Market inefficiency is situational

 

“Market inefficiency is mostly cyclical now. In the past it was structural.” - Howard Marks 

It used to be that smart investors could create an information advantage to beat market efficiency. Either information could be gathered faster and more efficiently, or information could be processed better than other investors in order to generate an edge.  Those days are mostly behind us. Market efficiency is a dynamic process. Investment edges are lost. Traders get smarter, buy better information, and use new technology. What was cutting edge becomes common place. 

This does not mean that information advantages cannot be created. It does mean that converting any piece of information into higher returns is hard work. Many structural advantages have been reduced; however, in the current environment market inefficiencies can be viewed as cyclical. The environment changes and market efficiency changes. This efficiency dynamic will especially occur during transitions between regimes.  

Inefficiencies will be abundant during periods of market stress and crises. Rationality will decline during stress. Changes in the business cycle will create dispersion of opinions and dislocations from efficiency.  Periods of transition increase hedging which may not be profit maximizing. Hence, there is market friction between trading groups and a decrease in market efficiency. 

Why bring this up now? Global reopening, greater dispersion in government policies, bond sell-offs, large Treasury auctions, and EM dislocations all create market environment changes that change the level of efficiency. The efficient unprofitable environment may be profitable tomorrow. Efficiency is not static. Market fluctuate between levels of efficiency and economic agents have different abilities to exploit inefficiencies.   

Currency Carry and Funding Risks - It's all about the plumbing

 

Currency carry trades are a core strategy for many investors, but the opportunity set will be affected by funding risks. Carry trades can be profitable but there needs to be funding to conduct the quasi-arbitrage of buying high yielding currencies and selling (funding) with low yielding currencies. The plumbing matters and if there is a risk to obtaining funds, trades can be undertaken, and markets cannot move to any sort of equilibrium. The link between currency carry and funding dislocations has become more apparent since the Great Financial Crisis. 

If you want to be involved with currency carry, you have to know and follow closely the plumbing of financial markets. These financing plumbing issues have become more apparent with repo financing is Treasuries, swaps spreads, and currency trades. 

A new paper focuses on financing risks and shows that the world has changed significantly since the Great Financial Crisis regardless of liquidity provided by central banks. See "Currency Carry Trades and Global Funding Risks" by Nissinen, Suominen Filipe. They measure funding constraints as the deviation from covered interest rate parity. Funding risk will be the standard deviation of dispersion away from covered interest rate parity. Figure 2 shows the deviations from covered interest rate parity. There is a large break in the time series at the GFC. Figure 3 shows the deviation from covered interest rate parity for a long/short portfolio, and figure 4 plots the funding risk. Funding risks are associated with the equity risks of banks who will providing funding for carry, the short or low interest leg. 

Funding constraints are costly. An increase in funding risk will increase the likelihood of currency crashes and reduce the correlation between long and short legs of a currency markets. Funding increases frictions and increase the likelihood of currencies delinking. Markets become segmented when volatility affects the plumbing of funding. Analyzing classic carry trades that create long/short portfolios based on ranking of yields in forward currency markets, the researchers find that funding risk will impact future carry returns. 

This work provides a nice addition to our knowledge of currency carry trading, but more importantly, this research focuses on institutional constraints and structure on trading strategy returns. Gains and losses are associated with plumbing. An adverse change to market structure will turn good strategies on their head independent of trader skill. Trader skill is enhanced through getting around constraints or avoiding plumbing problems.   

Moving beyond uncovered interest rate parity to uncovered return parity

The failure of uncovered interest rate parity is one of the most well-documented puzzles of international finance and the driver of currency carry trades. Carry is one of the main factors explaining cross-sectional currency returns along with trend and value; however, research has also focused on the link between currency and country asset returns in general, not just interest differentials. We have posted on differential equity returns as another factor explaining currency returns. See, Using equity returns to forecast cross-section currency returns.

Two economists with the Bank of Canada have developed a more general approach for measuring expected exchange rate changes through a concept they call uncovered return parity. See, Uncovered Return Parity: Equity Returns and Currency Returns. Under this model, uncovered interest rate parity is a special case of uncovered return parity. 

In this approach, the expected exchange rate change is related to the both the relative interest rate and equity returns. Both of these will impact currency flows and hedging. An exchange rate change will be a mixture of responses to both rate and equity return patterns. This more general approach will actually explain the impact of commodity shocks that can be seen from oil because oil price changes will impact equity returns for many countries. Commodity prices impact capital returns and flows. 

The authors test their uncovered return parity model through OLS regression and through a finite mixture model (FMM). While the coefficients are not always the same, the importance of the equity factor is clear when tested with the interest rate differential.   

For investors in currencies, looking at the relative trends in equity returns can support relative value trades in the currency space.  

Using equity returns to forecast cross-section currency returns

 

All markets are connected around the globe. These connections are based on price differentials and flows. Money will flow to those places where higher returns can be generated. If there are higher returns in the equity markets of one country over another, capital will flow to that country's equity market. The corresponding flows will impact currency markets because the flows will increase currency demand and drive exchange rates higher. These exchange rate adjustments will, of course, impact overall returns. A recent paper looked at the link between equity market returns and cross-sectional currency returns and finds a strong positive relationship. See "The Equity Differential Factor in Currency Markets" by Turkington and Yazdani in the Financial Analysts Journal second quarter 2020. 

Currency market seem to be predictable cross-sectionally with carry, trend, and valuations factors all generating positive returns for ranked long/short portfolios. The authors found that ranking 12 month equity returns can form an equity differential strategy similar to carry. The strategy is not based on buying the underlying equities but using equity returns as two possible signals. One, the higher equity returns may signal  higher economic growth. Two, the higher equity returns lead to higher relative demand which requires the purchase of the spot currency. Many, however, have viewed that causality moves from exchange rates to equity markets, but this study given the lag relationships shows the causality moving from equities to currencies. 

Using a long sample from 1990 through 2017, the authors find that the equity return differential does a very good job of creating currency portfolios that generate positive returns across a broad set of currency pairs in the G10. The portfolio is a netted set of currency pairs and not just a ranking approach for the 10 currencies against the dollar. A similar approach is used for carry, trend and value as a comparison. The equity differential actually generates the best return to risk ratio. While producing slightly less return versus carry, the equity differential portfolio does not generate the negative skew found in the carry factor portfolio.

Using equity return differentials can be another method of trading currencies and show the strong return and capital flow link across all financial markets. 

FX Carry Returns - Pricing in Skewness

 

FX carry has been a go-to strategy for currency traders, but it is not without risks. For many, holding high yielding currencies and shorting a low yielding basket will generate steady positive returns until there is a shock to the system and markets switch to a return free-fall. The downside risk can be extreme when there is a crisis and leads to the analogy of picking up nickels before a steamroller. 

Recent work has focused on measuring conditional skew in currency markets. See "Conditional Skewness in Currency Markets" by Alina Steshkova. She models the distribution of currencies as skew-t distributed and finds a strong negative relation between skew and interest differentials as well as real exchange rates. Currencies with high interest differentials or show high real exchange rates will exhibit negative skew. This result is applicable to both developed and emerging markets. Skew is time varying because the underlying factors associated with skew are time varying. 

What makes this work especially interesting is that conditional skewness can forecasts currency risk premia and is priced within carry trade portfolios. The cross-sectional carry returns can be explained by skew as a dominant characteristic especially for high interest rates even after accounting for the dollar as the reserve safe currency and volatility. 

The value from this information is significant if you want to trade currency carry. Beware of negative skew and if you think you are getting paid just for carry, you are wrong. This skew should be seen in currency options and if it is not priced in the market, there are opportunities to build portfolios that can either hedge carry risk or for options strategies that can create a slight return edge. 

Even for the simple carry strategies, knowing that return is being generated from taking skew risk is valuable information. There is no free lunch, but there is a menu for what you will get at lunch.   

Currency trading and business cycle dispersion - Buy strong growth and sell low growth in cross-sectional portfolio

 

Currency markets performance is much easier to explain when analyzed cross-sectionally rather than as a single pair. Positive portfolio gains can be achieved by building long/short portfolios based on momentum, carry, or value. For example, long high rates and short low rates, or long positive (short negative) momentum. 

In essence, investors are taking advantage of  dispersion across specific factors. A more primal relationship with currencies could be the dispersion in economic conditions, like growth. An exchange rate is a relative price so currency changes should be related to the relative behavior of macro fundamentals. A new paper shows that this is an exploitable relationship between the output gap of a country relative to the US and spot exchange rate changes. See  "Business cycles and Currency Returns" by Colacito, Riddiough, and Sarno. 

The intuition for this cross-section macro-fundamental idea is simple. Stronger relative growth should translate into greater demand for the exchange rate. This is consistent with signs associated with growth differences in fundamental exchange rate models. The authors use a unique approach of exploiting output gaps through deviations from growth trends. Measuring the output gap through any number of simple methods finds there is an increasing excess return function based on sorting from weak to strong economy currencies. Buy economic growth (positive output gap) and sell economic weakness. Cross-sectional analysis changes the emphasis of business cycle and macro fundamentals away from bilateral forecasting; however, this business cycle relationship also applies for time series analysis.  

What is most interesting about this simple result is that it is independent of interest rate differentials (carry) models. There is significant variation in output gaps even relative to interest rate differentials which suggest that sorting on business cycles generate unique risks.

The cumulative returns are positive, but there has been a change in behavior in the post-GFC period. Of course, the spread in output gaps were diminished in this period and there was a flight to dollar safety. Clearly, the QE period did not show the same annualized gains based on output gap sorting; however, if there is a normalization of different economic growth paths, that is, more dispersion, the business cycle may again be a source of cross-sectional and times series returns. 

Persuasion and dealing with different decision-makers - The Miller-Williams decision-making style matrix

 

Investment decision processes come in many forms. There is the lone wolf portfolio manager that has the ability to make decisions without input or approval from others. There will be constraints on his behavior but not explicit collaboration. The team or committee approach is often employed by larger money management firms that have longer investment horizons. This is more likely to occur with asset allocation decisions. Finally, there is the quantitative approach where a systematic model will generate the investment decisions. Of course, a committee or PM will likely oversee the decisions from the model. what is not often discussed with group decisions is the process of persuasion to achieve consensus. 

Any group decision requires persuasion. Unfortunately, building consensus requires understanding the minds of the other decision-makers in the room. This is a skill or a consideration not generally discussed.  Nevertheless, there has been interesting research on describing or classifying the decision-makers that will have to be sold to reach a conclusion. This classification scheme can be condensed in the Miller-Williams decision-making style matrix. 

Any good presenter should be aware of the different types of decision-makers in his audience; however, this means investment decisions will be driven by a narrative and not just the output from a model. 

The most common decision-maker is the follower at 36% of the Miller-Williams survey group total. Followers are looking for others to make a decision or focused on proven methods. They will not generally think outside the box. Controllers at 9% need to be just given the details in a very logical way. Thinkers at 11% need to be given lots of facts and theory to work through many alternative scenarios. They along with controllers are going to be more risk averse but will likely improve the depth of decision-making. Skeptics at 19% are not going to be open to new ideas and will be looking for creditability and endorsement from others. Charismatics represent 25% of most key decision-makers and will need simple visuals with a focus on results although they will show strong enthusiasm. They will ask whether this idea will make money, and can you provide an easy explanation? 

My guess is that many hedge funds will be dominated by one or two types of decision-makers but not likely followers or skeptics. Pension fund boards will likely be dominated by followers and skeptics, like the majority of decision-makers in the survey sample.

Anticipating the thinking of the audience for any decision approval is a critical skill in gaining support for ideas within a firm and for selling a strategy to clients. These are considerations that are not normally considered when just developing model results.  

Changing stock bond correlation and macro factors - what has to happen

 

An expected stock-bond correlation is my primary asset allocation worry and keeps me up at night. So much of portfolio construction has been driven by this negative relationship that a switch to a positive correlation will up-end conventional thinking from the last two decades. 

Simply put, the negative correlation has been the best diversifying hedge in the money management business. If there is a stock decline, hold bonds, get your carry and price appreciation and rest easy. It has been a better alternative than holding the median manager for many hedge fund strategies and it has been cheap diversification. However, the legs may be cut out of this hedge if we have a different inflation regime. There is no carry and if rates rise from inflation, there will be a clear return drag. This would end the benefit of risk parity strategies and end the core benefit of 60/40 base case for portfolio construction. 

A look at longer history shows this correlation has been positive for decades prior to 2000. There was less value with a safety premium during the higher inflation periods. 

The research on this topic has been increasing, so we should be able to anticipate and prepare for the new environment. A good research piece on the macro drivers of this correlation is "Macroeconomic determinants of the correlation between stocks and bonds' by Marcello Pericoli of the Banca D'Italia. This work first shows that there has been a clear and significant change in the stock-bond correlation from positive to negative based on changes in macro relationships. In the US, the growth inflation correlation moved from negative to positive and the real rate inflation correlation moved from positive to negative. This was not just a US macro pattern. A similar pattern occurred in Germany.  

The author breaks down the covariance between stocks and bonds into five components. Of course, the correlation between stocks and bonds will not be the same as the covariance. Higher stock and bond volatility drives the correlation to zero. Some of the components that drive the stock/bond correlation are unambiguous while others are not clear and are based on how inflation is perceived versus growth and real rates. 

First, there is an uncertainty channel associated with real rates which is positive. A change in real rates will have a positive impact on the discounting of both  stock and bond cash flows.  Second, expected inflation will have a negative impact on the co-movement of stocks and bonds. An increase in inflation will negatively affect nominal bond cash flows while stock cash flows will provide a hedge against inflation.  

The next three factor signs are harder to distinguish. They can be called the cash flow channel, the discount factor, and the portfolio rebalancing channel. If the economy is neutral to inflation shocks, then these co-movement is easy to measure. With the cash flow channel, the impact is based on the correlation between stock cash flows and inflation. This correlation is related to  supply and demand shocks and whether inflation is countercyclical or pro-cyclical. The discount factor channel relates to money illusion and whether investors use nominal discounting of real values. The rebalancing correlation is related to the co-movement between dividend yield and real rates and represents the switching around business cycles - negative during expansion and positive during recessions.  This will be associated with the equity risk premium.

What happened to the correlation between stocks and bonds in earlier decades and the current environment? The global economy has moved from a countercyclical expected inflation environment to a pro-cyclical expected inflation environment. This is noted by the big switch in the growth and inflation correlation. 

The stock/bond correlation is driven by uncertainty concerning expected inflation, real rates, the correlation between inflation, dividend and real rates, and equity risk premia. The long-term swings in this correlation are based on the inflation business cycle link, specifically, the link between future cash flows and inflation. A slowdown and higher inflation are not on the top of most minds, but an increase in real rates and growing inflation are occurring right now. 

Bond-stock correlations around the world - All have been moving higher

Treasuries have been a great diversifier for the US stock market. The same cannot be said for international bonds from developed markets or emerging market bonds. Treasuries have played a unique safety role not just for US stocks but also other stock markets around the world. Unfortunately, all of these correlations are moving higher so the bond diversification gain has been declining. 

A simple analysis provides the foundation for the safety argument. We looked at the following combination of ETFs, SPY as a stock benchmark, IEF as a Treasury bond benchmark, BWX as a developed market international bond index, and EMB as an emerging market bond index proxy. The correlations are based on one year of daily data. 

Exchange rate forecasts - We are not getting any smarter, use them at your own peril

Analysts have learned a lot about currency markets over the last few decades, yet exchange rate forecasters, on average, still cannot do better than the random walk model. Recent research which uses perhaps the most extensive database on forecast across currencies and over time has not found any improvement versus older survey and forecasting analysis. See "Analysts Forecasts and Currency Markets" by Florian Mair 

The Meese and Rogoff forecast puzzle continues. Macro fundamentals do not seem to help forecast exchange rates. The same hold for the Fama puzzle concerning the poor forecasting power of forward rates. We can talk about rational expectations, but the numbers still suggest biases. 

There are a number of possible explanations for these results and some research is in conflict, but the overall conclusion is that exchange rate forecasting is a game with few winners. Forecasts are negatively related to excess returns; however, the author finds that forecast dispersion is positively related to currency returns when added to a carry trading strategy. These forecasts also underperform for long/short portfolios relative to factor forecasts associated with carry, value and momentum. You can use these classic factor models as better alternatives to following the experts although the level of significance is not strong for differentiating between strategies.

The numbers on skill have not changed. You are not getting any value following the forecasts of other; however, if analysts are confused as measured by dispersion you are actually obtaining useful information. 

   

Discretionary versus quant systems - Can you tell the difference? - The "I, Robot" problem

Can you construct a test to determine whether a fund is discretionary or quantitative/systematic without asking the manager directly? There is much discussion about quants versus discretionary, but how can you tell the style without the manager making the declaration? 

On the one hand, this is a distinction that should not be relevant. Performance success is all that matters, yet there is so much discussion between the relative value of quants and discretionary managers that this choice should be thought through in more detail. At a high level, what makes a systematic fund special? 

The Turing test has been pondered by many; can a computer pass for a human? How could someone tell the difference between a computer and human managing money through a set of questions? 

This question has been a repeated issue in science fiction writing like Isaac Asimov's classic "I, Robot". Can we separate the discretionary manager from the quant robot? Put differently, how can you tell the difference between a human and robot running a portfolio?

Can we distinguish human management and discretion by their mistakes? Humans will have behavioral biases. Robots can be programmed to eliminate these biases. However, robot managers can still make errors and have biases. Robots may will only use information programmed to assess. Robots may have a more difficult time adapting to changes in market regimes, but humans may also have the same problem.   

This question gets harder as we add machine learning programs to the quant field. With machine learning, a computer program is able to adapt and learn. This could be closer to the discretionary manager. A discretionary manager may be able to see new linkages between data and performance, yet big data and data science is trying to do the same thing. 

Is the key feature of a discretionary manager the ability to be creative? is the key fault the ability to make mistakes? Is the key feature of a quant system the assessment of large amounts of data? Is the key fault with inflexible behavior when faced with change? Is there a simple answer to distinguishing between robot and human portfolio manager? 

Global financial uncertainty is a complex combination of country, region, and overall global market volatility

Global financial uncertainty is a critical component of global macro trading aa well as having a real economic effect. Higher volatility will impact correlations across markets and with time series as well as increasing the dispersion of returns and risk premia around the globe. Recent work on a new Global Financial Uncertainty index was completed by researchers at the central Bank of Finland, discussion paper 1.2021. It provides further insight on the dynamic impact of uncertain across markets. 

The researchers looked at realized daily volatility for stocks, bonds, and exchange rates for a large sample of countries to create one single uncertainty index. Note that this uncertainty measure actually is a cross-asset volatility index. The researchers use a dynamic hierarchical factor model to measure the relationships across regions and countries. This factorization can be used as an assessment of uncertainty on subcomponents such as a global, region, or country measure. 

This global financial uncertainty (GFU) index is correlated with periods of market stress; however, it is different or captures different risks than found in the financial cycle or in world industrial production variation. The combination of equity, bond, and exchange rate volatility provides a deeper measure of uncertainty than a single market volatility index. 

There is a clear measurable uncertainty multiplier whereby an increase in the uncertainty index will lead to further or deeper declines in economic activity. The authors also find that there are unique regional behaviors There is higher uncertainty associated with a set of countries that may face a common shock or are relatively open so there is a financial link or spillover from one country to another. 

It is also clear that all global shocks will not impact all countries the same way. Clearly, uncertainty shocks to large economies will have spillover effects to other parts of the world. A US or EU increase in uncertainty will spill-over to most other financial markets. Nonetheless, EM regions like Asia and LATAM may be sensitive to their regional shocks but will not be affected by increases in uncertainty that may happen in the EU. 

What happens to financial market volatility as measured by the GFU index may not be connected to other measures of uncertainty and risk. There is a high correlation between this multi-country and asset index and the VIX index at .86. However, the GFU is less correlated with US financial uncertainty and financial cycle index. A global index will capture regional and country dislocations, and the financial cycle is not the same and an aggregate volatility measure.  Additionally, the policy uncertainty indices that capture news headline measures of uncertainty are correlated with financial volatility measures. Still, uncertainty in the news is not always related to uncertainty in market prices. 

All of this work points to the importance of tracking global volatility and uncertainty if you want to trade across regions and markets. Trade cannot be effectively measured without continual tracking of cross-market uncertainty. 

Why are central bank statements so important? - Disclosing private information

It used to be easier to assess monetary policy; follow the rate change or surprise and work through the dynamics on markets. With extended low rates and a zero bound, the flexibility of central banks to adjust rates is limited, so investors have to look at small changes in the forward guidance or statements to make an assessment. Everything rides on the nuance of writing, forward guidance, comments by Fed officials and summary of economic projections (SEP). 

In this case, investors are looking for small changes in statements to assess when policy will change and provide some insight on the central bank's assessment of economic growth. If there is a statement that tells us something on expected growth that can be useful for changing growth assessments, investors may react positively on negative rate news. This is especially true if the central bank is a better forecaster than private economists. Their economic assessment is also more important about when or how the central bank will react since they have inside information on the central bank reaction function.  

An immediate reaction of the stock and bond markets may be sizable and last much longer if there is an implicit view on economic growth. Investors have to look at the disconnect between stock and bond markets to give insight on what the Fed is thinking. See "Deconstructing Monetary Policy Surprises— The Role of Information Shocks" in the American Economic Journal: Macroeconomics

Investors may not like reading Fed tea leaves, but it is all the more important when policy changes and choices are less frequent and rate signals are constrained. Slight changes in commentary will have important changes in distributions and in relative reaction across asset classes. Forward guidance however poor is all we have from a constrained Fed. 

From Von Clausewitz - Coup d'oeil - strategic intuition with a glance

 

Von Clausewitz, the great nineteenth century military strategist, thought deeply about decision making during the fog of war. Often the difference between success and failure on the battlefield was associated with quick and decisive decisions. He used the term "coup d'oeil", strategic intuition, the ability at a glance to make a decision as a necessary skill for a successful commander. More precisely, this intuition is the "Rapid discovery of a truth which to the ordinary mind is either not visible at all or only becomes so after long examination and reflection." Isn't that the skill of a good investor and trader?

However, intuition is often downplayed relative analytical skills with investment decision-making as if there is a choice between analytic and intuitive thinking. More recent models of intelligence and decision-making states that these two features, analysis and intuition, are actually tied together through the concept of "intelligent memory". 

The analytics are the development of memory and the condensing of facts and information while the intuition is the correct recall or arrangement of these memories. Instead of analytics and intuition being mutually exclusive, these two skills work closely together. Analytics is the use of rigor with preparing for decision-making while the intuition is the  creativity or use of this prepared information. Intuition is not something to be avoided or suppressed but a skill used to support decision making especially when time is critical. 

"Intuitive decision making is the act of reaching a conclusion that emphasizes pattern recognition based on knowledge, judgment, experience, education, intelligence, boldness, perception, and character." - William Duggan, Columbia university management professor, assessing the US Army Field Manual 5-0 and its advise for officer decision making in his work "Coup D'oeil: Strategic Intuition in Army Planning". Intuition helps quicken the process with finding a course of action (COA). 

A trader can do all of the analysis necessary of understanding the market situation, but then there needs a level of intuition to apply the analytics to specific course of investing action. Experience helps with taking action because similar situations have been seen in the past. There may be multiple courses of action; however, the experience which serves to focus intuition can reduce the choice set and allow of action at a glance.  

A key question is whether this intuition is a skill that can be learned, improved, or made systematic. In a more formal sense, this is similar to what Gary Klein, the natural decision-making guru, would say is a Recognition Primed Decision Model (RPDM). Recognition of key analytics or details can prime or focus decision-making. Experience can sort through a broad set of data to find the signal within noise. While some would say this is in conflict with Kahneman system 1 and system 2 thinking, in reality, intuition as a means of focus is an important path for appreciating how decisions are made in real life. 

While there may be some who have inherent skill at investing intuition, it is more likely that coup d'oeil is something that can be gained through repeated action and experience. As presented in the US Army manual, this is a skill that can be identified, processed, and learned. Yet, how can repeated action lead to improvement of intuition? How can this skill be applied to investment decision-making? 

For many quants, the coup d'oeil or glance is conducted by a computer. The analytics point to possible choices which are hard-wired into a course of action. There is nothing wrong with this choice framework, but a similar process can also be applied to discretionary decisions. 

I suggest that intuition for investing can improved through two processes that are applicable to all decision-making:

1. Logging decisions with after action review. Why was a decision taken?  What was the outcome?  Why did you get the decision right or wrong? Was it luck or within the bounds of what was expected? This process of measuring decision quality creates forced learning and the chance for improvement through a feedback loop.

2. Use analytic tools to shorten the decision preparation. If all of the data and information is readily available, more time can be spent on the decision that has to be made. You cannot see the opportunities at a glance if you have not prepared the field for observation. This marries analytics with intuition and allows for critical time to be spent on courses of action. 

For more on Clausewitz and investing see the following:

On the "fog of war" - No one gets this quote right, but the concept stands

Judgment And Experience - A Recipe For Success For Any Strategy

What is your focus? Schwerpunkt - the center of gravity for your investment efforts

A complex market is not the same as a complicated market - Know the difference

 

There is a difference between complicated and complex systems, and it is important to understand the distinction. Complicated markets are not the same as complex markets. You will have problems as an investor if you don't understand or confuse the difference. Investors should accept the idea that markets are generally complex and not just complicated. 

A complicated market may have many components or parts. It may be technically difficult to manage, but the components can be separated and dealt with in a systematic way. A car engine can be complicated, but it can be taken apart, rebuilt, and still work effectively. The parts work together to achieve an outcome. There is a linear and precise way that a complicated system can work. 

A complicated market can be understood by working through steps. For example, if the Fed lowers rates, it is clear what will happen to bond and stock markets and it will be clear how that rate change will work through lending channels. There may be many steps, but they can all be described and the reaction or outcome is known.   

So, what is a complex system? A complex system may not have a high degree of order or control. There is  not clear predictability. A complex system cannot be known with certainty. There is a level of ambiguity with how a complex system works. There can be competing explanation for how the system works. It is not a clear engineering problem. 

The Fed may lower rates, but we are not able to precisely tell you what will happen to markets. We may have a good idea for what will be the reaction, and we may have history to guide us, but there may be surprise responses on what will be any given reaction. 

Complicated versus complex market system 

When central banks thought of themselves as economic engineers that used optimal control methods. They failed with controlling economic behavior. We have many reasons for this failure but all fall within the problem of complexity. Many financial engineering solutions failed because of unindented consequences or changes in behavior to the system that could not be anticipated. Markets are complex not complicated. 

A complicated market can be understood and effectively managed. It is just a matter of gain the right knowledge. You hedge against ignorance. A complex market cannot be understood, so investors need downside hedges to protect from what is unanticipated because the market system cannot not be knowable. 

The new regime - "Money Debt Hangover" after the pandemic and lockdown

 

Economic and financial history can often be defined by simple regimes or environments that define large directional moves and biases in the financial markets. After the fact, these regimes seem very clear albeit there may be disagreement on what are the defining characteristics. These regimes cannot always be described in quantitative terms but are often characterized through a simple narrative. 

The "Great Moderation" which was signaled by inflation targeting was far from it given some of the crises during this period. The EMS, Mexican debt, and Asian debt crises, however, busted any idea of moderation for short periods. The period of Reaganomics and monetarism included the Latin American debt crisis, currency dislocations, the '87 crash, and the thrift crisis. 

Before the fact, these regimes may be discussed by market analysts in broad terms but are still ambiguous enough to not provide strong guidance to most investors. All of these regimes take time to develop. We are in one of those transition times and those who identify the environment first will be rewarded.  

The vaccination programs are working. Lockdowns and restrictions are being adjusted to the current situation. Fiscal and monetary policy have been used to offset the economic shock, so the old pandemic regime is ready for a full transition. Investors have been anticipating this event for some time although there have been bumps along the road, yet it is not clear what will be the reaction in markets.  

The global bond shock is an early sign of capital further adjusting to the transitions. Markets are forward-looking, so it should be expected that they get ahead of any new regime world. There are two core problem or questions for investors as they for form these forward-looking views. First, what are the economic recovery paths and is the only way these paths can be achieved is through monetary-fiscal support. At what point is a recovery sustainable enough to allow for the payment of the stimulus and the return to normalcy? China is ready to for the transition. The US may be preparing for transition. The EU may be behind with this transition. Second, what will be the reaction of markets to this recovery path? What is the a normalized equity or bond market? How much pain will be associated with transitioning to a normal market?  

Questions are associated with whether markets are ahead or behind the transition and are policy-makers ready or accepting of the new regime. Ask Fed officials and they will tell you that any "money debt hangover" is not supposed to begin for at least for another two years. Ask politicians and they will say the stimulus was needed to jump-start or speed the recovery. Markets may have a third view that does not agree with either. 

Are we in a bubble world? Some objective measures from the ETH Zurich Financial Crisis Observatory

 

The bubble narrative has often been overused especially if it is not backed-up with some numbers. There needs to be some objective way of stating that markets are in a bubble, or more importantly identify the markets that display bubble behavior. This is done through the Financial Crisis Observatory. 

This center uses a power law methodology to identify bubbles or extreme up and down moves across a number of asset class and large number of individual markets. The observatory tracks markets through time and provides a probability measure of "bubbleness". The model looks at four states: positive and negative bubbles as well as positive  and negative bubbles likely to reverse based on a value score. 

There currently is a clear bias to positive bubbles as would be expected given the large amount of liquidity in markets. The asset class focus is on commodities and stock indices. There is a bias given the smaller number of markets followed in these asset classes, but it provides some clear indicators of overcrowded markets and herding at this time versus past measurement. 

The FCO report is published once a month. It can provide focus on markets that seem especially extended although we have seen research that bubbles last longer than expected.

Some of our earlier posts on bubbles:

More on Bubbles - trends last longer than expected

All bubbles are not trouble

Bubbles everywhere if you know how to look for them

Volatility and price bubbles - What you need to know - Hard to say there is a strong relationship but too quiet is not good

Financial stress in US, advanced economies, and EM have normalized

 

Financial stress during the COVID crisis has been a developed world problem. In general, financial stress shocked markets in March and then saw a strong policy response which pulled stress back down to normal. Markets responded as expected to this change in stress. Down on the increases and then rallying on the reduction. 

There was not the same stress variation in EM markets as measured by the Office of Financial Research (OFR) indicators. The stress indices are a weighted average of market variables that are believed to represent stress in markets. Given the greater underlying variability in some of the EM indicators employed, the index will show less variability when it is normalized. 

The longer-term stress levels are shown below. The spike in stress was strong, but short in duration. This crisis certainty was not the same as stress dislocations during the GFC. Central banks and governments reacted faster and with more force than in 2008. 

By any measure, EM did not behave like the shock in 2008. EM equity returns have not been immune to DM stress, but the link seen during the GFC was more acute and the DM - EM equity and bond links are representing new history and not the same old spill-over to weaker economies. This stress delinking offers return and diversification opportunities.