Loopring Learning Series, Part 4: The Cost of Illiquidity
Ask someone what (il)liquidity is, and you’ll get familiar answers: the cost of trading an asset, the bid/ask spread, something that makes it difficult to buy/sell an asset — or enough of an asset — or finally, in crypto-land, something that punishes many currencies/tokens in the market. These are all correct, yet it seems that confusion still abounds around this important concept. This article reiterates a few maxims, and hopefully offers a few new ways to think about liquidity, or the lack thereof.
Buyer’s Remorse and The Liquidity Continuum
Aswath Damodaran describes illiquidity as the cost of buyer’s remorse: you just bought an asset, but you immediately regret it, and want to sell it back. The expense you incur on reversing your decision is the cost of illiquidity.
It follows that, if illiquidity is the cost of the buyer’s remorse, then perfect liquidity is the absence of buyer’s remorse, or, zero cost to immediately reverse a trade.
This leads to a point worth considering: there are no absolutes — an asset is not liquid or illiquid, but rather falls somewhere along a continuum of liquidity. To illustrate, consider the two extremes:
Perfect liquidity and zero remorse
In theory, a single immovable price would exist, the bid-ask spread would be zero, and there would never be an imbalance of supply/demand that could overwhelm one side enough to resort to moving its price. A market maker, or an infinite queue of orders on an exchange, would always stand ready to absorb your order, and regenerate a replica in its place. We can stop theorizing here, however, because with zero-spread — and with no chance of moving this gridlock to a higher or lower (yet-still-zero-spread) gridlock— there is no incentive for market makers nor speculators. Thus, we see that a perfectly liquid asset cannot exist assuming rational economic actors; the recursive logic would be: no incentive, no one shows up, no assets trade = no liquidity. We also see this implausibility with our own eyes: prices move, and market makers try to buy low and sell high.
Zero liquidity and terrible remorse
Looking at the other end of the spectrum, consider the old aphorism that “everything has its price”. This can actually be viewed as a quip about liquidity (and morality, greed, etc…). Ignoring sentimentality, it states you can sell/buy anything, as long as you’re willing to accept a lower/higher price. Thus, we see on the illiquid end of the continuum that there is no such thing as a perfectly illiquid asset. You can always acquire or dispose of an asset if you can stomach it.
Let’s back up a bit, though, and see why there should be any costs to trade assets to begin with, and what determines an asset’s location on the liquidity continuum.
[I’d like to note, many of the thoughts below can be found in all sorts of previous literature, but I borrow most heavily from Damodaran’s presentation of the facts. If you have time, you’d likely be better off reading his 60 pages on the matter.]
Trading Costs
Measuring the cost of illiquidity through trading costs, we can say that less liquid assets incur higher costs than more liquid assets (as a percentage of asset value).
There are 4 components to trading costs for any publicly tradable asset:
- Commissions (fees)
- Bid-Ask spread
- Price impact
- Opportunity cost
1. Commissions
These are the only explicit costs that traders see: paying a broker or exchange a nominal or percentage fee per trade.
In contrast, the following costs are all implicit — they don’t get extracted from our wallet or listed in a fee table on the side, but we certainly pay them, and they are often much larger than commissions.
2. Bid-Ask Spread
This spread is a market maker’s livelihood. If traders bounced around buying and selling at these prices all day in a sideways market, the market maker would earn the difference — easy money. In reality, this spread is their protection and is meant to cover the following costs:
A) Asset inventory: A market maker has some optimal inventory position they’d like to maintain. This may be because they’re constrained by risk, regulation, or capital. The bid-ask spread is their tool to converge on this level. If their bid is too high, they will amass a large inventory; if they ask too low, they will have a large short position. A wider bid-ask spread means they’re not bombarded by orders on one side or the other.
B) Processing: Each trade costs resources — humans filing paperwork, or, more realistically these days, fixed technology costs for data services — thus market makers/dealers need to ensure the trades cover these costs.
C) Informational disadvantage: Market makers are required to quote prices and execute with traders at these levels. Thus, they basically have informed ‘adversaries’ trying to take advantage of them. This is referred to as adverse selection. Traders have different motives for trading an asset: information-based, liquidity-based, or valuation based. The expected return of trading with these (sometimes) better-informed investors/traders is negative, so they have a spread to cover these losses.
All the above, plus a margin for this to be a positive yielding endeavor, is what constitutes the bid-ask spread.
3. Price Impact
Price impact is just what it sounds like; your trade impacts the price of an asset — pushing it higher when buying, and lower when selling. This happens when a trade creates an imbalance between buyers and sellers, and the way to regain ‘equilibrium’ is with a price change. As we alluded to above, no market is completely liquid, so an order (especially large ones) will move the price as it eats into the opposite side’s orders. This price impact is often only temporary and will revert as liquidity returns to the market.
Another reason for price impact, and perhaps more important, is informational cues. A large block trade attracts the attention of other traders because they may believe it’s motivated by new information that the trader possesses. This portion of the price impact may not be temporary.
However, some assets and markets do quickly adjust and revert price impact after block trades. As Damodaran states:
An early study* examined the speed of the price reaction by looking at the returns an investor could make by buying stock right around the block trade and selling later. They estimated the returns after transactions as a function of how many minutes after the block trade you traded, and found that only trades made within a minute of the block trade had a chance of making excess returns. Put another way, prices adjusted to the liquidity effects of the block trade within five minutes of the block.
4. Opportunity Cost
Opportunity cost is an important concept in finance, but in this sense, we mean it in a specific context: the cost associated with waiting to trade. While patience may reduce bid-ask spread and price impact components, waiting can be costly on trades that would have been profitable if made immediately, but which were rendered unprofitable due to waiting.
Determinants of Bid-Ask Spread and Price Impact
First of all, note that the bid-ask spread and price impact in $ terms don’t mean much on its own. What traders care about is the spread or impact as a percentage of the asset price.
Looking at public equities, bid-ask spreads have a negative correlation to:
- Price level
- Market cap
- Volume
- Number of market makers
When one of these goes up, bid-ask spreads should compress.
Conversely, bid-ask spreads have positive correlation with volatility.
The same relationships hold for price impact. The above is quite intuitive and meshes with our baseline view of what affects liquidity.
The price impact and the bid-ask spread are both a function of the liquidity of the market. The inventory costs and adverse selection problems are likely to be largest for stocks where small trades can move the market significantly.
One interesting point which was less obvious to me, was that stocks where institutional activity increased significantly had a large increase in bid-ask spreads. This may reflect the perception of market makers that institutional investors tend to be more informed investors, and thus widened their spreads to protect themselves. However, institutions also bring increased trading volume, which should have a dampening effect on this phenomenon and could, on net, tighten spreads.
Of course, I’d like to tie things into how Loopring Protocol may help! As an open protocol, our ultimate goal is to allow any entity to create a decentralized exchange of their own. These exchanges can be plugged into a shared network layer, effectively unifying sources of liquidity. Any token trader can submit orders and find trading partners in all corners of the earth. In doing so, we hope to reduce all the costs of illiquidity listed above.
In our next edition of the learning series, we’ll stick with the theme of liquidity, and analyze some methods for quantifying the discount that comes with illiquid assets.
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