returns.

The initial stage involves the mapping of cryptocurrencies to sectors and the creation of sector indices. To achieve this, a minimum market capitalization threshold of $15 million has been set for a cryptocurrency to be classified within a particular sector. CoinGecko has been utilized as a resource for this purpose, although it should be noted that there may be instances where a crypto asset is associated with multiple sectors. In such cases, personal judgment has been exercised to determine the appropriate sector classification.

Subsequently, data retrieval has been conducted through the utilization of an open-source cryptocurrency API, with cryptocompare being employed for this purpose. Python programming has been utilized to generate historical sector returns. The resulting outcomes are presented below:

Next steps — update model and generate weekly heatmaps + create backtest model to test hypothesis.