From bubble busting to short squeezes and back again - Business displacement meets speculative finance

The world turns differently in a risk-on displacement market. The pandemic has been disruptive to many businesses which creates huge displacements in valuation. Some firms may never return to their old profitability and market positioning. Others will rebound or may reinvent themselves. Given the economic spike down, revised economic growth can turn losers into winners. There will be new winners and losers, and many of  the old ways of extrapolating sales and earnings just will not work. 

Disruption and displacement lead to valuation uncertainty and one man's value play is another man's possible speculative bubble. Greater return dispersion will occur across and within industries. Hence, there can be extreme short interest in some names viewed as having excessive prices and huge flows into names that are expected to be set for a new lift-off. There can be inflows and short interest growing at the same time. This is a condition that cannot last. There will be a winner and loser as valuation is revealed. 

Yet, when short interest gets too large, the bubble hunter becomes the hunted, and we move to a short squeeze. What make sense for one short trader becomes irrational in the aggregate; a sign of crowded herd behavior. The excessive bubble crowd switches to the negativity crowd, and it becomes a race to see who has the deeper pockets and exposure staying power. Reality will be somewhere in the middle but overshoots will occur as excessive feedback loops continue to be the norm. 

For those on the sidelines, these localized extremes should be viewed as important warning signals. Little bubbles and extreme behavior at the individual market level may just tell us that the bigger asset class bubbles may quickly move to the forefront, albeit this talk has been going on for some time. With a speculative mindset, excessive and cheap capital, and the inexpensive marketability of financial products and brokerage, the potential for upside and downside overshoots is more likely. We now live in a fat-tail world. 

Nevertheless, there are ways to play the game better.

1. Know the rules of the game. At extremes, the rules will change or the obscure (fine print) rules will be invoked. See the margin changes brokerage restrictions of January.

2. Think through extreme scenarios. Extremes will occur so be ready through anticipating low probability events. Can bad stock move higher? Can markets overshoot fundamentals? Can expected risks be breached?

3. Be ready for both left and right tail events. Everyone has been talking about meltdowns, but in January we saw the opposite melt-ups. Can you live in a fat-tail world? How will you adjust to fat-tails? 

Correlation and cointegration - You need to think about both when looking at connections across assets

When thinking about cross-market relationships the go-to statistic is correlation. Discuss diversification and the go-to again is correlation, but correlation is just one tool that is often overused especially when looking at time series and trading relationships across markets. A deeper yet critical concept for any portfolio tool is cointegration which focuses on commonality across prices not returns. Cointegration is not a new concept and is actively used by many quant firms, but the key concepts are not usually discussed by most portfolio managers. Markets can be correlated but not cointegrated and cointegrated markets may not be correlated. 

The best analogy used for cointegration is the "The Drunk and Her Dog". The wandering of the owner and dog on their way home may seem individually random, but the two are connected on their walk with corrections if one moves too far away from the other. Prices that are cointegrated will not be free to wander like random variables.  Cointegration measures the long-term co-movement in prices. The time series of two assets can have high correlation, but one series can have an upward trend drift and thus not be cointegrated. If you are expected that the prices will move together or mean-revert you will be disappointed.   

Information on cointegration augments any hedging analysis beyond correlation. Cointegration describes how asset prices are tied together, their tendency for mean reversion, and whether there is a common stochastic trend. 

Two asset price series are cointegrated if there is a linear combination between the two assets which is stationary. What is applicable for two series can also be applicable for a set of asset prices. There can be a cointegrating vector which defines common trends that will makes the system of markets stationary. Cointegration analysis can lead to some form of error correction modeling as a representation of the link across markets which is critical when looking at systems of asset prices. 

If someone starts talking about trading based on correlation of assets and how markets may move together and does not mention anything about cointegration raise your risk antenna. They may not be giving you the whole story, or they may not have done all of their homework. In either case, ask more questions. 

Know your taxonomy and solve problems in finance

Taxonomy is critical component for fields like botany and zoology. The same should be said about investments, yet not enough time has been spent on the issue categorization. There are reasons for this lack of focus, but this should change as more data science work is applied to investments. No one says that categorization is easy but engaging in the process of finding groups with similarities will help with building any portfolio and generating diversification. In particular, unsupervised learning through tools like cluster analysis will help develop better thinking in this area. 

Investment management has generally taken a simple approach to taxonomy. For example, indices use size as a mechanism for characterization. There are large cap, mid-cap, and small cap indices. However, these size-dependent indices are often inefficient. The advances in defining other factors create different taxonomies. We now know that size may be a poor way of looking at categorizing stocks.

Another taxonomy is based on industry groups, yet cluster analysis and even simple correlation analysis shows that industry groups may not be a good way to bundle stocks. Firms, unlike plants or animals, can change their business group and many companies have characteristics of multiple industry groups. The same problem can be seen with country groups. The composition of one country index may be very different from another. Firm characteristics like are dynamic and my not be tied to risk.

Even asset class taxonomy may be fuzzy. How do you classify convertibles? What are the categories for fixed income? When does investment grade end and high yield begin if you look beyond ratings categories? A close look at commodities shows that many are not highly correlated.

A taxonomy based on factors beyond size are by their very construction stochastic. Stocks will fall in and out of a value or momentum category, and there are many factors that may have some excess returns for a period of time only to see them disappear. This does not even address the issue of stocks that may fall into multiple factor categories. 

At this point many will shake their head in frustration and state that any taxonomy in finance is flawed and without relevance. The concept of taxonomy is fluid and dynamic in finance, yet this offers an investment opportunity. 

Clustering can be used to find commonality across equities that are not seen through naming conventions. By using clustering as tool for groups, there can be more focused opportunity management. The power of cluster analysis is that there can be a greater depth of understanding than found with simple correlation analysis which is usually calculated is blunt linear calculation. Unsupervised learning techniques can offer a better way of categorizing and forming perhaps a better asset taxonomy. 

GameStop (GME) - A game changer? Focus on the rules of the game

 

I can live with risk as measured by the volatility of markets. What is harder to live with is the risk or uncertainty on the rules of the game or the structure of markets. While there has a focus on the wild moves in some equities attributed to retail herds, crowds, or swarms, there has been less attention to what is happening with the rules of the game. 

Investor should expect increases in margin for volatile stocks, futures and options. This is a rule of the game. You may not like it, it may come at the wrong time, and it may actually further increase market volatility, but it is part of the game. Cash has to be reserved for this contingency. However, what happens if some brokers restrict trading in specific names given their capital requirements. It is within their rights and it may be a prudent call to protect the business, yet if an investor does not have a contingency for this change, there will be a whole new level of business risk.

The rules of the game also mean there can be short squeezes. If short interest gets so large, normal dynamics will be adjusted to account one-sided behavior. Borrowing costs will increase. Long will account for their advantage by just not selling. 

Extreme behavior leads to extreme responses and like a car that begins to skid a quick response may be the real problem and feedback gets reinforced and accentuated. And, we have not even begun to see the response by regulators. 

Is a structural response necessary to these market moves? An immediate answer is yes. Markets cannot be driven to either meltdowns or melt-ups by a feedback loop of trading driven by non-fundamental excess whether it be from the long or short side. Uncertainty can be minimized by reducing ignorance and know how the market structure works and what rules may change.