Behavioral finance literature as highbrow self-help books - So what?

It is a secret pleasure. I read self-help books. Now, I want to win friends and influence people, so I won't admit this at a cocktail party or preach how others can get better on a range of activities. Self-help works, sort of. I read my new self-help material and change for a while, but then I get back into some old habits until the next article or book. The behavior change was not hard-wired. 

Behavioral finance and decision biases books can be a form of self-help - a form of high-brow self-help. Most of the articles will focus on some bad cognitive activity which harms rational behavior. They will offer a description and an explanation for its existence. The research may also offer a solution for avoiding the problem. 

They tell us why we will go to perdition if we continue down this path. We are then shown how so many engage in this bad activity and then told how there are real costs. If it is a dry academic piece, the focus will be focused on description and measurement. If it is a practitioner's journey, there will be offers of how this activity can be stopped and how life will be better through just following a different direction.

After an investor hears about the solution to this behavioral bias, he is supposed to change his irrational ways never to go down this path to ruin only to find that like a new year's resolution, the backslide will occur in weeks or months. Another article will be published discussing another bias and the process of self-help will begin again. There is  a repetitive process of new knowledge or a review of old concepts coupled with a solution followed by a swearing never to engage in such activities only to see backsliding. Perhaps there is some improvement, and a few biases are fundamentally changed but human nature is usually faced with  lack of discipline. That is why many need trainers for exercise, coaches for skill development, and teachers for overseeing activities.

The solution is clear good behavior has to be hard-wired through rules. The key advantage of systematic investing is hard-wiring good behavior. Predictive models may come and go but structuring good behavior will always provide gains.

Commodity shock sensitivity - Look at stocks to usage ratio

Let's put the commodity super-cycle discussion aside and focus on the more immediate issue of declining stocks to usage ratios for soybeans and corn. When inventories are low, commodity markets get more sensitive to any demand shocks. There will be a scramble for soybeans and corn, and nearby prices will reflect the stock-out risk. When inventories are high and can be used as a buffer stock, any demand shock will be muted. 

Trends can extend and move well outside the norm for the last few years in low inventory markets. The longer term stocks to usage tables below tells the price story when inventories are low. A price rally from current levels is not assured, but if you had to bet on a tail event, this is a good year. 

 

Commodities - Positive market shocks abound

 

Top commodity headlines from Wall Street Journal this year. Headlines are supposed to be eye-catching, but do you see some common themes?

US Natural Gas Shortage ...  2/18/21

Container Shortage  ... 2/18/21

Platinum Jumps ... 2/12/21

Diamond Prices Regain Their Sparkle 2/9/21

Wood-Pulp Prices Surge...  2/21/21

Silver Surges... 2/1/21

Palm Oil Market Soars ... 2/21/21

Stimulus Adds Fuel to Copper Rally ... 1/15/21

Shrinking Grain Supplies Send Prices Soaring ... 1/13/21

Saudi Arabia to Cut Production ... 1/5/21

Cotton Prices rise ... 1/4/21

While the focus has been on increasing equity prices, there are significant price increases in commodities. The chart shows some selected commodity moves over the last three months which actually dampens the shocks seen in 2021. The magnitudes of these price three-month changes are conservative relative to shorter-term price increases and less traded markets. For example, lumber futures prices are up 70 percent in the last three months.  

Some of these commodity moves have been demand related and others have been associated with supply shocks. The biggest moves as should be expected come from demand shocks with a supply disruption like natural gas. Many of these shocks will be corrected. It is in the nature of commodity price movements to see self-correction, yet commodity markets, in general, are moving away from a morbid period associated with a down super-cycle to a market where imbalances are more common. While we can point to market specific reasons for these increases, these gains are becoming more common.

While passive long-only commodity investing is usually a fool's game when markets have not been in backwardation, there are clear commodity opportunities that have fundamental stories that are often missing with many current equity plays.   

Irrationality and ticker confusion - We are not getting smarter

 

"nobody ever went broke underestimating the intelligence of the American people..." widely attributed to H.L. Mencken

I, like many, thought that with the behavioral finance revolution investors would get smarter and  decision biases and mistakes would diminish. Sorry to say, markets don't work that way. Those investors that are irrational may be weeded from the markets as their profits are eroded, but the pockets of those that are irrational can be deep, and there will often be a new set of irrational investors who will take their place. Do  not underestimate the irrationality of investors. 

Some biases will unlikely leave us. It may be hard for investors to hang onto winners and sell losers but making mistakes like buying the wrong stock based on not matching the company and ticker seems to be an easy problem that should be solved. Of course, there will be those who get it wrong, but it should be assumed that these traders will never have enough buying power to create a big market move, wrong. 

We are not ever talking about GameStop (GME) type of speculative buying. The errors we are discussing are with buying a stock that has a close name to another and will jump on news or comments unassociated with the real firm having news. For example, look at the case of Zoom Video Communication. It has the ticker (ZM). However, there is another unrelated company called Zoom Technologies that had the ticker (ZOOM) but has recently changed its ticker to (ZTNO). It jumped over 1500 percent last year based on investors making a mistake on ZOOM being ZM. The ticker changed and the price has normalized. 

Earlier this year when Elon Musk mentioned Signal as an alternative to Facebook, the stock Signal Advance SGNL, a firm in the biotech field, jumped from .60 to over 38. You have got to be kidding! Buy now and think later?  

The sad issue is that this is not just exuberance in the last year. This irrationality and investment mistakes have been happening all the time. When there is new information and high turnover, mistakes are made. 

A recent paper actually matched similar tickers that were different by one letter, had a reversed letter, or has similar names. They then looked at news shocks for the larger cap or more liquid alternative of a different named pairs and found that there was a significant spillover effect, positive correlation and co-movement in trading turnover with the potential mistaken pair. When there is surprise new information, confusion grows, trading turnover increases, and mistaken trades are made. The incidence of these trading confusion events is more likely that most would think, and it creates a sharp increase in trading turnover. When the cause of confusion disappears, trading becomes normalized. See "How Much So Investors Trade Because of Name/Ticker Confusion?" 

Slightly smarter investors may actually make money by playing off these ticker mistakes. Investors are not getting smarter and with a greater influx of new retail traders, it is not clear that investors are going to get smarter in the near future. Yes, many retail traders are doing their homework, but the odds are still with HL Mencken.     

Data preprocessing - Getting the foundations right is important for the return factory

Hedge fund return generation is like a production process or factory is an apt analogy of what goes on inside the firm. This is especially true of quant firms where there are well-defined repetitive tasks. The quant return factory differs from a discretionary artisan stock-picker. Using this analogy requires investors to dive into the production line and start with data preprocessing. The trader thinking like a factory manager should focus his attention on key data bottlenecks and the inference engine for decisions. For the investor, differentiating managers requires thinking through the factory production line.

See our past writing on the factory narrative:

• MiFID and managers as return factories - For whom is it an issue
• The hedge fund as a system - A different way of thinking about due diligence
• Information, meaning, and action - the components of asset management
• Be like Honda - Portfolio management as a production process

For the production process, the inputs are critical for any good output. If the quality of data is poor, it does not matter what is the level of model sophistication employed. If the preprocessing of data takes too long or not done correctly, there will be a "garbage in" problem. 

Some the key components of the production process that should be considered:

Data collection - 

What is the source of the data? Is there an alternative that provides a check on prices and scrubs bad data? Most would be surprised at the fact that even price data will have problems.

For fundamental data, there are always exceptions and outliers that have to be addressed, reviewed, and replaced. For macro managers, the problem of economic revisions, and getting announcement data is a problem that can be overcome with effort. 

The tilt to new data requires a new scrubbing process because checking can be difficult. Additionally, there has to be an assessment as whether new data are orthogonal or unique relative to existing data.

Data preprocessing - 

What is the form of data to be used for analysis? Take the simple case of market volatility. What is the right number for volatility? There is historical data with different look-back periods, implied, and calculated numbers that may include open, high, low, and close. Choices have to be made and numbers calculated and stored. Decisions have to be made for whether some calculations are made and stored before inputted into models.  

Data categorization  -

Data has to be categorized. For the macro category, there is the issue of using government data collected with some delay, and survey data which may have less delay. Different data have different sensitivities to assets so it may have to be categorized differently. Data are also structured as set dates which can be directly incorporated into a process while other data may come as a surprise because it was not pre-announced. 

Production process - 

All data has to be structured to be included in models at the right time and through a process that can lead to timely output that matches the decision time period. 

There are many ways of managing a production process. Both manager and investor should review on a periodic basis to ensure that data management and analysis is done efficiently.  

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.