Finding Signal in markets.vote

An analysis of the first year of markets.vote by @ShiftRunStop#8542

Introduction to finance.vote

finance.vote Logofinance.vote are building a suite of dApps for DAOs. A ready made box of tricks that will it quick and easy to start new Decentralised Autonomous Organisations. They have already built working tools to facilitate a fair token launch, price discovery and prediction, governance voting, liquidity mining and staking and a payroll system. These tools use a variety of galaxy brain crypto-economic technologies and strategies such as NFT-gated sybil resistance, quadratic voting and gamified yield farming using Harberger taxes.

Find out more about finance.vote on their website.

Introduction to markets.vote

markets.vote Logofinance.vote's markets.vote are incentivised prediction markets for a range of cryptocurrency tokens. markets.vote has been running since November 2020 on Ethereum and since April 2021 on Binance Smart Chain. On each chain, finance.vote ID holders use quadratic voting to choose which token(s) they think will perform best over the coming week. At the end of the market period, 100,000 FVT is distributed amongst all those who chose the winning token, proportional to the number of votes they assigned the winner. Winning voters will also receive more voting power for forthcoming vote markets. The list of coins to vote on is different on each chain.

On Ethereum the list is dominated by the big players in DeFi as well as the OG cryptocurrency, Bitcoin, and a USD stablecoin.

The list of coins on Binance Smart Chain is a more general mix of high cap cryptocurrencies and a USD stablecoin.

A more detailed summary of markets.vote and analysis on users' voting habits can be found in this document by Discord user @Lizzl Datadeo#8507.

Predicting the Market

The cryptocurrency market is notoriously volatile and unpredictable. It is clearly not possible to perfectly predict the future. But with the combined consideration, market analysis and intuition of hundreds of crypto degens will it be possible to predict the market more profitably than relying on your own strategy?

Beyond the fact that the crypto market is fundamentally difficult to predict, a number of other factors came into play that may have reduced the quality of prediction signal coming out of markets.vote.

All of these factors, and others, will have greatly increased the noise in the data produced by the prediction market. The question is, is it still possible to extract some signal from within that noise? Can we find a strategy that could have been consistently adopted based on the result of each week's prediction market that would have allowed an investor to outperform the market average?

Potential Strategies

A number of investment strategies that could have been adopted based on the week's prediction have been chosen for the purpose of this analysis. All of the historic voting data has been extracted from the contract as well as all of the token prices at the start and end of each round's market period. Using this data it has been possible to play out each strategy to see how much money would have been made (or lost) by adopting each.

The strategies chosen for this exercise were as follows. Each strategy begins with $1000.

Notes: Where applicable, any trading fees that would need to be paid each week when exchanging from one token to another have not been taken into account in this analysis. On the occasions where multiple coins received the same number of votes the capital was evenly divided between them both.

BSC markets.vote data

It's fairly clear from the similarity of the lines for all strategies on the chart above that no clear signal can be drawn from the historic markets.vote data on the Binance Smart Chain. This is almost certainly because mass hedged voting by a relatively small number of voters using multiple IDs has introduced too much noise to the data. It is notable, however, that despite the apparent lack of a clear signal in this data the strategy that would have earnt the investor the most money would have been to go all-in each week on the token that received the most votes from the markets.vote users.

Using the button above to respawn the BSC Crypto Hamster numerous times confirms that following no single strategy based on BSC markets.vote data would have produced a markedly different result than getting your pet hamster to do your investing for you.

ETH markets.vote data

In contrast to the data produced by markets.vote on BSC, on Ethereum there are distinct differences in outcome between the different strategies that could have been employed. One remarkable thing is that markets.vote voters consistently failed to choose the token that would perform best the following week. Furthermore, they often chose the token that would perform the worst. This is reflected by the fact that after 52 rounds of voting, if after each week an investor had moved their entire capital to the token predicted to perform best they would end up with only $852 from their initial $1000.

Even more remarkable is that the strategy that performed best over 52 weeks was for an investor to move their entire capital each week to the token that had received the fewest votes. If that strategy was consistently adopted the initial investment of $1000 would have become $10,369 after 52 weeks. This is a 12× better return than investing in the most voted for token each week.

Mashing the "Respawn ETH Crypto Hamster" button above quickly highlights that only very rarely would a near brain-dead rodent have made more money than if you had consistently invested in the very tokens that the markets.vote users told you not to. Conversely, only very rarely would little Hammy have done more damage to your initial $1000 investment than if you had consistently moved your investment to the token with the most votes.

Just so you know what you could have won, if the markets.vote users had perfectly predicted the market each week and you had consistently moved your entire capital each week to follow their predictions, your initial investment of $1000 would have become $267.5 million after a year.

References