Behavioral theories of factor risk premia - Hard to measure causes of under and overreaction

Some of the most important risk premia are a result of investor market behavior and risk aversion and not just structural risk issues that require investor compensation. Given the association between risk premia and behavior, these premia at times can be fleeting or at least time varying as behavior changes. 

Time variation of returns will be associated with the degree of error expectations and under or over reaction to the market environment. There are systematic errors in expectations which arise when the macro or market environment is changing. For example, greater uncertainty may cause investors to have greater under reaction to market events based on risk aversion to loss. One-sided flow of information can create overconfidence. 

Most of the major risk premia have both risk-based and errors-based stories that are used as explanations; however, behavior stories may be harder to manage because they will be related to mistakes that can be corrected. Unfortunately, we don't know when behavioral errors will be closed.  

Nevertheless, it is useful to look at risk premia conditional on the risk sentiment environment. Macro risk factors can be measured within a framework, but measuring behavioral factors has to be focused on sentiment and excesses that create biases. 

Debt sustainability - this issue can again become important in the post-recovery period

Debt levels have exploded around the global. The growth has been most dramatic in developed countries like the US, but this debt explosion has been occurring across most countries. In the case of the US, the central bank has been a strong buyer of Treasuries, so the debt is not being held by private investors. Debt has been exchanged for reserves. There is a growing view through Modern Monetary Theory (MMT) that the monetizing debt is not a problem and if it becomes a problem through higher inflation, it can be solved through quick reversal of policies. 

Nevertheless, all countries may not be able to engage in the current debt policy extremes of the US nor may the US at some point. For most countries, debt sustainability will be an economic problem if certain extremes are reached. In the pre-MMT world there was a clear focus on debt through the strong arguments in This Time is Different: Eight Centuries of Financial Folly by Reinhart and Rogoff. However, any current emphasis on austerity has been relegated to a policy closet. Still, it is important for investors to score countries on this critical issue and be aware that it can serve as a catalyst for market sell-offs in specific countries. 

Country debt sustainability can be measured through a scorecard. Here are some of the most commonly used factors for assessing debt vulnerability:

1. Debt/GDP - As the debt to GDP exceeds 100% there is greater likelihood of a slowdown in growth based on the cost of maintaining the debt.

2. Primary balance (government revenue - expenses and interest costs) - Sustained negative balances especially during periods of robust growth calls into question the ability to pay principle.

3. Interest rate versus GDP growth - When rates exceed GDP growth, the cost of debt will not be able to be maintained.

4. Weighted average maturity of debt - More short-term debt increases the risk of rolling over principle when the debt matures.

5. Interest/revenue ratio - An increasing ratio will mean that other government expenses will be crowded-out by interest payments.

These factors must be weighed against the governance of the country, policy uncertainty, and the overall demographics which affect ability to pay. The likelihood debt will be a problem is also affected by the financial stability of the country and the external imbalances current account imbalances and foreign indebtedness. 

While there may not be an immediate debt crisis, tracking country difference will pay-off. There are limits to country borrowing, and those limits, if reached, can lead to large currency declines, rising rates, and equity selloffs over a short time period.

Words of Estimative Probability (WEP) - Words matter and poor precision leads to decision failures

 

Conveying probabilities and uncertainty is difficult especially if numbers are not used. Word choice matters. Words have different meanings and there is the potential for a disconnect between the sender of messages and the receiver. When words are used to convey probabilities, there is significant room for error. 

The current Afghanistan situation may be a perfect example of the problem of conveying assessments in words and deciding based on those possibly ambiguous assessments. We don't know the exact information given or the debates that were held concerning risks in Afghanistan, but if those sitting around a conference table used words like "probably not" or "little chance", it is likely that these had a range of probabilities and a range of understanding. 

The ambiguity in word choice was a key finding of the CIA analyst Sherman Kent who first studied words of estimative probability (WEP). He tried to get analysts to be more precise in their language in order to minimize ambiguity. He conducted surveys and developed a mapping of words to likelihood. We have written about this extensively in the context of investment committees. For investments there is not the risk of life with word choices for assessments, but the cost for wrong interpretations is real. 

Sherman Kent - My guy for precision in forecasting language

Sherman Kent - the godfather of precision in forecasting language

Language, perception, and numbers - The translation problem

What does failed intelligence tell us about investing

"Likely" or "Probable" - there is a difference in language when talking about recommendations in the new year

Recent research has replicated the work of Kent and has found similar results. For the new update on this work, see the posting by Wade Fagen-Ulmschneider from the University of Illinois. While finding similarity with Kent's work is good, this research shows that there is still a range of meaning for these words. We present the comparison with Kent's work and the range of probabilities for different word choice. Notice that the tail or extreme events have the highest level of ambiguity.

Someone can leave a committee meeting and have a very different interpretation for the likelihood of a given event. There can still be a high degree of ambiguity. Should investment committee members be given a scorecard on the words that could be used? That may not be a crazy idea. The key point is to ask analysts and committee members to offer precision with their estimates. This is not about the second decimal point but exacting specific information on what is being predicted and offering precision with the chances of an event occurring.

Of course, using probabilities has its own set of issues. What does it really mean to say that there is a 10% probability that a government will fail? Of course, there needs to be a time frame, but what is a 1/10 chance in next year really mean? This is the problem of uncertainty and subjective probabilities. We can say with countable data that there was a 10% probability that rates will rise by 75 bps in six months based on historical data, but that is very different than saying that there is a 20% that tapering will be announced at the September FOMC meeting. Learn to be precise in your assessments and that means defining clearly what is being handicapped. 

Money, money everywhere but in the loan market

With the Fed still buying $120 billion in Treasuries and mortgages every month, short rates still close to zero and real rates solidly negative, the loan market should be exploding with firms and households borrowing to take on new projects and funding consumption. Banks should be willing to lend given the strong deposits and excess reserves. That is the assumption, yet reality is different. 

Reality is different because credit markets are different. Banks will ration credit not on solely on price but on standards for lending and expected risks. If perceived risks are high, the price of credit will not matter. Similarly, borrowing will be based on expected return from an investment project. The discount rate is relevant, but cash flows dominate. Similarly, households will take on more debt only if there is the perception that future income will improve. While central banks can change the cost of credit and the supply of money, these variables may not control behavior in the credit markets. The credit question is always forward looking for both parties. Will the creditor be paid interest and principal, and will the borrower generate enough to pay interest and principal? 

Sustained growth is driven by business and consumer confidence and not just low interest rates. Right now, business and consumer confidence are declining which creates an economic headwind.

Sherlock Holmes as Data Scientist

 I never guess. It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts. 

-Sherlock Holmes A Scandal in Bohemia.

Data! Data! Data! I can’t make bricks without clay! 

-Sherlock Holmes, The Adventure of the Copper Beeches

Sherlock Holmes is a good model for the effective investment analyst. He employs inductive logic although Sir Arthur Conan Doyle, the inventive author, sometimes uses the term deductive to describe Holmes. The police try to solve cases using their deductive skills, and Holmes as the consulting detective relies on his inductive skills. Deduction is not wrong; however, in many cases withholding hypothesis and focusing on observation may be more appropriate.

Deductive logic starts with a hypothesis and moves to observations and confirmation or rejection. It is a top-down approach. The police will have a theory that they present to Holmes, but Holmes inverts the process through starting without a hypothesis. Observations are made and from this information there is drawn a hypothesis or theory. Inductive logic extrapolates from observation. This is a bottom-up strategy. The solution is not developed first but is derived from a careful review of the facts. Induction starts with asking why about the information observed in an attempt to understand facts. There is not a hypothesis that tries to employ facts to support a view. 

The quant investor who focuses on data employs inductive logic. There may be a hypothesis tested which is the basis for deductive decisions, but the focus is on asking the question - what does the data say? 

Country risk and global equity trading - Emerging markets still have a strong local focus

Country selection still matters when decomposing risk for emerging equity markets, not so much for developed markets. Some updated charts on the variance decomposition between developed and emerging markets shows that EM markets are still not fully integrated with DM markets. Country choice and awareness are still important.

For developed markets, market beta followed by industry risk are the two most important components of variance decomposition. Country risk has shrunk over the last three decades. DM markets have become more deeply integrated. A large corporation in the UK or Switzerland and not much different than a US firm in the same industry. 

While there is a similar trend toward integration in emerging markets, the movement is much less pronounced. Market risk and country risk explain 90% of the variance with the relative importance respectively 2/3rds and 1/3rd. Focusing on country risk is still important. Of course, there is variation on geography or region integration, but the country macro environment for EM matters. 

EM investing should be integrated across equity, bond, and currency markets based on the macro environment. Capital flows into EM equities will be sensitive to macro events with sensitivity associated with overall trade and financial integration. The need for a global/local macro holistic approach is greater than developed markets that will have a stronger focus on global macro market beta considerations. 

Quant strategies - Factors and alpha

How can quantitative strategies be classified? Taxonomy is an essential part of science and an important, albeit sometimes overlooked part of finance. Even in data science, the core activity of unsupervised learning is to form some classification system. Quant strategies come in all types and use a multitude of data science techniques for classification; nevertheless, the key to understanding strategies is through classification. Unfortunately, many quant managers do not want to be typecast through a rigorous system because it may diminish their perceived alpha. Classification, however, allows for skill measurement and some form of prediction for when a strategy will work and when it will underperform. 

Most of quant strategies can be classified as factor-based. The manager will make investment decisions based on a single factor like momentum or value. There value-added is through the method used to extract that factor. One value manager may be different than another through their system or process of finding and exploiting the risk factor. However, the single factor can serve as the best explanation of return variation. 

Since risk factors can apply to any asset class, the taxonomy will classify the quant by the factor or set of factors that dominate the risk exposure and the asset class employed. A quant manager could be an equity value, credit carry, or multi-asset momentum. The risk factor must be well-defined and well-known to serve as a factor descriptor. The large factor zoo that can explain return variation explodes the taxonomy but still makes it informative. 

An enhanced version of factor investing is when a quant uses some data set to dynamically adjust or change multiple factor weights. Factors are measured at any specific time and adjusted or constrained by a second set of variables such as the business cycle. We can call this dynamic factor weighting. 

Within any quant strategy there can be further classification based on the form of implementation. This will be the portfolio construction. For example, a risk parity approach or volatility control can serve as a tertiary rule for classification.  

The second set of quant strategies are those that cannot be explained by a well-defined factor and hence can be classified as an alpha strategy. These strategies are unique from any well-known factor and hard to classify and thus must be given a verbal description. The inability to classify makes them extremely useful because they may have the strongest diversification benefits. Unfortunately, while it may prove to be useful to form a strategy grouping by alpha, it may be hard to predict future returns other than through extrapolation.

Classification systems for quants or any hedge funds by factors provide a more detailed and objective measure of fund behavior than any approach that is based on self-identification or description and thus helps to truly identify skill. 

Know your taxonomy and solve problems in finance

A simple taxonomy of diversification - All diversifiers are not alike

The cycle of thinking for superforecasters: "Try, fail, analyze, adjust, try again"

 “To characterize the thinking style of superforecasters, Tetlock uses the phrase ‘perpetual beta,’ a term used by computer programmers for a program that is not meant to be released in a final version but that is endlessly used, analyzed, and improved. Tetlock finds that ‘the strongest predictor of rising into the ranks of superforecasters is perpetual beta, the degree to which one is committed to belief updating and self-improvement.’ As he puts it, ‘What makes them so good is less what they are than what they do—the hard work of research, the careful thought and self-criticism, the gathering and synthesizing of other perspectives, the granular judgments and relentless updating.’ They like a particular cycle of thinking: ‘try, fail, analyze, adjust, try again.’”

- Daniel Kahneman in Noise 

One of the most important investment books over the last decade is Superforecasting: The Art and Science of Prediction by Philip Tetlock and Dan Gardner. (See Superforecasters - What Does It Take To Be Great? for more details.) Dan Kahneman recognizes the important skills associated with superforecasters. It is not about being the smartest guy in the room. It is the process of constant questioning and criticizing your work to get better forecasts. It is not just practice but real time evaluation so that as new information comes in underly assumptions and conclusions are reassessed. This process is not easy, but it leads to better results. 

Macro factors and bond excess returns

There are a significant number of macro factors or announcements that can be used to predict the direction of bond excess returns, yet all signals are not created equally. One of the jobs of good researcher is to focus on what has predictive power and discard or place minimal weight on those pieces of information that have less value. 

The bond markets are the bread and butter of global macro investor and there are real and inflation factors that can add forecasting power for futures excess return beyond what can be found in forward rates. (See "Macro Factor in Bond Risk Premia" by Ludvigson and Ng.) The countercyclical behavior of risk premia is driven by these macro factors. Investors are compensated for risk associated with the business and liquidity (inflation) cycles. 

The Fama-Bliss framework showed that excess bond return can be driven by the spread between forward rates and one year yield. The shape of the yield curve is a key driven for return forecasting and can explain about 1/3rd of the next year's return variation in the 2-5-year maturity range.  When macro variables are added to a forward rate factor model (Cochrane-Piazzesi), a model that explain about 44% of next year's excess bond returns. 

The macro inputs are associated with a real factor combination to cover output and employment information and an inflation factor associated with aggregate price levels. Investors need to be compensated from real and inflationary risks. Using 132 monthly economic series based on data from Stock and Watson, the authors find significant factor loadings using principal component analysis (PCA) around real output, inflation, and financial variables. This is done through bundling economic data around a latent factor model. 

The research found that the first component is associated with real economic data like output, employment, orders and housing, and financial data. The second component is associated with financial information like credit spreads while the third and fourth factors are associated with inflation. The eight factor is associated with stock market movements. 

The importance in this work for macro investors is that there is value in macro information that can explain excess returns and bond premia beyond a set of forward rates. Forward prices do not imbed all information about future bond returns. 

 

Diversification as ignorance protection

"Diversification is a protection against ignorance. [It] makes very little sense for those who know what they're doing." - Warren Buffett

Who isn't for diversification? Who admits their ignorance? These two topics go hand in hand. If you have strong knowledge about an investment, the bet size should be consistent with that knowledge. Better knowledge, less risk, greater exposure, and less diversification.  It is that simple, yet while we may have an information advantage, we can still be wrong. Diversification still protects against the chance of being wrong.  

As important as our perceived knowledge is a second question on the common knowledge held by others. Our knowledge needs to be unique, yet we don't know the knowledge that others have. It is not our absolute knowledge that matters, but our relative knowledge versus the market. It is not clear what information is embedded in prices.

Unfortunately, we also don't know the reaction or behavior by other investors to new information will be. We can only guess what their actions will be. We can have perfect knowledge about future events, but we cannot guarantee the market reaction to any piece of news. We may precisely know the employment numbers, but that does not mean we will be guaranteed excess returns because we cannot predict the action of others. 

Diversification protects against ignorance, not just our lack of knowledge about an event or company but also the knowledge and behavior of other market participants. The knowledge problem is just too complex. Regardless of how much knowledge you may have, everyone needs protection against ignorance. 

Think about the form of investment arguments

 

- From N Goodman 1965 Fact, Fiction, and Forecast

quoted from Counterfactual definition in Palgrave Dictionary of Economics D McCloskey 

All the above are essential equivalent statements, yet many analysts will say or act as if they are different. It is important to classify the type of statements used for the basis of argument. By stripping down arguments to their primal relationship and thinking through the choices of description, better tests and conclusions can be drawn.

It is critical when discussing a concept, especially when there is money on the line, that there is not only precision in language but precision in the structure of the argument. Forecast precision starts with assumption and argument precision. Faulty logic will always cost investors returns in the long run even if luck prevails in the short run. 

"Not priced in market" - Phrasing just says you don't agree with market

We constantly hear the phrase that some event or information "has not been priced into market". For some will say, "higher inflation is not priced in bonds", or "equities have not priced in tapering". It is often said with a sense of authority when the phrase is just saying that your view of market value does not match the current market price. You could just as easily say you believe the market should be higher or lower although that may not seem as authoritative. 

It is important to be clear that your view is different than the weight of opinion embedded in prices. Everything, through the funds committed, is priced in markets; however, the weight of opinion may not be correct about what prices should be at some future date. 

If there is no articulated difference between your view and the market, you are not presenting anything new or unique to the market. You agree with average opinion. Without a difference of opinion, you are only providing commentary on facts and market behavior. The strength or value of your opinion is based on the dollars committed and a function of risks taken. 

Good investment advice can come in short quips

"As a wise macro manager once said, it’s usually 3-4 big trades a year that drive 90% of your returns”

“Every macro process begins with a liquidity framework” 

“Equities don’t suddenly break because of over-valuation, they typically break when liquidity is tightened” 

“The rally in equities is constantly in question; e.g. at 1250 in S&P, there was no workout in Europe -- Greece was going to default and there wasn’t enough capital in the European banking system to withstand it” 

“Certain years and certain cycles favor trading over investing, in others it’s the photographic negative."

- Goldman's Tony Pasquariello quips 

Good investment advice can often be boiled down to short phrases. I agree that big returns for macro managers come through just a few big trades and not grinding out gains through many small trades. A macro process is always based on knowing the direction of liquidity.  Equity breaks on tightening liquidity is always a useful guide. I have almost never seen a time when a wall of worry wasn't present in equities.  Some quips may just be observation; however, the best will have some predictive value.  

The investment world is coming to an end! - Not yet

 

"Most of the time, the end of the world doesn't happen."

    - Howard Marks, Sept. 2008

Investment talking heads will often describe events in terms of doom and gloom. The world is coming to end. Forecasters only receive attention by being gloomy Events are unprecedented or never seen before with far-reaching implications. Those predicting better time are discounted. Those that disagree with gloom are often viewed as naive. 

The reality is that markets and the economy adapt; nevertheless, adaption is not painless. This is the process of creative destruction. There will be losers, but there will also be winners. This does not mean that there are not real costs with gloom events, but economies adapt. Adaptation does not mean that there is a return to the past. Adaption may lead to a very different world which is not liked or immediately accepted. Government and regulation attempts to smooth adaption and create an environment that is acceptable to the most with protections for all. 

Money is made not through accepting doom but thinking through the implications of negative thinking and what can be done to reverse or avoid the situation. Scenario or what-if thinking looks for ways to exploit pessimism which is discussed but not acted upon. For every doom scenario, there is a possible solution response. 

Fama- French factors - The easy money has been exploited

One of the great advancements in finance was the development of factor risk premia measures beyond market beta. It completely changed investor thinking about the type of risks faced, the measurement of risk, and the potential causes of return. 

The core work horse of the constantly growing number of risk premia is the Fama-French five: size - the spread between small and large stocks (SMB), value - the spread between cheap and expensive stocks (HML), momentum - the spread between strong and weak past returns (MOM), the spread in profitability (RMW), and the spread between firms that invest conservatively and aggressively (CMA). These are viewed as time varying and are related to the business cycle. The excess returns associated with each may rise and fall and even turn negative, but these factors represent core risks that will be compensated in the long run.

However, over the last few years the return spread between risk premia have closed and all have moved closer to zero. Because of greater knowledge of premia, larger flows, and changes in behavior, the Fama-French core risk premia don't see to provide insights or explanation of current stock behavior. Those investors who have focused on these core premia have been disappointed in return performance. The investment world waits for either a return to normalcy or a new paradigm. There is no easy money to be made through following well-established wisdom in a world that is in transition.  

50 years away from the gold standard - Cannot go back but going forward cannot continue

Many often use the word "unprecedented" to describe market events, yet this word is often overused. However, 50 years ago, the US went off the gold standard on August 15, 1971. A half a century ago the great currency debasement began. $35 for an ounce will never occur again. Could a different path have been taken that ensured money being tied to gold? Unlikely, given the stresses on Bretton Woods. It was never really a workable system as the global economy moved away from the devastation of the second world war.  Perhaps Keynes and his idea of a supranational currency, bancor, would have bene better, but that would have just moved the world to a true fiat system sooner.  

Going forward, monetary stability of purchasing power was placed at best in the hands of bureaucrats and at worst with politicians that only looked for short-term solutions to knotty economic problems. Forget long-term stability and herald the age of economic expediency. Nothing of the debasement story should be a surprise. There will be periods of stable prices albeit rising, but biases have tilted away from the dominance of monetary stability to a full employment goal with the emphasis on inflation as a measure of economic slack. Unfortunately, global slack and excess savings has dominated the last two decades and checked any immediate painful consequences of great debasement.  

The view is that economic technocrats will be able to stop any major inflation debasement because they have to skill to control economies and politicians have the will to stop any debasement pain through shocking the real economy. The Bretton Woods and early floating exchange decades tell us that bureaucratic skill and political will are always in short supply. 

For investor, the vision for the future should accept that inflation tail risk will not be a distant threat but a specter for any portfolio. In the short run, the monetary economy can be controlled, but tail risk from debasement swans should be expected.  

It is worth listening to President Nixon spin this change in regime. (You can get it on youtube: Nixon speech on ending Bretton Woods.). What a twisted piece of rhetoric that sounds so wrong 50 years later.  

Fortune is he who understands the cause of things.

 

"Felix, qui potuit rerun cognoscere causas" 

Fortune is he who understands the cause of things.        - Virgil 

The poet Virgil provides a great quote for any investment researcher who is often faced with the problem of deciphering causality and separating it from correlation. This quote also uses the word happy instead of fortune for the translation of felix. 

Whether fortune or happiness, understanding the causation is one of the primary skills of analysts. However, the real work come after finding the cause. From the cause comes the forecast and from the forecast comes the decision. The forecast and action may be more important for the trader who wants to make. 

While finding the cause is critical, it is not imperative. For example, many successful traders are reactive and may not know the first cause. The trend-follower does not focus on the cause but rather the result in prices. Understanding of causes may  give fortune but may not lead to wealth.  

Counterfactuals, history, and market analysis

Counterfactuals of past events have taken the history profession by storm. While not always liked by traditional historians, the process of thinking through counterfactual examples has enlivened historical thinking and has created fresh views about critical historical events. Other say that these what-ifs are not really the study of history but just speculation and conjecture about an event - a parlor game that is not worth serious consideration. Counterfactuals investigate the chain of causality in hindsight. 

History counterfactuals have especially focused on big events like war to ask the simple question of what would have happened if the winner became the vanquished in a given battle. Would the tide of history have changed? If conditions change, would we live in an alternative universe, or is the tide of history inevitable. 

Counterfactuals can also be applied to economics and finance through simple thought experiments. What if Jerome Powell was not the chairman of the Fed? What if Great Britain did not have Brexit? What if there was less QE after March 2020? Of course, these examples are backward looking. The more useful exercise is to use counterfactuals about future events. 

Thought experiments, not forming a decision tree of probabilities, are often the bread and butter for the good forecaster. Counterfactuals can be run forward to develop different vivid scenarios and create alternative worlds. What if a Fed taper was announced tomorrow? These scenarios, often relying on past thought experiments, are foundational to market forecasting. Forecasting based on past data, probability estimates, and thought experiments enrich the choices of what may be possible. 

The classic proverb "For Want of a Nail" can be applied to counterfactual history. What the shoe was properly nailed? 

For Want of a Nail

For want of a nail the shoe was lost.
For want of a shoe the horse was lost.
For want of a horse the rider was lost.
For want of a rider the message was lost.
For want of a message the battle was lost.
For want of a battle the kingdom was lost.

And all for the want of a horseshoe nail. 

Commodity Cross-correlation - Single factor shocks and financialization, then and now

 

The dynamics of commodity trading has always been more complex than other asset classes. The cross-correlation within commodities is surprisingly low relative to other asset classes so there is no simple or well-defined "commodity beta". The markets that go into the commodity asset class bucket is a hodgepodge that do not often move in common. Investing in a commodity basket will give you a very diverse set of risks from weather and global business cycle to logistical uncertainty. 

With such a low cross-correlation, it is hard to say that buying a commodity basket will give inflation protection or strong exposure to the business cycle. To get strong single factor exposure, the cross-correlations must rise. 

Metals, energy, and agriculture prices are often driven by different factors, yet the period surrounding Great Recession showed a remarkable increase in correlation across all markets. Some will state that this was driven by a common factor - the global decline in growth. Other will say this increased correlation was associated with the financialization of commodities through a strong increase in index buying and switching between commodities and other asset classes. 

Disentangling these issues are not easy because we have limited events for comparison. We do know that correlation will rise in a strong recession given a global decrease in demand; however, the pre-GFC period saw increased usage of commodity indexing to gain exposure to this asset class.

We have seen a fall in commodity index trading with the fall in the commodity super-cycle and the great bear market across many commodities. There was a mass exit from this asset class. The environment over the last year has changed. Index trading is increasing, and we are seeing an increase in correlation across many commodities as inflation expectations have risen. 

Commodities will still be more diverse than equities but we expect that commodity cross-correlations will increase. This will lead to common price behavior more closely tied to higher inflation expectations and the desire for portfolio diversification. For the more casual commodity investor, this decline in cross-market dispersion will be positive for their portfolio structuring. 

Sustainable investing continues to grow

The single biggest trend in investing over the last few years has been the movement to sustainable investing. Sustainable investing may now encompass 35% of all invested assets and may total $35 trillion dollars. (See the Global Sustainable Investing Review 2020.) This growth is astounding with $5 trillion investing in the United States over the last two years, yet it is less clear how this has impacted return behavior for individual stocks. 

There are several strategies for sustainable investing, so it is hard to disentangle return behavior in the same way as say market capitalization, value, or momentum. The global study identifies seven key sustainable strategies that range from exclusionary screening to thematic investing, yet the definitions do not provide clear guidance on how stocks can be filtered. There are no set standards.  

The two most popular are exclusionary investing and ESG integration. Exclusionary investing can be as simple as no tobacco stocks while thematic approaches may be a play on clean energy companies. 

At one third of all investing, it is now critical to have a view on sustainable investing and more importantly track the issues and decisions of sustainable investing to look for signs of crowding or price flow dynamics. Exclusionary stocks should see an increase in their risk premium and thematic stocks may see a flood of new money that will push prices above fair valuation. Sustain investing should be a core investing principle but looking for the impact on price is good fundamental investing. 

Where are we and where are we going? The perennial question for investors

 

No amount of sophistication is going to allay the fact that all your knowledge is about the past and all your decisions are about the future. (Ian H. Wilson, former GE executive)

Most analysts spend time focused on the past through conveying information or knowledge. There are descriptions of new data generated. It is placed in context with past events. A time series is reviewed. 

The analysis is about the past. It is just telling us where we are. Meaning is presented through context. Less time is spent on what new information means for the future. This presentation of knowledge is critical and necessary for any discussion of the future. 

I don't blame analysts for their focus on describing the past. I do it myself, yet the hard work is predicting or handicapping the future. Where are we going? A forecast must be made and confidence in that forecast has to be measured. The odds of success should be presented.

Trend followers focus on the past, yet there is clear intellectual honesty in their analysis and a clear focus on the future. There is no misrepresentation. Past prices are turned into trend signals. There is no deep narrative. Manipulations of price are turned into a signal and a decision about the future is made - prices are going higher, lower, or direction cannot be determined. If the perceived future proves to be wrong, there is an exit. 

There are, of course, other ways to convert past knowledge into future decisions, but there is no simpler way to start the process. Alternative forecast should start with the premise that the trend is correct and requires a strong standard to take any opposite position.