Prediction markets aren’t a new concept, but they are a tool being used more widely these days. For the most part, these are being used for pseudo-gambling on events, but there’s something more interesting in them for me. What if a prediction market could be used as a tool to model risk in the same way What does an actuary do for an insurance company? If we presume that a prediction The market can leverage information asymmetry; could we then use the price The mechanism of a prediction market to determine risk and act accordingly when managing a fund?

For example, there are quite a few protocols that are launching to provide yield to users who store their funds in the protocol for spending purposes. Let’s call these stablecoin reward protocols. At a simple level, they work like so:

1. A user provides to the protocol $100 of USDC, and in exchange, the user receives spendable notes which can be used for everyday purchases like groceries or e-commerce transactions by anyone who accepts the protocol.
2. The USDC is then staked to generate rewards, and an internal ledger is kept for who possesses what percentage of the backed funds.
3. Eventually, someone may withdraw some or all of their funds, such as a merchant who needs to pay their bills or a settlement intermediary like a card issuer paying a merchant bank, at which point some portion of funds is withdrawn from Aave, and then paid out to the recipient address.
4. At regular intervals, the yield generated from staking is used to buy back BAT and returned to people who’ve locked their funds into the protocol and are spending with it, and in this way, they get cashback just like if they had a bank account and a debit card.

Something to note, though, is that the crux of this design relies on the returns of the protocol generating competitive yields. For example, banks can generate profits on bank deposits because they can use customer funds and leverage from fractionalized lending, so that they can provide loans like mortgages. The loans cover the costs of the interest returned to the bank account holder and allow the bank to return a profit from the difference between the interest earned on the loans and the interest paid to get the funds to provide the loans.

In other words, the maximum rate the bank can return is determined by the interest they can charge on the loan. In Defi terms, that applies in the same way that the maximal yield that can be returned is relative to the market rate of lending USDC or another underlying asset to Aave or some other lending protocol.

So how could someone generate a higher yield? By not just simply holding the funds but instead swapping the stable assets into more volatile assets and trading them effectively, operating like a hedge fund. This is effectively what FTX was doing business with funds deposited into their exchange and allowing Alameda Research to use them and take the profits for themselves. In theory, this could be built into a protocol too, but the financial risk remains.

To define the risk, let’s take the above example again of a user putting $100 of USDC into the hedge fund protocol, and then the hedge fund automatically swaps the $100 from USDC into a memecoin. If that memecoin drops to $0, then when the user goes to withdraw their $100 later, the protocol cannot return $100 because it doesn’t have it. Instead, it’s got effectively worthless memecoins, which can’t be swapped, and then the user who submitted the $100 is now without their funds.

Now, let’s say the memecoin jumps to $1000 and the hedge fund exits the trade back to $1000 USDC, then the protocol has now generated $900 of profit, which could then return a 900% yield to the user minus any fees. In this sense, the risk That was taken, which generated a massive reward for the user simply by placing funds in the right protocol, and this exemplifies the risk-to-reward ratio when it comes to the management of funds.

So, how can we generate higher yields than simple collateralized lending with Aave, but do so in a way that hedges the risk a bit? We can use prediction markets as both a risk canary signal and as an arbitrage opportunity to generate risk-adjusted returns. Let’s say, for example, $80 was spent on the memecoin, but the other $20 was spent on a prediction market bet that the price of the token would not be at least $80 by the end of the day when the user withdraws their funds. In this case, the prediction market on the price would behave similarly to how an options contract works, hedging the risk.

However, what if we wanted to hedge some other type of risk, such as the loss of funds due to a protocol hack occurring on Aave, which made it, so the stablecoin reward protocol couldn’t return funds to the user? We could achieve this by betting “no” on a prediction market that won’t be hacked before a set time. If the protocol doesn’t get hacked, then the fees won from the prediction market are stored away and can be returned to the user or used to cover losses later in the event of a future hack. However, if a hack does occur, then the user could take a “yes” bet on this as insurance to cover their funds. Given the probability should be relatively small, then the cost to insure their losses on the prediction market should be relatively small. In other words, they may need to spend 2 cents to get 98 cents back.

Here are some ways that I could see this prediction market framed.

Will account ABC123 lose access to their full deposits minus any internal transfers between now and the withdrawal event? In this way, the cost to the user is something small, like 1 cent or 2 cents, and the cost to the protocol to ensure is very high. The protocol either generates funds from the successful withdrawal because the protocol stayed secure, or their bet is paid out because the protocol was hacked.

Additionally, since the prediction market would require evidence of a hack, the shifting of the yes bet upwards can act as a canary signal to the user to automatically withdraw the funds. In this way, a hacker could conduct the hack and receive both the funds from the uninsured (since it’s an opt-in bet) and a portion of the bet.

I’m not totally convinced the incentive structures are properly structured yet, But I think there’s merit to the use of prediction markets as an insurance mechanism. Can anyone come up with a better structure that generates revenue for an arbitrary protocol, protects the user’s funds in a catastrophic event like this, and allows the hacker to claim some portion of the funds as a bounty structure (presumably they’d return the uninsured funds to prevent criminal conviction and claim the bounty via insurance)