Why traditional Hedge Fund Strategies don’t work in Crypto
Hypothesis: The most effective way to deliver a market-beating return within the Digital Asset Class is to use deep fundamental analysis, through an effective, proven bottom-up process, to distill the list of available investments into a well-thought-through portfolio.
In traditional markets, portfolio optimization takes many forms. But, in recent times, Modern Portfolio Theory (MPT) has taken over, principally driven by Henry Markowitz. This principle, which sees techniques such as Risk-Parity and Mean-Variance Optimization to derive a portfolio return with a lower risk profile. This has become the industry standard, looking at variance and correlations between individual positions within a portfolio.
However, within the Digital Asset Class, due to the nascent stage it is currently in, investors are quite limited in their ability to focus on various portfolio optimizations from a volatility or correlation perspective. Much of the studies done to determine more efficient portfolio frameworks where performance or risk are augmented by analysis of historical data, point in the same direction as the work done by DeMiguel et al in Optimal Versus Naive Diversification: How Inefficient is the 1/N Portfolio Strategy?. They conclude that “various optimizing models in the literature… there is no single model that consistently delivers a Sharpe ratio or a CEQ return that is higher than that of the [benchmark] portfolio…”. This work was focused on Equities, however, Patanakis et al followed up in 2018 with Optimal vs naive diversification in cryptocurrencies and come to the same conclusion within the Digital Asset Class. These studies are supported by the various research studies found at the end of this document. All these studies point to the same conclusion, and there is yet to be a substantial, consistent outperformance using a variation on MPT within the space.
Much of the research done on portfolio optimization, has focused on portfolio optimization techniques relative to individual cryptocurrency positions. This has in most cases resulted in the consistent conclusion that a research-based portfolio framework is superior in driving alpha, over a volatility or other risk metric-based optimization technique.
Further to this, over the recent years, according to PwC, long-only funds have consistently outperformed and we believe the above is the reason why. Not only that, we have seen that index products are limited in their returns relative to a basket of targeted exposure.
Diversification
In this space, having too many positions to monitor creates considerable risk within the portfolio, as there is so much change and evolution on a daily basis. Therefore, designing an optimal portfolio framework that seeks to limit individual position count to ~20–25 proves to be most effective. Not only are they easy to manage at the top level, but it also means you can spend a lot more time, dedicated to each position. Analyzing and monitoring for both upside and downside catalysts. Not only that but it is also shown to be sufficiently diversified so as to limit risk versus a broad market benchmark as well as versus the individual asset risk profiles.
When considering ongoing assessment and monitoring of individual positions, we must consider the work done by Corbet et al, in “Cryptocurrency Reaction to FOMC Announcements: Evidence of Heterogeneity Based on Blockchain Stack Position”.
“Our primary observation is that digital assets do not in fact, react in an identical manner, and so, should not be viewed as one category or market. Currencies appear linked to the FIAT market, and remain connected (at least in a statistical sense), to the policy making decisions of traditional FIAT banks (Federal Reserve). While we isolated currencies from the rest of the application layer, we observe other forms of application to display a similar reaction. Our most significant finding however, is the observation that protocol based assets display evidence of a completely different reaction, in some cases moving in an opposite direction to currency based applications. Such findings confirm our previous proposition, and warrant digital assets to be evaluated based on their use, and place, within the blockchain stack rather than viewed as a single entity within the cryptocurrency market.”
This conclusion supports our view that within this space, positions must be handled from an analytical perspective individually, and without broader market or categorization assumptions. You can group individual assets within generic categories, such as Smart contracts, specific protocol ecosystems, or sub-sector focus like Oracles. However, this, as the above study points out, does not mean they move with higher than usual correlations with regards to positive or negative sentiment shift. Therefore, the Investment Committee will focus on individual positions, without making typical market assumptions about correlations that you would see in traditional equities markets.
The work done by Corbet et al is supported by a previous study of theirs which considers Cryptocurrency as a portfolio diversifier for a traditional investor. Within the assessment of that hypothesis, they concluded three key findings, which relate directly to Dragonfly AM’s approach to raising AUM and managing it:
1. “The analysis of spillovers in the time-frequency domain demonstrated a lack of linkages across markets at short frequencies.”
2. “…found the Bitcoin price can affect the levels of [others]… according to the identified dynamics of pairwise spillover, the influential power of Bitcoin is particularly evident for periods of rapid price increases in Bitcoin. Our results revealed the presence of a positive contagion effect across cryptocurrency markets.”
3. “…There is a role for cryptocurrencies in an investor portfolio but that their structure and behaviors also indicate the cryptocurrency market contains its own idiosyncratic risks that are difficult to hedge against…”
In summary, we acknowledge that this space is evolving constantly, and as such we will continue to research the most effective ways to drive a return whilst managing risk for the portfolio. It is clear that, at present, the most impactful way to manage capital within the Digital Asset Class is a combination of Long-Only exposure, with deep, detailed fundamental analysis of each project and individual management of each position. The various studies done show that the ability to drive returns through quant strategies is limited, and the outcome is almost always underperformance relative to the benchmark (bitcoin or the top 100 index).
Individual Asset Weight Limits
Further consideration must be taken with regard to individual positional limits. Given that we have shown Digital Asset portfolios to be unable to respond effectively to MPT, we have devised our own approach to managing risk, in the form of ‘exit liquidity assessments’. Our approach focuses on understanding the key limitations of a typical portfolio manager and responding with an effective alternate strategy. Our alternate strategy results in a better, more rounded approach to this ever-evolving space.
The various studies done on Portfolio Optimization, as we have shown, support this premise. Therefore, we have opted to focus on the more manageable, and incredibly important element, Volume. Daily volume is a key indicator in the space, offering a number of important insights into a project’s level of interest from traders and investors, as well as signaling potential forward risk. For us, we will be monitoring the volume of each live position, as well as potential future positions.
Finally, when you are building your models and analyzing your portfolio framework, you must consider how it scales. A portfolio that works at $1 or 2 million, may falter at $10m. It’s important to always consider the longevity and the future-proofing of any investment product you build.
Research:
Portfolio Risk Assessment under Dynamic (Equi)Correlation and Semi-Nonparametric Estimation: An Application to Cryptocurrencies — Link
Burggraf, T. (2019). Risk-Based Portfolio Optimization in the Cryptocurrency World. Energy RN: Energy Economics(Topic). — Link
Schellinger, B. (2020). Optimization of special cryptocurrency portfolios. The Journal of Risk Finance, 21, 127–157. — Link
Härdle, W.K., Petukhina, A. (2018). Portfolio Optimization with Cryptocurrencies — Link
Jiménez, I., Mora-Valencia, A., Ñíguez, T.M., & Perote, J. (2020). Portfolio Risk Assessment under Dynamic (Equi)Correlation and Semi-Nonparametric Estimation: An Application to Cryptocurrencies. Mathematics. — Link
Ma, Y., Ahmad, F., Liu, M., & Wang, Z. (2020). Portfolio optimization in the era of digital financialization using cryptocurrencies. Technological forecasting and social change, 161, 120265. — Link
Corbet, S., Larkin, C., Lucey, B.M., Meegan, A., & Yarovaya, L. (2017). Cryptocurrency Reaction to FOMC Announcements: Evidence of Heterogeneity Based on Blockchain Stack Position. ERN: Central Banks — Policies (Topic). — Link
Corbet, S., Larkin, C., Lucey, B.M., Meegan, A., & Yarovaya, L. (2017). Exploring the Dynamic Relationships between Cryptocurrencies and Other Financial Assets. — Link
DeMiguel, V., Garlappi, L., Uppal, R. (2007). Optimal Versus Naive Diversification: How Inefficient is the 1/N Portfolio Strategy? — Link
Platanakis, E., Sutcliffe, C. and Urquhart, A. (2018) Optimal vs naïve diversification in cryptocurrencies. Economics Letters, 171. pp. 93–96. — Link
Brauneis A., Mestel R. Cryptocurrency-portfolios in a mean-variance framework. Finance. Res. Lett. 2019;28:259–264. — Link
Borri N. Conditional tail-risk in cryptocurrency markets. J. Empir. Finance. 2019;50:1–19. — Link
Liu W. Portfolio diversification across cryptocurrencies. Finance Res. Lett. 2019;29:200–205. — Link
Moose_22 is co-founder of Dragonfly Asset Management
DISCLAIMER: This content is for EDUCATIONAL AND ENTERTAINMENT PURPOSES ONLY and nothing contained in this blog should be construed as investment advice. Any reference to an investment’s past or potential performance is not, and should not be construed as, a recommendation or as a guarantee of any specific outcome or profit.
