---
title: Cryptocurrency Factor Diversification
url: https://www.ml-quant.com/papers/ssrn/4744431/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4744431
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4744431
featured: 2024-03-06
citations: unknown
topic: Crypto & DeFi
---


# Cryptocurrency Factor Diversification

Research indicates that adding size and momentum-based cryptocurrency factors to a stock-bond portfolio can significantly diversify it, with machine-learning asset allocation strategies enhancing these benefits.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4744431
- Identifier: SSRN 4744431
- Released: 2023-01-09
- First featured: Quant Letter No. 39 (2024-03-06): https://www.ml-quant.com/issues/2024-03-06/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Crypto & DeFi

## Related

- [Machine Learning Cryptocurrency Returns](https://www.ml-quant.com/papers/repec/eee-finana-v-94-y-2024-i-c-s1057521924001765/): Machine learning can predict return in cryptocurrency markets, primarily based on simple factors like market price and momentum.
- [Factors Influencing Cryptocurrency Prices: Evidence from Bitcoin, Ethereum, Dash, Litcoin, and Monero](https://www.ml-quant.com/papers/arxiv/2511.22782/): The study examines the price factors influencing five major cryptocurrencies from 2010-2018, highlighting the roles of market conditions, long-term appeal, and the SP500 index.
- [Cointegrated Portfolios and Volatility modeling in the Cryptocurrency Market](https://www.ml-quant.com/papers/ssrn/4756389/): The study finds a correlation between cryptocurrency market volatility and trading spread, suggesting portfolios based on the Johansen procedure yield the highest returns.
- [Stylized Facts of High-Frequency Bitcoin Time Series](https://www.ml-quant.com/papers/arxiv/2402.11930/): An analysis of the Bitcoin market index from 2019 to 2022 reveals two periods of volatility, suggesting greater market efficiency at shorter time scales.
- [Modelling crypto markets by multi-agent reinforcement learning](https://www.ml-quant.com/papers/arxiv/2402.10803/): A multi-agent reinforcement learning model simulates crypto markets using Binance's daily closing prices of 153 cryptocurrencies from 2018 to 2022, accurately emulating crypto market microstructure.
- [A Data Engineering Framework for Ethereum Beacon Chain Rewards: From Data Collection to Decentralization Metrics](https://www.ml-quant.com/papers/arxiv/2402.11170/): A study of consensus reward data from the Ethereum Beacon chain offers insights into reward distribution and evolution, aiding in the assessment and refinement of blockchain systems' decentralization, security, and efficiency.