Forced transactions, liquidity profiles and market ecology.

Foreword:

I do not claim any originality in the thoughts I present here, since I am woefully unaware of what knowledge is already available in the financial literature. However, I can say that what follows is the result of my own independent thinking coupled with the insights gleaned from great thinkers in the financial space.
Here I try to layout a generalised principle underlying various market phenomena.
I coin terminology here, not because I claim to have invented the concept, but merely because I need some way of referring to various concepts I describe here and am not familiar enough with financial literature to know whether or not a pre-existing term already exists.
I split this into 3 sections. The first 2 sections are rather short and go over defining two concepts. The final section undergoes how I see these 2 concepts interact and how it contributes to various market phenomena.
In sharing this, I hope to help other retail investors (Like myself) understand the powers at work in the market.
For the market veterans, I am not sure whether what I am about to share is exceedingly obvious or not.
It is my hope that through continued discourse, the ideas here can be challenged, refined and most importantly, tested, using proper quantitative and empirical methods.
If what I have written here has already been discussed, explained and tested; forgive me, I do not know any better as a lowly retail, non-finance major plebeian.

Forced transactions, liquidity profiles and market ecology.

Section 1: Forced transaction -

I must begin by defining this term, as I will refer to it frequently. Simply put, the forced transaction is a transaction that must be completed, irrespective of price.
Examples of such transactions follow:

Flows from passive indexation tracking a benchmark — As new funds enter an ETF, the ETF is required by mandate to purchase its constituent securities. These transactions must occur in order to ensure the proper weighting demanded by the ETF, independent of what price the transactions occur at.

Market maker hedging — As market makers take the other side of options positions, these positions must be hedged out with the appropriate delta-proportional longs or shorts. These delta-hedges must be put on, regardless of the price of transaction.

Section 2: Liquidity profiles -

Market participants supply liquidity that have characteristic profiles according to the participants strategy or mandate.
To illustrate, take what is commonly called the value investor.
The value investor offers bids when prices are a certain threshold below what they deem to be the intrinsic value of a security, and conversely offers asks when prices are above.
As a stylised example, take a value investor who deems an asset A to have an intrinsic value of $100 and is willing to buy it when it falls 30% from its intrinsic value or sell it when it rises 30% above its intrinsic value. The value investor supplies the market with liquidity in the form of bids at 70 USD and asks at 130 USD.
Obviously, different value investors may have different intrinsic values and different deviations away from the intrinsic value that they are willing to transact at. Important to note about this type of investor is that their liquidity profile (Given that they do not undergo style-drift) is stable and unchanging, regardless of whether the market is trending up or down.

As another stylised example, take a trend-follower, who is one that offers market bids when the market is trending up and offers market sells when the market is trending down.

The holder offers no bids or asks.

This is not intended to be a comprehensive list of the types of investors and their corresponding liquidity profiles. The key point to take away is that different market participants offer unique liquidity profiles which may or may not change according to market conditions. Each market participant is able to deploy a certain volume of capital/assets to their liquidity profile. The sum of the liquidity profiles of all market participants broadly makes up the liquidity of the market as a whole. Liquidity profiles need not be static, as illustrated by the trend follower, who change their liquidity profiles according to different factors.
In markets that we might dare to call “normal”, liquidity profiles might be expected to be continuous and smooth, which may be due to the presence of different participants, with comparable assets/capital to deploy, that each contribute their unique liquidity profile to the overall market. Even a market consisting solely of value investors could create a smooth liquidity profile, provided that there is a diversity in the intrinsic values calculated by each participant and a diversity in the deviation away from that intrinsic value that they are willing to transact at.

Section 3: Market ecology -

Forced transactions in an illiquid market; Shifts in volatility regimes.

The core idea that I wish to propose is that volatility regime shifts occur when a forced transaction occurs at a volume that overwhelms the liquidity profile of a market.

To illustrate this idea, consider the following situations:

Consider a market consisting solely of value investors, who are only willing to transact when an asset A is 30% above or below its intrinsic value. When a forced transaction occurs to buy the asset, the only price at which the asset can clear at is 30% above intrinsic value. Conversely, the only price that clears for a forced transaction to sell is at 70% of intrinsic value. In such a situation, the market shows extreme hysteresis, with the transacted price only being able to occur at those two values.

In a market consisting of solely holders, both buy and sell transactions are not able to be absorbed by any liquidity. Thus transaction prices tend towards zero or infinity.

In a market consisting of solely trend-followers in an up trending market, any order to sell is able to clear by being met by the willingness of trend-followers to buy. However, any order to buy is met with no matching offer to sell and thus transaction prices tend towards infinity.
Conversely, in a market that is down trending, any order to buy is able to clear but any forced order to sell is not met with a matching buy order, thus transacting prices tend towards zero.

These fictionalised scenarios demonstrate a basic mechanism at which price movements which we may deem to be “volatile” may occur.

A key variable to note is the volume that each liquidity profile contains.
Consider a market of 2 types of value investors and a trend follower. The 1st type of value investor holds 400 shares of stock A, has 400 shares worth in capital and is willing to transact when the price of stock deviates 15% from its intrinsic value (Let’s say 100 USD). The 2nd type holds 100 shares of stock A, holds 100 shares worth of capital and is willing to transact when the price of the stock deviates 80% from its intrinsic value (Let’s say 100 USD for this type of value investor as well). The trend follower is willing to provide buy offers at market price, provided that the market has been trending up.

If a forced transaction to buy 200 shares were to occur, the market would absorb this transaction at 115 USD.

However, if a forced transaction to buy 401 shares were to occur, the last transaction to clear would occur at 180 USD. The trend follower is able to offer bids but not offer sells as stock A has trended up.
If a forced transaction to sell were to occur, the trend follower is able to offer bids at 180 USD, ensuring prices do not fall, provided that the forced transaction does not overwhelm the capital that the trend follower can deploy.

With the following illustrations, I hope to demonstrate the following idea.

When capital/assets become overwhelmingly owned by participants with the same liquidity profile, it sets up the conditions for volatile movements in price, provided that a forced transaction occurs at a volume that overwhelms their liquidity profile.
Although this idea may be nearly impossible to test historically, I presume that it could be tested in silico, by modelling how prices movements occur in a simulated market with participants of a single liquidity profile, or mixed market participants where the assets/capital deployable by one type of investor overwhelms all others. The prediction I would propose is that markets with diverse participants (In terms of their liquidity profiles) that have comparable assets/capitals to deploy, would be less likely to volatile swings.

Forced transactions by themselves do not cause volatile regimes, when the market has liquidity profiles that can offer enough volume to absorb such transactions. An example of this could be when market makers are able to delta hedge without shifting the price violently.

It also important to stress that liquidity profiles need not be static, but can shift according to different strategies/mandates of different market participants (As illustrated by the example of the trend follower). Sudden changes in liquidity profiles may render a market, that hitherto was able to absorb forced transactions without shifts in volatility regimes, suddenly vulnerable.

By no means is this concept meant to explain all market phenomena, however, I believe that it may be a general principle that can help guide overall understanding of the markets.

Leverage -

Leverage falls into this framework by affecting capital/assets deployed for each market participant and introducing a source of forced transaction.
Leverage increases the contribution of a participants liquidity profile to the overall market liquidity profile, essentially by increasing volume of capital/assets deployed. When done in size, this can contribute to distorting the market liquidity profile, over-representing the liquidity profile of the market participant employing leverage.
Leverage introduces a possible source for a forced transaction, since margin-calls can force the participant to transact in a price insensitive manner.
Again, I believe this idea could be tested historically by observing the amount of leverage deployed in a market compared to the magnitude of price distributions that occur in such markets. One should expect a positive correlation between leverage and large price changes (Both size and frequency).
This idea could also further be tested in silico.

Market positioning -

It is well known that markets that become heavily positioned towards a certain strategy have a tendency to unwind in violent ways. I hope it is clear by now that such phenomena occur, in my framework, due to the concentration of capital/assets into a single liquidity profile. This necessarily brings irregularities, skews and/or discontinuities in the market liquidity profile, thus rendering it to shifts in volatility regimes if a forced transaction of sufficient volume were to occur.
Again, such an idea might be testable in silico using a simulation where the majority of market participants utilise a single strategy and then introducing a forced transaction, counter to the liquidity profile provided by those majority of market participants.

Gaussian distributions -

Price movements in regular markets are commonly accepted to follow Gaussian distributions and becoming pathological during shifts in volatility.
It is my contention that the distribution of price movements corresponds to the liquidity profile of a market.
Since I am not a financial major, I am not aware of what data is available, but if historical information regarding the order book is available, one may expect that the distribution of orders is somewhat Gaussian during normal regimes and becomes non-Gaussian during volatile regimes.

Transience of strategies -

Many market strategies that previously showed good performance have deteriorated over time. Such phenomena can be explained under this framework in the following manner.
Particular market liquidity profiles lend themselves to being exploited with specific strategies. However, the very act of trying to take advantage of such liquidity profiles ends up changing the overall market liquidity profile itself, as more assets/capital is deployed in pursuit of those strategies. This change in the liquidity profile means that the previous strategy is no longer able to work as the liquidity profile now lends itself to being taken advantage by a different type of strategy.

Short squeezes -

A short squeeze occurs when the liquidity profile cannot provide sell offers in sufficient volume, in response to a forced buy transaction. As the forced transactions increase the price, this creates new forced transactions, as other participants who are short are forced to cover their positions, but in a market that cannot provide the liquidity to the volume demanded.

Passive investing -

Passive investing introduces both a particular type of market participant (The holder, who does not transact) and a source of forced transactions. Increased proportions of market participants who only hold and do not transact, as previously explained, influence the liquidity profile in such a way that transacted prices tend towards zero or infinity as the market becomes more dominated by them, depending on the direction of the forced transaction. The passive fund also introduces forced transactions since when the passive fund receives inflows of money they are forced to transact to buy; conversely when redeemed they are forced to sell.

Final thought:

The market ecosystem —

Just as an ecosystem that becomes dominated by a single species eventually leads to the ecosystem deteriorating as a whole, so too I believe that a “healthy” market is one that contains a diversity of market participants.
As markets become heavily over-represented by one type of liquidity profile, a forced transaction occurring in sufficient volume is enough to destroy those market participants and thus fundamentally changing the overall market liquidity profile.
Markets with a plethora of participants contributing different liquidity profiles increases the chance that a degree of liquidity is provided in sufficient volume to withstand any forced transaction.

Acknowledgements:

These ideas were not born in a vacuum and drew on knowledge from many different sources.
To name a few:
Ernest Shelford
Cem Karsan
Michael Green
SqueezeMetrics
Hari Krishnan
Corey Hoffstein

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