Hi @Monportefeuille ,
A few thoughts from me.
It’s fantastic to see the application of data-driven methodologies to component selection. It’s a step up from the simple market cap weights (which I’m a big fan of for Trad Fi and DeFI) not to mention the community sentiment selection criteria used by other protocols.
I think the name will benefit from some more brainstorming.
1 month inclusion time neglects the impact of initial product pumps etc. I realise that the early life gems offer more upside (and better Sortino ratios ???, but they are more risky.
It’s not clear to me whether the methodology accounts for liquidity mining. For example, Alpha is currently offering staking rewards on the native token which I assume is based on token reserves (unsustainable) rather than protocol income (sustainable). Does this LM impact the ratio? Should it? (The methodology may well allocate to tokens during LM and exit before the rewards become ineffective).
Provision of more backtest data is essential. Seeing how it could have worked will build confidence that the model / application of the model works in Crypto. It would also be good to see a record of recommended weights going forward with the actual returns calculated as time passes - then we can see how the prediction works vs the market.
I would also be interested to see how the recommended allocation changes over time. [One of the issues we identified with the TTI methodology was that large swings in weights / additions / removals as tokens get repriced by the market resulted in significant rebalancing. This adds friction to the management of the product. Particularly when the trade size becomes significant compared to the token liquidity.
One problem I have is that while the model is great for a rational / efficient market, my worry is that crypto markets just don’t behave in the way the model holds [Maybe I should just convert my holding to a total market cap portfolio…]
I think that the biggest challenge is whether the market is looking for a product like this. The coop has experience of launching products that are statistically sound but don’t match what the market is doing ($GCI is conservative in a Bull market), and more data-driven products ($TTI) have been deferred.
Can we convince ourselves that people will choose to hold this product with long term conviction compared to others? Selling the use case for $DPI and $MVI is pretty straight forward ( You need broad DeFI exposure, The metaverse is early but has the opportunity to be high - if you pay attention).
In the current market, I think the only downside strategy that our (current) customers are considering is reducing their leverage. So, I’m not seeing huge demand for smart Beta (Yet). [Which I think is similar to @verto0912 's comments]
I know that the investor relations team have been talking to a few more traditionally-minded investors and I think hat the challenge is convincing them that tokens outside BTC / ETH / etc have value / uses cases. Then that DPI is a good strategy. I’m sure they will succeed in this, and that smart beta products will eventually gain traction (we want to be early to capture these people, but are we too early?).
What assessment have you done on customer demand over the next 12 months?
It really is an impressive strategy, I think that the outstanding challenges are:
- showing that it out performs
- Estimating the size of the market available in the medium term.
Thank you 