Cryptocurrency as an Investable Asset Class: Coming of Age

Battle Count: 83

Highly relevant for quantitative trading in cryptocurrency markets. The paper provides: (1) validated smart beta strategies (CSIZE, CMOM, CVALUE) with post-2020 robustness; (2) evidence that traditional equity factors (MOM12, MOM24, VOL, VOLUME, betas) do NOT work in crypto; (3) jump risk characteristics critical for risk management; (4) cross-exchange arbitrage analysis showing implementable returns are ~4% weekly (not the 68%+ theoretical); (5) crypto carry trade profitability compression since 2024; (6) factor model structure (C-4) for systematic portfolio construction; (7) higher-order factor selection (FS-FMB) for nonlinear alpha capture; (8) correlation regime changes affecting diversification benefits. Directly actionable for crypto quant strategies, though the paper is a review rather than a single novel strategy.

Moderate complexity. The factor model construction (C-3, C-4) and smart beta strategies are straightforward quintile sorts. Jump detection requires tick-level data and BNS test implementation. The FS-FMB procedure involves sequential factor selection with Fama-MacBeth regressions. The KA factor requires implementing Kolmogorov-Arnold representation. Cross-exchange arbitrage requires data from 165+ exchanges with FX conversion. The carry strategy requires perpetual futures data at 8-hour frequency. Main challenges: data aggregation from multiple sources, handling survivorship bias with 47,000+ coins, and implementing the higher-order factor selection procedure. No code is provided.

The paper is a review/survey that synthesizes existing literature. Data sources are identified (CoinGecko, Binance, Kraken, CryptoCompare, Bloomberg, CaPiRe dataset, FRED). However, specific code for reproducing all analyses is not provided. The methodology is well-documented with clear sorting procedures, sample construction rules, and statistical tests. Key parameters like winsorization levels, market cap thresholds, and sample periods are specified. The FS-FMB and KA methods reference prior working papers by the same authors.