There is a relentless proliferation of new tokens competing for investor consideration within the cryptocurrency market right now. Despite Bitcoin's role as the foundational cryptocurrency, the returns exhibited by some of the newer tokens are prompting investors to evaluate their investment potential. The question then arises, how might an investor approach building and managing a portfolio of these newer tokens?

One approach relies on the use of fundamental analysis and discretionary decision making. This approach requires the timely analysis of each token’s economics and technology. For the average investor, the effort required to analyse any one token multiplied by the number of new tokens being created likely results in some degree of decision-making paralysis, fatigue, or avoidance altogether. Even if an investor can handle the analysis requirement, making rational discretionary decisions in a market characterised by extreme price volatility adds another challenge. Whilst this approach may yield significant returns for highly skilled investors, its overall difficulty makes it unlikely to yield the same returns for average investors.

An alternative approach utilises a custom indexing strategy, designed to simplify the investment process while capturing the performance of newer tokens. This strategy leverages a set of customisable filters to streamline the selection process and these buy and sell decisions are made automatically in rebalancing periods at a frequency chosen by the investor.  This approach bypasses any requirement for fundamental analysis or discretionary decision making, making it more feasible for the average investor. To investigate whether this approach will yield appealing returns, we conducted a series of exploratory studies.

Evaluating the returns of younger and older tokens

To begin building an indexing strategy, we need some way of identifying the ‘newness’ of a token within the cryptocurrency market. Themelia provides an ‘Age’ filter to investors for use when building indexes. Age is derived from the ‘Date Added’ data point provided by our data aggregators, and it represents the first day the token was listed. For instance, Bitcoin has a Date Added of July 13, 2010, which, at the time of this analysis, gives it an age of almost 13.87 years. Since the inception of the cryptocurrency market and the existence of the data aggregators that followed soon after, the Date Added and its derivative, Age, has become an excellent proxy for each token’s lifespan. 

Given Bitcoin is the oldest of all tokens, it follows that all other tokens have an age somewhere between 0 and 13.87 years old and we used this as the age range for our first study.      

The initial step involved segmenting the age range as follows:

• Segment_1 encompasses tokens representing the newest 25% of the age range, i.e. 0 to 3.47 years.
• Segment_2 encompasses tokens representing the oldest 25% of the age range, i.e. 10.4 to 13.87 years.

Based on these two segments we used the Age filter in the builder to create two indexes; Younger_Tokens_1 and Older_Tokens_1.

Following this, we used a Thematic filter to exclude any tokens classified as 'Stablecoin'. This ensured that our analysis did not reflect the performance of tokens designed to maintain a fiat currency peg. 

For the purposes of this study, we did not use any additional Market Capitalisation or Daily Volume filter, so all tokens within the Themelia universe were available for selection. In practice, investors might consider applying additional filters for Market Capitalisation or Daily Volume to ensure that any tokens selected possess sufficient liquidity for their investment requirements.

We chose to construct equally-weighted indexes over capitalisation-weighted indexes to provide a picture of the performance of each index without the results being dominated by tokens with larger market capitalisations. 

Any filters selected are applied at the inception of the index as well as any subsequent rebalancing periods, so we selected a weekly rebalancing frequency to give us the most control over the portfolio constituents.

The chart below shows the performance of both indexes over the last 365 days.

The overall return for the period was 181.97 percent for the Younger_Tokens_1 and 67.22 percent for the Older_Tokens_1. This represents outperformance by Younger_Tokens_1 of 114.75 percent.

To deepen our understanding in a second study, we took our original Segment_1 of 0 to 3.47 years from our first study as the new age range and then divided it into two new segments:

• Segment_3 encompasses tokens representing the newest 25% of the new age range, i.e. 0 to 0.87 years.
• Segment_4 encompasses tokens representing the oldest 25% of the new age range, i.e. 2.6 to 3.47 years.

We then used the Age filter again to create two new indexes based on these two segments; Younger_Tokens_2 and Older_Tokens_2.

All other filters and settings were left as they were for the first study. The chart below shows the performance of these two new indexes over the last 365 days.

The results confirmed the first set of observations, except in this case there was a more significant outperformance overall by the Younger_Tokens_2. With returns of the Younger_Tokens_2 and Older_Tokens_2 of 385.25 and 141.17 percent respectively, the Younger_Tokens_2 outperformed by 244.08 percent.

We felt compelled to repeat the process for a third study to see if the observed pattern continued, so we used Segment_3 of 0 to 0.87 from our second study as the new age range, and divided it into two new segments:

• Segment_5 encompasses tokens representing the newest 25% of the new age range, i.e. 0 to 0.22 years.
• Segment_6 encompasses tokens representing the oldest 25% of the new age range, i.e. 0.65 to 0.87 years.

Again, we used the Age filter to create two new indexes based on these two segments; Younger_Tokens_3 and Older_Tokens_3.

All other filters and settings were left as they were for the second study. The chart below shows the performance of these two indexes over the last 365 days.

The results were consistent with the previous studies, although in this case the outperformance by the younger tokens was more emphatic, with the Younger_Tokens_3 returning 1259.52 percent and the Older_Token_3 returning 189.85 percent. This represented the most significant outperformance of 106.67 percent by the Younger_Tokens_3, of all three studies.

Conclusion

The consistent patterns observed across all three studies, each with indexes built on different sets of age filters, suggests a robust correlation between a token's age and its performance, with newer tokens outperforming. In some respects, this is what one might expect; as newer tokens are created that have potentially more powerful economic technologies and therefore greater future values, it would seem logical that they would exhibit greater price appreciation. Although, this should be measured against the fact that Bitcoin, the oldest token in existence has outperformed many tokens from younger eras. Whether the phenomena observed in these studies is just a function of cryptocurrency token hype cycles, or whether these newer tokens will deliver on their longer-term promises, might not even be relevant. For investors, this could simply represent an opportunity to capitalise on the returns of younger tokens.

If so, the challenge lies then in deciding how best to capture these returns—whether by building a portfolio using fundamental analysis and managing that portfolio with discretionary decision making, or, by deploying an indexing strategy. While the first approach allows for skilled investors to outperform, custom indexing strategies allow the average investor to maximise their competitive advantage.

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