Conducting a Portfolio Review in Crypto as a Martian
“Lessons in life will be repeated until they are learned”
As 2022 draws to a close, what lessons can we in the digital asset investing space draw from the tumultuous events of the past 12 months? One of the best pieces of advice I have ever received for managing investment portfolios is to periodically review your portfolio as if you were a ‘Martian’, i.e. with ‘fresh eyes’, as if you didn’t have the influence of recent price performance or other backwards-looking and ultimately useless information or emotions. A Martian looking at an asset for the first time does not have any emotional connection to it, nor do they have an agenda, bias or hubris to contend with. A Martian mindset is therefore very useful in providing absolute clarity when reviewing your portfolio. Today I would like to explain why this unemotional and unbiased periodic review is one of the best end-of-year strategies we can add to our investment approach.
There are only two (often interlinked) variables to successful investment management: risk and return. Of the two, I feel there is often too little emphasis on the risk variable. Managing risk in your portfolio is one of the most important drivers of long-term, consistent performance. There are many different ways you can manage risk, which vary in their applicability to each asset class and goal of any given portfolio. For me, looking historically at how the portfolio reacts to a range of short-term stimuli is a very instructive source for identifying risks within a portfolio. Although we are looking backwards, as long as we can look at the picture with ‘fresh eyes’, we can derive important insights to enhance our investment decision-making going forwards. You are basically forcing yourself to ask the question ‘what would I do today if I saw this investment proposition for the first time’?. Sadly, there is not one neat and all-encompassing measure which can give you all the answers. The list of risk metrics I look at is wide-ranging.
Of course, you cannot identify great investments by just focusing on risk. Even in the digital asset space, there is no getting away from the need to use in-depth research and analysis to identify protocols with excellent fundamentals and sustainable competitive advantages as these factors have been shown time and again to ultimately drive long-term returns. However, my view is that adding a dispassionate assessment of risk on top, through monitoring reactions, beta and other metrics only serves to enhance your detailed bottom-up analytical assessment. As we have shown before, statistically, long-only portfolios outperform in digital assets. So with all this in mind, I focused on making sure that risk assessment figures alongside the assessment of return potential when reviewing my portfolio.
So how can you in practical terms conduct such a review? The annoying but honest answer is it depends. There are a number of risk factors which can be more or less relevant depending on the type of asset in question, and the objective of the portfolio. Perhaps the group of factors that are most broadly relevant to all investments are macro ones. I suppose the first step, therefore, is to identify which risk metrics are most suitable for the type of assets in your portfolio. The key is that these metrics should serve you with the most unbiased, methodical and reliable set of key data points.
In this piece, I’m going to focus on two key risk metric examples:
- Macroeconomic data
- Beta and Alpha
These two are in no way an exhaustive list but do offer the initial building blocks for using backwards-looking data to frame your view on individual assets properly.
With that in mind, I’m going to present just a small window into what I have found to be a good way to frame your process. As, when you start this process, it is important to have objective, up-to-date information on all assets, from which you have a new baseline that you begin to add (good metrics) or subtract (bad metrics) points, often through trial and error in fact.
So for me, I am quite interested in how assets react to macroeconomic market news. I believe that there is a lot of predictive power in this because it can often introduce some new hypotheses which otherwise wouldn’t have been considered.
So first, looking at a selection of some interesting digital assets, let’s look at when the CPI data release was worse than expected and calculated the average 1-Day response of this selection of assets:
Immediately, we can see that Near performs particularly poorly. However, with an average return of -3.23% during this event, Near returning -7.55% doesn’t step too dramatically far outside of the average range, especially when factoring in the much higher volatility of this asset class. That does not however change the fact that Near consistently reacts much worse than its peers to negative news. For me, this indicates a generally poor sentiment towards the protocol and suggests that when times are bad, the tokens are used as the first port of call in terms of a source of cash by investors.
The interesting outlier here is Lido. It is interesting because it bucks the negative trend. Now, immediately I’m trying to come to a bunch of conclusions as to why, but then I remember… I’m a Martian! For now, I’ll take note that on the announcement of market-unfriendly macro news:
- Near — Performing very poorly
- Lido — Performing very well
So the only information I have carried with me is that this selection of assets on average falls -3.23% on bad macro news, and that Near really underperforms while Lido outperforms
So now I’ll look at when the CPI is better than expected:
Now let’s look at when the CPI is better than expectations. Here we get a pretty predictable response to good news. All of the assets are up across the board, by an average of 11.83%. Note, however, Lido once again comes out on top in this scenario. We have now seen that when macroeconomic news is bad, Lido is relatively unphased. When it is good, Lido outperforms. Consistent momentum is a very positive sign, and is something that typically bodes very well for any asset’s long-term return potential.
Now, in order to dig a little deeper, I would want to see how these assets react over a slightly longer period of time. Analogous to seeing how the water reacts to the pebble, and then how it looks once calm. For this, I have compared 1-Day reactions with 5-Day reactions:
Quant is the major outperformer here, where it seems that as token price sells off, buyers come in and ‘hoover’ up the tokens. This is extremely bullish, and suggests that even when the market is falling, people use it as an opportunity to acquire more Quant. This simple price performance reaction shows real conviction in the growth expectations for Quant. Conversely, we see Near very much underperforming even in this slightly longer timeframe.
I love this chart because it highlights how much of the volatility in the market is just ‘short-term noise’ and that even a few days past a macro surprise event, fundamentals have taken over as the key driver of returns. As regular readers know, we are long-term investors. My point in using these very short-term charts is simply to identify the market’s view and expectations towards each asset. In the chart, while the reactions are initially positive across the board, only Lido ends the 5-Day period marginally positive and most first-day gains are lost, some would say effectively displaying the textbook bear market backdrop.
The reason these comparisons are important is that they allow us to successfully reframe each asset, understand how the assets have moved and how as time progresses they respond to various stimuli. This helps us to not only time our purchases and assess how each asset is likely to affect the overall volatility of the portfolio, but perhaps more importantly also to gain an understanding regarding the market’s conviction and expectations regarding the asset in question.
Taking short-term price volatility as an indicator of general investor expectations regarding a protocol, for me, some other interesting observations emerge:
- Cardano — Routinely shrugging off bad news, with investors seeing its sell-offs as a buying opportunity, perhaps reflecting a renewed optimism about the protocol’s outlook
- BNB — Similar to above, but also offering much lower volatility across all market conditions, perhaps reflecting its ‘safe haven status’ in the eyes of the market
I hope you can see that these simple examples help add to a 360-degree view of a particular investment, allowing you to assess each one objectively and within the context of its peers.
One of the most commonly used risk metrics in investment terms is Beta. In the next section, I would like to explain what Beta is and how we can use similar analysis to help us understand the risk profile of our portfolios.
What is beta?
“Beta (β) is a measure of the volatility — or systematic risk — of a security or portfolio compared to the market as a whole (a chosen sensible benchmark). Stocks with betas higher than 1.0 can be interpreted as more volatile than your chosen benchmark.”
With the blue line denoting the average beta — 0.187, we can see that generally, this selection of assets is much less volatile than the chosen benchmark. This bodes very well, as it means that they generate an average excess alpha of 18.7%, whilst delivering lower volatility.
A high Beta can be a positive or a negative in a portfolio. If you think about it, if markets are rising strongly, a higher beta means your portfolio will — in theory at least — outperform. However, it will be more volatile. Alpha is different and can be defined as outperformance relative to a given benchmark. A higher alpha is always a good thing in portfolio management. It means investors in the fund achieved a higher return for the risk they took.
Looking at the chart, BNB and Quant show extremely good alpha, while producing lower than average beta — Quant especially so. This is incredibly important because while looking at the previously introduced macroeconomic reactions is important, it is more important to begin to piece these individual puzzle pieces together and start to view that 360-degree assessment of each position, without ignoring additional important data points. While Quant didn’t show up too much on the macro reaction data, it does now appear to be an extremely good holding in terms of generating all-important alpha for the portfolio.
Final Thoughts
I have just used two risk metrics as examples of the myriad of risk assessments we routinely undertake when assessing portfolio risk. This risk assessment complements but in no way replaces the importance of fundamental analysis. In assessing Digital asset protocols, we have found there really is no better way of identifying great investments than a deep dive into the fundamentals. Here we put a great store in analyzing the on-chain data, such as protocol revenue trends, wallet activity, TVL, development activity, roadmaps and much more.
Overall, it strikes me that my approach to assessing risk in a portfolio is perhaps a strange combination of systematic/robotic but also creative trial and error! I feel it’s exactly what a Martian would do! The important point is that wherever possible, we need to remove our emotional biases when assessing individual positions we already hold. By doing so, we can massively improve your ability to identify future winners and future losers, with ‘fresh eyes’.
Moose is Co-Founder & Analyst at 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.
