Critique of Pure Liquidity
The extensions and implementations of general liquidity theory
The Axiom of Liquidity
In finance, liquidity refers to the ease with which an asset can be bought or sold. This traditional definition is lacking, and understates how critical measuring liquidity is — as evidenced by this blog, nearly everything we come across can be contextualized in relation to some philosophy of liquidity. The dictionary definition runs into the problem that, to an ordinary person who is not privy to this school of thought, it’s hard to explain what “liquidity” is without concrete, field-specific examples and definitions. In government, for example, “liquidity” can refer to the ability of the system to distribute resources according to demand. In a business context, “liquidity” can refer to the condition of its underlying properties (current and future revenues, assets and intellectual property, regime risk, etc.) in enabling current and future stability of the system. These sub-definitions give rise to programs like the PPP loan program, where the necessity to give businesses emergency funding to continue to operate was weighed against the probability of companies being given unnecessary operating liquidity and fraud. We have looked at liquidity in markets, underpinning trust, in the motivations behind the college system, and more. If we can define liquidity in a more sufficiently broad, yet clear manner, we can interpret novel situations with a consistent framework and in a reliable manner.
Definition 1: Liquidity is the friction between theory and reality
The motivation behind this definition is to underscore that the implementation of theoretical concepts necessarily cannot be seamless in the real world. Let’s take a simple example of a binary option that pays out $100 on a coin flip resulting in heads and $0 on tails. This option is clearly theoretically worth $50. But if I can’t find a single person to sell me this option at $50, what good is my theoretical calculation? If the best offer I can find is $48, the difference in cost can only be explained by liquidity. This definition neatly encompasses both models of theoretically valuing the option at what it’s worth and realistically valuing the option at what you can transact it for. Naturally, both models have to be taken into account when making a decision on whether to purchase the option — analyzing the potential purchase through the lens of liquidity allows you to arrive at a decision. Note that in financial situations, this doesn’t exactly equate to transactional costs, which can usually be concretely accounted for — the costs of liquidity arise from transaction sizing, immediacy demanded, and the nature and scale of the overall market itself, all of which are dynamic concerns of subjective importance and push decision-making in different ways.
Developing a liquidity-based decision-making framework seeks to draw out actions by constructing logical conclusions from defined, knowable concepts and verifying through reality, as opposed to formalizing and updating theories based on observing historical and incoming phenomena. Of course, we cannot stick to this too rigorously — the confidence in a liquidity-derived decision scales inversely with the abstractness and complexity of the event or phenomenon. Constructing a macro thesis while being rigorously bound to liquidity is likely impossible due to the number of assumptions you’d need to make and the lack of ability to verify most postulations. However, liquidity excels at microstructure-level application, and stacking these theories on top of one another gives us a more rational way of theorizing than pure speculation, and hopefully allows us to reduce the overfitting of our induction.
The Elements of Liquidity
In constructing liquidity as a system of thought, we lend its usage across many domains while somewhat muddling what “liquidity” clearly implies in each context it’s utilized in. However, we want to preserve the usage of the core term “liquidity”, as it would be laborious and unnecessarily complicated to create a new term for each type of domain-specific liquidity, and might lead to the misconception that liquidity across fields isn’t related or linked. As shown earlier, the colloquial use of “liquidity” in the context of finance as opposed to government differs in low-level elements, but implies the same high-level thinking.
Definition 2: In a domain-specific context, liquidity refers to any element in the set of considerations that arise during the potential implementation of a theory.
Liquidity theory is unique — it underpins traditional theory, but also has material concerns, serving as a sort of “bridge” between the two. While theories and decisions can be derived from liquidity, it requires some sort of postulation, no matter how small, to get the flywheel spinning. In markets, liquidity is borne from the economic theory of supply and demand. In government, liquidity arises from the ruler’s political theory. Even in law, liquidity concerns proliferate from the principle of due process embedded in the Constitution. Some considerations may overlap across domains — there is usually some monetary element involved — but liquidity as a concept is too broad to be able to explicitly define everything it encapsulates.
Lemma 2.1: When an outcome is said to “assume liquidity”, it insufficiently considers or is outright ignorant of liquidity.
It’s important to know when liquidity is assumed as making decisions while assuming liquidity can lead to a severe misfiring of intention and unintended consequences. Outcome here is utilized very liberally — something as simple as the product of multiplication can be an “outcome”. Let’s look at a simple example of how assuming liquidity in even minor outcomes can lead to catastrophic results. Utilizing the theory of market capitalization, we can take the current market price for an asset and multiply it by the number of the asset outstanding to imply a total valuation of the asset. Say, $50 per unit multiplied by 1 million units gives us a valuation of $50 million. Now, let’s say I control the supply of this asset — internally, I have an additional 10 million units that I can issue. Utilizing the current market price per unit, I value this stash at $500 million. Both calculations have assumed liquidity — the concern that the price quoted is not irrespective of size — and value the asset at a price without considering its depth. However, unaware of this, I decide that I’m going to borrow $500 million against my stash valued at $500 million, reasoning that since it’s fully backed by my calculations, it’s not risky. Everyone knows how this story ends — loan payments come due, the implied valuation is unable to be converted into tangible cash to anywhere near the same level, and I default. The power of liquidity-based decision making is that the risk of consequentially assuming liquidity is very low. However, simply being aware of liquidity is not enough — rationally, not every element in the set of liquidity considerations is equally important. To further give the framework nuance, we will need to incorporate some relative weighting.
The Branches of Liquidity
While we will refrain from defining liquidity in every possible context, we will address the low level usage of liquidity in a more concrete manner.
Definition 3: As a mechanism, liquidity refers to anything that has an effect on some or all of the elements from Definition 2
We creep closer to the usage of the dictionary definition of liquidity. This definition encompasses the utilization of liquidity in contexts such as “traders providing liquidity to the market”, which can be painstakingly read as “traders providing resting orders, robust price quotes, etc. to reduce the weight of the consideration of the friction between size and price.”
When our decision framework results in an action, it necessarily outputs a trajectory that incorporates and responds to liquidity considerations. Which considerations we prioritize alleviating the most will naturally vary — there is rarely a situation that is nontrivial yet simple enough to allow for a “correct” ordering of considerations. Our decision framework follows 2 principles:
Avoid assuming liquidity as much as possible
Reduce liquidity (as defined by Definition 1)
Principle 1 is pretty self explanatory. It ensures that we won’t handwave away concerns and just assume things will work. Principle 2 is our first real foray into the realm of practical considerations. To be of any use, the framework has to be reasonable in its aim and output actionable decisions in a timely fashion. We can divide the competing philosophies on principle 2 into two general branches: flooding and pinpointing.
Flooding is about starting with the widest, broadest surface level elements and working in. The idea is to prevent compounding liquidity issues by casting a wide net and treating all the small, more easily fixable considerations as quickly as possible. Once the cracks are filled, attention can be turned to fine-tuning an approach to address larger considerations. A good example of a “flooding” decision is the PPP loan program, where immediacy of liquidity provision was prioritized over preventing unnecessary and fraudulent loans from being made. By being relatively indiscriminate with liquidity provision, businesses that needed capital were quickly able to get it, and the compounding effects of businesses going under never really materalized. After the situation was stabilized, attention could be turned to clawing back fraudulent loans. The cost of liquidity was reflected in the amounts lost to fraud or that were forgiven.
Pinpointing, naturally, is the opposite approach. Starting from the core, most fundamental elements, the idea is to stabilize the basis of the system. The idea is to make sure liquidity issues that would blow a hole in the system are minimized. It concedes that smaller concerns will never be eradicated and wouldn’t be practical to emphasize addressing. A good example of a “pinpointing” decision is quantitative easing, where the Federal Reserve backstopped bond market liquidity through asset purchases and low interest rates to preserve the financial system. They reasoned that as long as the deepest debt markets were protected, the liquidity would “trickle up” by consequently impacting the lending freeze in large banks, which would allow companies to borrow with confidence, which would result in job and economic growth on an individual level. QE didn’t exactly work as intended — lending didn’t pick up as much as they thought it would, and market distortions arose, a classic case of “infinite liquidity” syndrome. However, this will be discussed later when we address diagnoses of issues with the framework’s output.
Naturally, most decisions use some blend of both of these branches of thinking. In future sections, we will look at smaller scale, personal applications, different industries, large-scale, less verifiable speculation, and more.
To be continued…