Inflation expectations term structure - What are forecasters thinking when we form an inflation curve?

 

There are a number of ways to look at the term structure of inflation expectations. One can look at inflation swaps, break-evens across the Treasury curve, or calculate expectations from survey forecasts. Many of these methods are plagued with technical issues or do not make term structure calculations easy. The Philadelphia Fed has made this process easier with their inflationary expectations term structure model (ATSIX) that converts survey information into a more usable form. 

The ATSIX curve can be subtracted from nominal rates to create a real interest rate series. The ATSIX curve can be decomposed into a level, slope and curvature component no different than a yield curve.

Note that the last monthly update shows a shift up in inflation and an inversion of the expectations curve. All of the expectations are above the 2% target.   

Downside protection, tail risk and timing - Sorry, the path of equities will have huge impact on costs and benefits

Tracking the CBOE put protection index (PPUT) against the SPX provides useful information on the cost and timing of hedging. The PPUT index tracks a 5% out of the money one-month put strategy rolled monthly. There is a clear drag with performance versus the index which should be expected given the strong equity returns until the March 2020 crisis. At that time, the value of the put protected equity exposure downside. The five year total return through mid-April 2021 was superior to an unhedged portfolio.

Nonetheless, the five year performance does not tell the full story if an investor decided to employ a put hedge strategy starting on April 1, 2020. In that case, there is a strong performance drag.

Hedging after the horse has left the barn will not give you the return profile received from holding the strategy during a financial crisis. Of course, this analysis is based on a single performance path. If equities did not have the strong positive run the cost of hedging would have been significantly less or even produce a gain. Unfortunately, hedging costs and benefits can diverge wildly from expectations. 

Thinking About the Proprietary Data Edge - Creating, Manipulating, and Applying

How do managers create an edge? Many will say it is associated with skill; however, that does not provide any insight. A deeper explanation is that an edge is created from proprietary data, yet there are no clear definitions or framework for how we should we think about proprietary data? 

There are three broad means of creating and using proprietary data:

1. New data / unused data - Investors will often mean alternative, new, or unused data. If there is data that others don't have, there can be an edge. If previous unavailable data is used to answer a question more quickly, it can be viewed as alternative data. If there is data that is available but not well-known, it can be an alternative data source. Of course, the edge issue is whether the alternative data is actually correlated with future prices.   

2. Manipulation of existing data - A second set of proprietary data is existing information that is manipulated in new and different ways. It could be z-scoring data or creating a ratio between two data sets. There can be value in looking at the same data differently than convention.

3. The application of data to markets - Once found, data have to be mapped or linked to market returns. There is an edge with the techniques used to relate data to a forecast. For example, the link between macro data and bond returns can be measured through linear regression or through some form of machine learning. A non-linear technique may find a relationship that may not be displayed with a linear model.

4. The use of data to make decision - Finally, there is an edge created when the manager acts on the data or converts information to action. For example, there can be two trend-followers who use similar models but generate different returns because the map between data, forecast, and action is different.

Managers should walk through their use of data to determine whether there is an information edge. Investors who select managers should try and determine what it is that the manager does with data that creates an edge. 

Break-evens vs real rate changes tell us about market regime

 

Where are we in the economic cycle? Where have we been? Where are we going? Simple questions that often don't have easy answers. A simple approach is to look at the 2x2 chart relating 10-year Treasury real yields and change in the breakeven inflation rate. During economic expansion real yields should increase. In a recession, real yield will decline from slack as well as looser monetary policy. In an improving (deteriorating) or expansionary (recession) economy, inflationary expectations should be increasing (decreasing). Combining these two market-based economic measures creates a simple 2-dimensional matrix. 

If real rates and inflation break-evens are rising, the economic should be in strong expansion. If real rates and inflation expectations are both falling, the economy is likely in a recession. The II and IV quadrants of the 2x2 diagram represent transitioning environments; improving or tapering. Tracking this combination over the last 5 years using 3-month changes for inflation breakevens and 3-month averages for real rates shows how the economy has moved from recession to crossing into expansion. 

The expansion phase tells investors it is still worth holding risky assets such as equity growth and reducing exposure in safe assets such as bonds.  

History is needed to do good finance

 

“There can be few fields of human endeavor in which history counts for so little as in the world of finance. Past experience, to the extent that it is part of memory at all, is dismissed as the primitive refuge of those who do not have the insight to appreciate the incredible wonders of the present”. - Galbraith

There is no history without memory and there is limited memory with most investors. Yet, finance is a study of history. All theory and analysis are based on reviewing and manipulating the past to make judgments on the future. There are no financial experiments that can be run in a laboratory. There is no controlled environment. All we can do is study past data and look for generalization that may help us understand something about the future.

When we only look at a few years of data, we are closing our minds to history. Old data have to be reviewed in the context of the time; however, the past provides the fundamental insight that behavior often does not change. We repeat good behavior and bad mistakes. 

Our knowledge of known events may improve over time because we can use today as a context for yesterday. Better understanding of yesterdays will help explain tomorrows. 

The mechanics of market efficiency (MOME) separate the academic and practitioner

Market practitioners or realists will often sneer at the academics who study market efficiency. It is not because the practitioners believe markets are completely inefficient or there is easy money to be made; rather they have a different mindset of how they look at markets. The theorist looks for generalization and simplification while the practitioner is likely a structuralist who is focused on the plumbing of markets. Market plumbing focuses on the uniqueness of institutions and not generalization. There is money to be made by focusing on the details that may offer return. 

The practitioner out of necessity has to be an institutionalist that has to sweat the details of markets. The academic would like to avoid the uniqueness of markets and eliminate the small details that are market specific wherever possible. It is not that academics do not want to understand market details, but it does not often help with developing a general theory. Of course, there will be a conflict between these two views of the world given the difference in underlying assumptions. 

The practitioner has to worry about:

• transaction and operational costs
• liquidity costs  
• leverage, margin, and capital costs 
• the changing effects of fundamentals 
• the impact of surprises 
• the impact of noise traders
• crowdedness and the behavior of other traders 

The theorist may address these issues yet is looking for a framework that can be generalized across asset classes and not focusing on the mechanics that cause frictions. In markets that are highly efficient and competitive, the value-added comes from finding and exploiting small differences in market structure. Mechanics matter. 

K-shaped recovery for real and financial markets - A world of dispersion

As we examine the COVID economic rebound and reflation more closely, it has more characteristics of what some have called a K-shaped recovery rather than one that resembles the lifting of all boats. There are many lettered variation on recovery, but K-shaped seems to best fit the environment. We are not dealing with a classic inventory build recession or a banking crisis recession. A recession is always a time for dramatic economic change and technological upheaval as new methods for enhancing productivity are employed out of necessity; however, the relative impact of the COVID pandemic may make this recession more extreme.

The pandemic has changed both buying habits and the use technology and can be viewed as relative allocation adjustments associated with changing tastes. Taste and behavior adjustments will be more disruptive of the status quo which will create the divergent K-shaped recovery. Given this type of multi-dimensional recovery, it is unlikely that blunt instrument policies will work to help those on the bottom-end of the K and may lead to excessive on the top leg of the K. 

A K-shaped recovery means that some industries and groups will see a strong rebound beyond pre-COVID growth while others will suffer from a slow rebound or be hit with the costs of economic failure and have no opportunity for renewal. There are long-term winners and losers, and the job of an investor is to find the winners and avoid those losers. Policy-makers may desire to contain the losses and temper these excesses while allowing the macro adjustments to realign resources in this new world, but policy-makers cannot change the tide of taste changes.  

It is not clear that all businesses should be supported, or all job protected, but policies should attempt to focus on the bottom leg of the K to manage this transition. First, anything that supports stemming COVID infections supports the bottom leg of the K in order to minimize the cost of underproduction and underuse of labor. Second, lowering rates to support speculation without affecting the credit channels for lending to small businesses only helps the upper half of the K. Additionally, excessively low rates will also lead to over-valuation and excessive demand for the "good" firms versus "bad" firms. The result is a wider dispersion in valuation as seen in the graph below. There is no value judgment here. It is a current reality.  

The K-shaped recovery, a wide dispersion recovery, has already shown significant rotation among industries. Tech growth was a primary driver to stock returns prior to November only to see a rotation to value and more out of favor industries over the last 4 months. The vaccine-driven recovery will likely continue with the value gain; however, those firms driven by taste shifts will start to fall behind a create a success and failure return gap that will be skewed by companies that may not be survive. Current focus should pivot to finding the survival and failure portfolios as we deal with the time beyond initial recovery.

Crowdsourced forecasting methods - Should be applied to finance

 

Crowdsourced forecasting methods are being tested and assessed by the intelligence agencies and the US government to help forecast some thorny issues in geopolitical analysis. This work is still in its infancy and is not accepted by everyone, but the results from testing suggest that this can be an important alternative tool for forecasting under uncertainty.

There has been a growing interest in prediction polls, forecasting competitions, prediction markets, and different forms of  crowdsourcing to help with forecasts, but these tools have often been ad hoc and have not been integrated as part of any normal forecasting process with organizations. The premise for crowdsourced forecasts, however, is simple and based on the concept that there is "wisdom of crowds". Now the potential for groupthink when crowds are used in present; nevertheless, significant evidence exists that a set of diverse independent opinions creates better forecasting outcomes from crowd aggregation. 

Think of the averaging of individuals from a crowd as a form of ensemble  modeling. Along with value from the averaging of diverse opinions, individuals can be tracked and compared to others in order to find individuals that have forecasting skill. Crowdsourced forecast can complement existing methods while also uncovering additional useful information on specific topics. 

Crowdsourced forecasting methods can uncover unique information if it is conducted through precise methods that  ask  for likelihood to specific events. The forecasts have to be falsifiable. For example, will the 10-year yield be above 2% by the end of 2021? Will the YOY CPI inflation be below 2% at the end of the year? The answer can be in a form of a probability that can be updated. This is the preferred method for posing questions over broad questions. 

There is limited value from asking general opinions on a topic. Asking question in the form of a probability of an event occurring can provide precision in thinking on uncertain events. Output from precise crowdsourcing forecasts leads to tracking over time as well as a means of assessing the  skill of predicting specific events. 

Forecasts that are falsifiable have been around for the longest time on Wall Street when analysts are asked about such things as the level of the stock market, interest rates, or GDP. This is the bread and butter of Wall Street forecasters, yet many tasks could use better crowdsourced precision. This precision is especially the case within investment committee structures. It is not just important to assess outside forecasters. Rather it is the inside forecasts where there is more value. For example, what is the likelihood that hedge fund manager X will exceed a 10% total return? Or, what is the likelihood that volatility (VIX) will exceed 25% at the end of the third quarter. Crowdsourcing techniques and analysis increase accountability to those involved in an investment process.  

See the recent report: "Keeping Score: A New Approach to Geopolitical Forecasting" from Perry world House (University of Pennsylvania) 

More on the value growth debate - It is situational

Some useful graphs on the value and growth issue from Shroders that may not answer all investor questions on the topic but provide some use insights to add to the debate. 

1. The value over growth story does not show a strong relationship with real rates of interest. The current correlation is high, but the majority of observations show real rate have almost a flat relationship with the value growth correlation.

2. Value will increase especially when there is a large increase in real rates or a strong decline in inflation expectations like we had in the last quarter. It is the change not the level that seems to have a greater impact which suggests a business cycle adjustment as a key driver.

3. The value and growth effect cannot be separated from industry or style characteristics. Value will switch between cyclical and defensive stocks. Right now, there is a surge in cyclical exposure in the value space. Again, this seems to be suggestive of a business cycle link.

4. Value will strengthen when there is a surge in EPS. In  this case, value significantly underperformed before the EPS inflection and has done better than normal in the post inflection period.   

 

Boom and Bust - A great book on bubbles which is useful today

 

Boom and Bust - A Global History of Financial Bubbles by William Quinn and John Turner should be added to the reading list of anyone who is interested in the history of financial bubbles and wants to have a framework to think about bubbles today. It is deeply researched, adds new insights about booms that have not been discussed in other books and has an easily applied  framework for describing all of the historic episodes presented. 

The authors use the metaphor of a fire triangle  has to describe the three key components of a bubble:  speculation that provides heat, fuel that comes from cheap credit, and oxygen which comes from marketability or the ability to allow a bubble investment to get in the hands of investors. Every bubble will be driven by these three components, speculation from those looking for quick gain, easy credit from low interest rates, and the marketability that creates the opportunity for further speculation by allowing new market participants to play the game. 

The researchers start with some of the oldest and well-known bubbles, yet also explain why tulipmania did not meet their criteria for a significant  boom and bust event. They present some interesting cases not often discussed in other bubble books like the LATAM boom and railroad mania in the first half of the 19th century. They also review the Australian land boom and bicycle mania in the latter half of the 1800's. Of course, Quinn and Turner cover the usual suspects of the 20th century: the roaring 20's, Japan bull market, the dot-com bust, the sub-prime mess, and the Chinese stock excesses. 

They do a particularly good job of discussing how government while not always a cause, can abet booms and bust from actions that may in some cases seem well-intended but actually support or encourage the bubble fire triangle. Greed, supported by self-interest from a number of different parties often leads to unintended consequences which usually ends with well-known results. 

I now use the fire triangle metaphor to looks at current booms and it helps as a framework to show that the present is once again just a repeat of the past. The markets and situations may change but the end will still be the same.

Crowdedness for divergent and convergent strategies - How can it be measured?

Crowding is increasingly important topic with discussion of factor strategy returns. Not all factor strategies will behave the same from increases in crowdedness. Greater crowdedness will affect divergent and convergent strategies differently in the short and long-run. We have discussed the concept of convergent and divergent strategies (see: The "3 x 5 index card" on "Divergent" and "Convergent" hedge fund strategies), but the paper, "The Impact of Crowding in Alternative Risk Premia Investing", by Nick Baltas takes a different perspective and explains how different strategies will be affected by increased interest, crowdedness. 

A momentum strategy does not have a fundamental anchor. It is mean-fleeing and will gain from divergences through continued trend following. Hence, new flows can be a self-reinforcing mechanism which can lead to destabilizing behavior and a potential crash. In this case, more new capital will actually increase returns in the short run but will lead to distorts in performance the longer a trend lasts. 

On the other hand, a convergent strategy like value investing has a natural anchor, the fundamental valuation. In this case, there is a self-correcting mechanism, the long should increase and short decrease to close the value gap. As more capital flows into a value strategy the value gap should be closed. 

In both cases the turnover from rebalancing tells us something about performance. In the case of momentum, a positive feedback strategy, lower turnover will have higher performance from reinforcing behavior. This will not exist for value strategies.

Now we could measure crowdedness through money flows, but this information is difficult to obtain. However, we can learn something about crowdedness by the pairwise correlation of assets in the long and short portfolios constructed to make momentum and value factor strategies. In the case of momentum, if there is more pairwise correlation, a sign that investors are holding the same portfolio and thus crowded, there should be lower returns over time. A low pairwise portfolio should have higher returns. The research finds this result across different asset classes. It also finds the opposite result for value portfolios; high pairwise correlation in a portfolio suggests similar behavior that will lead to self-correction.

Everyone thinking the same for a momentum portfolio will start well but ultimately end with a bad result. On the other hand, acting the same will close the value gap for names in a value portfolio which will lead to more turnover and new opportunities to again exploit value.   

 

Crowded futures market trade measurement and momentum, value and basis factor strategies - Watch speculators who are adding positions

 

No one wants to be in a crowded trade. You want to get in early, but when others rush in, you want to head to the exits. Yet, measuring crowdedness is not easy. Crowdedness, the excess capital or interest in specific trades, can vary through time and create time varying returns. 

Recent research has focused on the potential crowdedness of factor strategies for momentum, carry, and value in futures markets through tracking net trade flows for specific reporting groups. This offers a direct way of measuring speculative interest through the information collected in the CFTC's Commitment of Traders reports. See "Crowding and Factor Returns". 

The researchers find that when speculative non-commercial interest is high there is a negative predictive impact on expected factor strategy returns. Your speculative factor returns are better when there is less competitive from other speculators. 

The speculative positions are measured as the net non-commercial positions relative to open interest against the average net speculative positions over the last year. Higher crowding leads to lower subsequent excess futures returns. 

Variation in crowding can also be measured and is related to past returns, performance chasing, higher volatility, and the cost of arbitrage capital as measured by increases in repo or the TED spread. Speculators follow performance and will change positions with capital costs. 

Factor strategies can be created by ranking momentum returns for 26 commodity markets, value based on deviations from long-term mean prices, and carry (basis) constructed from the difference between the first and second nearby futures. Once factor returns have been calculated, the crowdedness measure across all markets can be used to sort factors between periods of high and low crowdedness. From this sort, a measure of conditional crowdedness can be constructed. Excess returns are higher during periods of low crowding. We show the momentum results below. Similar relationships are found for value and the basis. 

The cumulative return for a factor strategy conditional on crowding is compelling for momentum, value and basis trading. If you are going to trade even simple factor strategies, it is important to focus on whether other speculators are jumping into the markets. 

 

   

Ex ante ignorance and ex post knowledge - Uncertainty shifting

 

When thinking about GameStop, SPACS, leverage, inflation expectations, ESG, and fiscal policy infrastructure development to name a view 2021 topics, I find that I am often ex ante ignorant. I may not know as much as I should about these issues, but all of a sudden the topic becomes prominent in the news, so I have to gain quick knowledge. The required knowledge is not just digesting the news, but trying to understand the underlying causes and drivers. After the event, I may show ex post knowledge. Uncertainty has been resolved through gaining knowledge, but it is often too late to exploit this new knowledge. 

So, the answer is to get more knowledge before the events? That solution does not resolve the issue because we cannot know everything, and we cannot always forecast where the next crisis or focus of attention will occur. Knowledge is a scarce resource, and our attention also has to be allocated efficiently. Everything piece of news cannot be given the same weight because it may just be noise. 

What is required is a framework to increase the speed of adjustment for knowledge acquisition. There is actually two parts, a framework for attention and a framework for learning. 

We do have some simple tools for the attention problem. Let prices do the talking. Trend-following is form of recognition priming and attention focusing. I cannot know everything, so I will focus on those markets that are moving. 

Focused learning is a different problem. We have written on the ladder of causality which may be a helpful tool for thinking about linking information with price action.  We have also discussed the issue of surprises and learning. The simplest approach which we find useful is an attempt to explain some phenomenon to a colleague through answering some simple question: What is going on? Why is this happening now? Who does it affect? How will markets react? This is purposeful learning. If you cannot explain, you need more study and avoid getting involved without further work. If you can answer those questions quickly and efficiently, it is possible to get ahead of the game. 

See:

Risk and uncertainty - The problem of closing the knowability gap

The ladder of causality -The basis for any investment analysis and AI

Breaking down surprises - the importance of learning

The Fed's TPLT framework, Temporary Price-level Targeting - Love or hate it, you need to understand it

 

"The Federal Reserve's New Framework and Outcome-Based Forward Guidance", a speech given today (4/14/21) by Fed Vice Chairman Clarida provides a simple and clear idea of how the Fed is thinking and describing their intellectual foundations or framework for current policy. As stated by Clarida, we are beyond the period of "copacetic coincidence".

According to Clarida, who has mentioned this three times in the last few months, we have an TPLT framework, temporary price-level targeting. The markets are anchoring inflation expectations below the 2% target because of ELB, an effective lower bound, so there has to be an overshoot to break the ELB problem.  

"As I highlighted in speeches at the Brookings Institution in November and the Hoover Institution in January, I believe that a useful way to summarize the framework defined by these five features is temporary price-level targeting (TPLT, at the ELB) that reverts to flexible inflation targeting (once the conditions for liftoff have been reached)."

We have to start thinking about monetary policy using the language and guidance that has been provided by the Fed. We can disagree with the success of this policy, and we may also be willing to trade against this view, but we have to accept that this is the framework that will be driving their internal discussions. The following are the five features that represent current Fed thinking.

First, the Committee expects to delay liftoff from the ELB until PCE inflation has risen to 2 percent and other complementary conditions, consistent with achieving this goal on a sustained basis, have also been met.

Second, with inflation having run persistently below 2 percent, the Committee will aim to achieve inflation moderately above 2 percent for some time in the service of keeping longer-term inflation expectations well anchored at the 2 percent longer-run goal.

Third, the Committee expects that appropriate monetary policy will remain accommodative for some time after the conditions to commence policy normalization have been met.

Fourth, policy will aim over time to return inflation to its longer-run goal, which remains 2 percent, but not below, once the conditions to commence policy normalization have been met.

Fifth, inflation that averages 2 percent over time represents an ex ante aspiration of the FOMC but not a time inconsistent ex post commitment.

An index of common inflation expectations - A helpful tool from the Fed

There are many inflation indicators of expectations, so any investor is faced with trying to determine which one is best or how to bundle these indicators into a single index. Some Fed economists have done the job through looking at an index of common inflation expectations which will be available on a quarterly basis. It should be noted that many of the Fed banks provide different inflation benchmarks including trimmed and  median CPI, inflation nowcasts. 

There are some flaws with tracking this index and it does not use any more information than other surveys, but it provides a simple measure of grouped inflation expectations that can provide another way of setting some benchmark. It is notable that there are significant differences in the correlations for inflation survey expectations. The current reading shows inflation expectations have not changed significantly and still seem anchored around 2 percent. 

 

Market efficiency - we have come a long way from the simple

"There is no other proposition in economics which has more solid empirical evidence supporting it than the Efficient Market Hypothesis"  - Michael Jensen in 1978. 

I came across this old quote from one of the leaders in finance research at the time - how prophetically wrong. Of course, making money is not easy because markets are generally efficient, but there is enough evidence to tell us that the world is more complex than our simple 70's view. 

There has been an explosion of research on behavioral finance, the limits of arbitrage, ideas of adaptive market efficiency, the ascent of risk premia modeling, new testing of efficiency, data science, high frequency trading,  and market microstructure. All have helped refine our thinking of efficiency. 

There is no settled science in investments where market behavior changes and adapts. Empirical evidence changes. Theories adapt. New tests are developed. The investment challenge is learning how to adapt to a dynamic non-linear world. We are often dealing with the same question but with different answers. The conclusion here is simple, take nothing for granted and continually questions conventional wisdom. 

Some thinking on market efficiency over the years: 

Market inefficiency is situational

Market Efficiency and Hard Thinking -

Markets: Efficient or Inefficient?

Momentum as the big embarrassment to market efficiency

Moneyball and Market Efficiency

Good and Bad Currency Carry Trades - We need more thinking on currency portfolio construction

Carry strategies are core to cross-sectional currency trading; buy the high-yielding currencies and short the low-yielding currencies. This strategy works but has been subject to negative skew and underperforms during crises or periods of high volatility. For researchers, the concept of currency carry is in conflict with uncovered interest rate parity and has been a puzzle which is only deepened without good pricing models to explain the risk premia. This puzzle has only gotten murkier with a paper called "Good Carry, Bad Carry". 

There are prototypical G10 carry trade currencies (AUD, CHF, and JPY) with JPY and CHF usually used as funding currencies with low rates and AUD often serving as a high yield currency. For the longest time, the sort of carry currencies would have showed same exposures for decades. A carry portfolio will always hold the rate differential extremes.  

However, it was found that if you exclude these three currencies from a G10 currency carry sort, you will get portfolios with higher Sharpe ratios and lower skew. Limiting the set currencies to exclude specific currencies will actually improve your portfolio efficiency. This is not supposed to be how a cross-sectional carry sort should work. Placing restrictions on currencies included in a portfolio will create good and bad currency mixes with the bad portfolios having lower Sharpe ratios and negative skew.  

The authors tested a number of variations for good and bad and get the same results. Hypotheses based on current carry thinking were tested for this anomaly but were not able to draw any definitive conclusions. Good carry portfolios reduce tail risk and have better return to risk. Investors need to think beyond simple sorts or look at risk through different criteria.

The researchers found that past carry predictors can explain bad carry portfolios but not the good carry portfolios. Good carry has unique return characteristics that are not driven by established thinking concerning carry. Bad carry is eroded by exchange rate changes while good carry is predominately driven by the yield differentials. Good carry is correlated with a dollar liquidity story since these portfolios always hold dollars risk but again this liquidity story is not a primary driver.

There are unique features with some currencies like JPY, CHF, and AUD that make them less carry attractive. Using a wider sample of currencies may not be helpful, and our understanding of currency carry needs further refinement. 

What you need to know about "crowdedness" risk and return

A Yogi Berraism: At Ft. Lauderdale Yogi was listening to his teammates talk about a restaurant in the area. Said Yogi, “Aw, nobody ever goes there. It’s too crowded.”

There always has been a significant interest in trade crowdedness, or more specifically, the big trades of hedge funds. However, there is a yin and yang with crowdedness. We want to know what smart money is doing so we can follow it, but we realize that if everyone is following the trades of others there will be a tipping point where the crowd will kill the golden goose trade. This is another way of saying the mix of buyers and sellers has an impact on the future direction of prices especially if there is a crisis. The structure or composition of funds and markets matters.  

Now crowdedness has not been well-defined, but a good measure is the numbers of days to liquidate equity positions held by hedge funds. Research has found that crowdedness as measured by the position-taking of hedge fund managers through the 13F filings will generate positive excess returns of approximately 300 bps versus stocks that are not crowded. (See "Crowded Trades and Tail Risk") 

This new crowdedness factor seems to be independent of other key equity risk factors. The collective wisdom or information edge of hedge fund managers along with their buying power seems to find stocks that will generate good positive relative returns. But there is a catch.

A close review of the crowded portfolios of hedge fund managers shows strong decline in returns or drawdowns when there is a negative market environment or crisis. Crowded trades do well until the market faces stress that may require liquidation. Investors will receive compensation for holding these crowded trades, but they will pay a price when there is a flight to the exits like during the GFC. Trying to piggy-back on the smartness of others can work, but when this collective smartness needs to find the exits, the market decline will be strong. The negative case is very specific, but it does suggest that following the action of others will have downside during a market unwind.

Geopolitical risks - The spillover to markets is real

 

Geopolitical threats create risk and uncertainty. This seems intuitive and can actually be measured through tracking geopolitical risk indices. These threats can be linked to market reactions, so investors can measure and act on these evolving risks. Below is the widely used Geopolitical threat index developed and updated by Matteo Iacoviello. It uses key word searches from leading newspapers around the world to measures geopolitical threats and acts. In many cases, elevated threats will impact financial markets.

A common theme of my investing thesis has been to focus on the nexus between risk, uncertainty and pricing. If uncertainty increases, it will carry over to market risk as measured by volatility. This increase in market risk will add to market dispersion, change correlations, affect risk aversion and sentiment, and change risk premia. Even if market prices don't move significantly, there will be a change in the wings of return distributions. 

Markets that engage in global trade in sensitive geopolitical area or have been perceived as a place of safety should be more sensitive to changes in these threats. Threats go up and there should be a flight to safety and a movement out of risky assets. 

A causal link from geopolitical threats spillover to oil price volatility and gold moves has been found with recent research. Similarly, threats influence the capital investment decisions of companies. This alternative data index can help with global macro decisions.

See recent research:

“Are geopolitical threats powerful enough to predict global oil

price volatility?” 

Environmental Science and Pollution Research https://doi.org/10.1007/s11356-021-12653-y

“Geopolitical Risk and Corporate Investment” Ruchith Dissanayake, Vikas Mehrotra, and Yanhui Wu

“Hedging geopolitical risk with precious metals” Dirk G.Baur and Lee A.Smales Journal of Banking & Finance Volume 117, August 2020,

“Forecasting realized gold volatility: Is there a role of geopolitical risks” Finance Research Letters Volume 35, July 2020

Margin credit - More complex than just saying it is rising

With the prime broker loses on levered positions, market talk has focused on overall stock margin; however, looking at some of the numbers suggests that the story is more complex than saying leverage is higher. 

We are certainly not arguing that the economy is not levered. The low interest environment is all you need to know. Cheap money will lead to greater credit usage. This is especially the case if the financial instruments being purchased are trending higher. Nevertheless, financial leverage is not the same as borrowing for long-term investment in plant and equipment where the measurement of uncertain future cash flows in an illiquid investment makes for a more difficult assessment.

The quick take:

1. Margin debt balances have increased significantly since the March 2020 crisis. Money is cheap and plentiful, and investors are taking advantage of the opportunity. 

2. The debt balances relative to the SPX market capitalization are increasing but the numbers are below the highs seen two years ago. Leverage has grown with the strong market, but the overall levels are not at extremes.

3. Free credit balances have grown from 2019 lows. There is money available to invest and it not as though all investor cash is being used to boost leverage. Margin accounts are getting the benefit of the rise in equities, so free cash levels have not seen excessive declines based on extreme speculative desires.

In the unregulated swaps markets, the world can be quite different, so any generalization on margin usage should be tempered with a fuller picture. In the regulated market, the leverage usage is more controlled.   

Sell-side research - You get what you pay for, no more, no less

 

Remember, all Sell-Side Research contains at least 1 of the following 3 elements

1.Trades that 40-Act Funds are running after serious traders/HFs stopped out.

2.Death Trap Trades where the bank’s desk needs to take the other side.

3. An honest opinion of an analyst.

Never forget, in life, even lies are intriguing and useful, they reveal where someone's interests are

- Matt Kessel

This view is an extreme, but there is truth in the words that sell-side forecasts and analysis can be biased, are driven by incentives, and have potential conflicts of interest albeit the biases are small as measured by numerous studies. There is also an element of dispersion in forecasts because the quality of analysts varies.

Honest opinions of experts can diverge between being poor and very effective. Academic and private researchers have studied analyst forecasts for decades and we do have a pretty good idea of the benefits and costs of research especially for equity markets. Additionally, there is enough analysis of sell-side forecasts to determine how to weigh this information to generate better forecasts. Don't use single forecasts blindly but employ the wisdom of crowds. Use the crowd estimates as a placeholder for market consensus. Agreement with the crowd may generate positive returns but will not create unique alpha.

In general, research finds that earnings and stock price forecasts may be slightly better than time series forecasts, but they may not be efficient and there may be biases over both under- and over-reaction to different market environments. Analysts have a hard time with turning points and change. Macro forecasts are biased and may not be efficient. There is limited edge gained from using sell-side information.

One of the more current issues with analyzing sell-side research is that the focus has been fairly narrow. There is a difference between reporting or producing analysis and forecasting price or earnings. The analysis comes first and may be more valuable. From analysis, there is an inference or a forecast. If the analysis is poor or assumptions are flawed, it is impossible to get the forecast right. The quality of sell-side research should be centered on the ability of the analyst to provide firm and industry information quickly, cheaply, and efficiently. Providing description of the market details in order to support investor analysis may be more valuable than the work of generating an earnings estimate, stock estimate, or point forecast.