The economic costs of bankruptcies have not yet hit the economy

Regardless of what the Fed may do to increase liquidity, it cannot solve the problem of solvency. Funds can be made available to banks, but if borrowers cannot quality, credit will not be available. Buying corporate debt may increase market liquidity and allow for greater debt issuance, but that may not solve the problem of basic cash flow.

The cost of bankruptcies and constrained credit will strain the economy and pull households and business away from reaching normalcy.

Distortion in fixed income - Corporates versus Munis

The Fed programs for corporate bonds have started strongly with billions in corporate bonds purchased through ETFs. These purchases will include firms on the verge of bankruptcy, no questions asked as long as it is included in the ETF benchmark. It has also caused a surge in new debt issuance. A municipal bond program, the Municipal Liquidity Facility, has been announced but details have not been finalized.  

Given the differences in program timing and size, there is a distortion between corporate and municipal bond spreads after accounting for taxes. 

This spread difference will likely close once the liquidity facility starts to kick in. Investors should think about these relative spread opportunities; however, the special risks of state and municipals should be apparent. States are not diversified nationally, and their lockdown policies will have a direct impact credit quality and relative supply issuance. Nevertheless, the muni-corporate opportunities may be one of the better places to look for fixed income value. 

If you want convexity, hold the trend-following CTA

What is the value of trend-following? In one word, convexity. This is clearly shown with the recent paper from the folks at KeyQuant, see "Unfortunately my CTA was diversified".  The idea of trend-following creating convexity is not new and variations of the non-linear performance has been produced in a number of different formats for decades. KeyQuant takes a different approach and compares the convexity of a trend-following CTA index against a non-trend-following CTA index. 

KeyQuant is able to show the difference between CTA styles with respect to convexity. You cannot get more stark CTA comparison, nor can you get a clearer picture of the benefit of style differences within the liquid alternative futures trading space. 

The capture measure puts into numbers what is visualized above. The capture measure looks at a threshold level and finds the associated return of a CTA strategy. A multi-strategy CTA is good. An investor will receive gains at the downside threshold levels, but the trend-follower will be even better with producing strong positive non-linear gains. There are two CTA worlds - a stable multi-strategy CTA that may mimic existing payoffs for other investments, or a trend-following CTA that may have lower stand-alone returns but will generate more convexity. 

An analysis using the return to drawdown ratio or Ulcer Performance Index (UPI) will generate a similar story. There will be a better pay-off scaled by a drawdown through using the pure trend-follower.  

Picking a CTA is a matter of  convexity choice. If there are no divergent moves and market stability, there will be little value with holding a strategy that has positive convexity. This is not a matter of saying there will be crisis offset from a trend-follower. It is question of whether an investor wants convexity. An investor may not want to pay for convexity in a stable financial world. However, if an investor is not sure of the environment, then having a strategy that can generate downside convexity is useful.

Dealing with uncertainty - Combine prices and beliefs for better forecasts

A recurring theme in forecasting is that the combination of forecasts or ensembles will improve prediction. Blend information from different sources to make better forecasts. Sentiment, beliefs, and opinions help improve the predictive information from prices. This is especially applicable if prices are distorted from the intervention of governments, come from illiquid markets, or face high bid-ask spreads. When it is hard to inject information into the price, looking at beliefs will be helpful.

Prices, through the action of market participants, provide market information but so do the actual beliefs of the traders. Beliefs are a separate form of information. An interesting paper, Are markets more accurate than polls? The surprising informational value of "just asking" in the journal, Judgment and Decision-Making, finds through direct comparisons between self-reports and prices in prediction markets that beliefs are as accurate as prices. The combination of beliefs and prices will actually add value. Prices do not effectively aggregate all information and beliefs from self-reports can be an important source of new information. Notice the difference in Brier scores, the squared error between forecasts and outcomes, between prices and beliefs. 

This study was conducted under a set of special conditions of self-reporting forecasts in a prediction market that tracked the beliefs of traders at the same time as trades were conducted. This was a laboratory study and would be hard to replicate in real life. Still, it provides a framework for thinking about how beliefs and prices can be used to build financial models. 

Use price information. There is no doubt that they are an aggregator of information, but if you can get beliefs, opinions, and sentiment, incorporate this added information. 

There is a move to nowcasting for predictions, but a significant part of the value of any nowcast is using information that is as timely as possible from surveys. Discount the data that is old but employ recent survey data of market participants as an added tool. 

The heavy lifting is determining how to weight this information relative to prices. For example, the latest information from the Atlanta Fed business survey was announced yesterday. See the Survey of Business Uncertainty SBU. The most telling numbers were the measures of expectations and uncertainty. These do not look like numbers consistent with robust increases in financial prices. These beliefs may help forecasting future asset prices.

This simple belief measure is not like the lab study done, but it can be suggestive of business executive beliefs that can be used to temper thinking about financial prices that have had the benefit of a surge in Fed liquidity.

"Debt relief"- Never good for the debtholder

If the Treasury will issue the debt, the Fed will currently buy it. There is little separation between the fiscal and monetary management. The same may also apply to other major central banks. For corporate debt, the Fed is buying hundreds of millions of ETFs and have support programs for investment grade, high yield, municipal debt, and commercial paper. Investment grade BBB spreads are now below the 2016 spike as measured by the BAML index. 

Bond life is good, yet past run-ups in debt have ultimately ended in significant debt relief. The debt relief is for the issuer and not the buyer. The flood of debt from fiscal deficits will have to be addressed and the only way to solve deficit excesses is through inflation or a restructuring.

Older research from Carmen Reinhart, Christoph Trebesch, "Sovereign-debt relief and its aftermath: The 1930s, the 1990s, the future?" tells a sobering tale of how past debt excesses have ended in "debt relief", the kind words for default. 

It may not have led to debt gridlock or a failure to have capital flow to debtor countries, but it resulted in a substantial cost to debt holders. In the case of WWI debt, it took 16 years and the Great Depression to have the debt shock, but it did occur. The debt crises of the 80's, 90's, and '08 resulted in a swifter debtor adjustment, so creditors may not have a decade to get their financial affairs in order.

Monetization and excess savings combined to eliminate a debt crisis, but timing may not always be aligned. The Fed may change their buying programs and a recovery will change the supply of credit. The equilibrium rate of interest and spread risk premia needed to clear markets is a war between extremes, a pull to negative rates by central banks and the pull to be paid a premium for an uncertain payment of principal and interest in the future.

Active mind set with passive investments - Your portfolio reveals preferences

Does return performance happen to you, or do you take responsibility for your portfolio returns? If a market surprise happens, do you say loses are the market's fault? Even if you hold index or "passive" investments, are you not still responsible for portfolio performance?

While we might live in a passive index investment world, it still requires that we have an active mind set which takes responsibility for portfolio construction and adjustments. The active mind set assumes ownership for what may happen to portfolio returns. You may not be able to predict portfolio returns, but your structured choices will be consistent with a range of outcomes. Passive investments do not infer there is a passive portfolio. A portfolio of risky index funds will represent a risky portfolio choice. The investor is responsibility for greater underperformance when there is a negative shock. Performance results will be consistent with the choices made even if there is no active management. 

An investor owns the world view consistent with their portfolio choices. A high exposure to equity indices is a higher risk, pro global growth portfolio. An unchanged portfolio over the last five months is a world-is-not-different portfolio. 

A portfolio is a set of revealed preferences regardless of whether it is viewed as passive or active. Portfolios reveal our market views whether we admit it or not. However, this does not mean that investor should become active except to the degree that their view of the world has changed.

Markets move and trend-followers make money - Story is partially right

We have heard this story before, "Look at those market moves this year; of course, a trend-follower should make money." Some have made money in 2020. Some have not. Most have done better than the market. Still, many have underperformed relative to expectations given the circulating theme about "crisis alpha", or market divergences. 

The key to understanding who will make money is to go back to basics. Are there trends? Did managers have a way of exploiting or identifying these trends? Did managers have high exposures to those markets that showed trends?  

We have used the simple framework of Styles, Timing, and Markets (STM) to classify trend-followings. What is your style for finding signals? What is your timing or look-back period? What markets and exposures do you trade?  However, this is all based on whether there are trends to exploit.

The folks at AQR have decomposed the drivers of trend in their research paper "You Can't Always Trend When You Want". Their research focuses on the simple idea that trend-followers make money when there are trends. If no trends, then no profits. Trend following performance can be broken into three parts, the average magnitude of the market move (absolute Sharpe ratio), the trend efficacy or beta that is exploited from the market move, and the diversification multiplier which measure the exposure to different markets. 

For this year, there have been strong absolute market moves, albeit tempered when discounted by volatility. The big differentiator is the trend efficacy or ability of the manager to exploit the trends and market exposure differences. More diversified managers have seen a drag on performance, and those managers who cut exposures given the volatility spike lost trend efficacy at just the wrong time. The issue of whether CTAs are actually trend-followers is a topic for another time.

There were four major trends to exploit in 2020 but each needed different signal capturing. Bonds needed long-term trend exploiting. Equity needed intermediate trend tracking to get short at the end of February and then turn long at the end of March. Energy futures needed an intermediate model that could hold a trend in the face of volatility, and currency trading needed a shorter timeframe. No one trend approach would have been effective in all markets. Additionally, the concept of volatility-sized positioning worked against managers.

As in the recent past, the best potential financial trends were arrested through active government intervention. When governments subvert market divergences, trend following will not reach its potential. Cutting market tails may be in the public's interest, but it will distort the level of price dispersion. The large market moves of yesterday will not be seen in the central bank muted world of today.

Asset allocation under stress - Learn to regime switch

There has been significant research on regime switching and asset allocation, but it has not been formally used by many investors. The reason for this lack of use has been simple. There have been few perceived major regime changes since the Great Financial Crisis. There has been a premium on keeping asset allocation simple and focusing on risky assets. The last five months suggest that managing assets under stress is not just critical but essential. When the world changes and there are tail events, allocations have to adapt.

There is some older accessible research for managing stress that can be easily implemented; see "Risk-based dynamic asset allocation with extreme tails and correlation" in the Journal of Portfolio Management, Summer 2012. The key take-aways from this work are twofold. One, focus on what is important for risk management, the CVaR, conditional value at risk or average shortfall given the VaR is breached. Two, model or focus on changing market regimes, in the simplest case, normal and extreme. The CVaR is dynamic and changes through time, but through using a regime switching model, these changes can be forecast. If you know or can measure the risk state of your portfolio, the allocations can be changed to reduce exposure to market extremes. 

The authors looked at a simple five asset portfolio, measured the CVaR through time and then developed a switching model to forecast the shortfall state. When the regime changes, the asset allocation is adjusted to some target CVaR level. Of course, when this is done with unbounded optimization, there can be extreme changes in the portfolio. The authors worked with a constrained optimization and found that there is still strong benefit from adapting to regime changes. A higher-level model will allow for more assets and a more sophisticated allocation process.

There can be different levels of allocation sophistication, but regime switching is now an accessible portfolio tool. However, even if there is some simple switching used with a nowcast model of financial stress, there can be improvements in asset allocation. Similarly, CVaR can be measured for a benchmark portfolio in a spreadsheet. The theory, tools, and data all make this type of asset allocation accessible and within the reach of most investors.

The Joseph and Noah effect - Stable versus tail extremes in portfolio management

A great analogy that will provide insights with portfolio construction is to think about the Joseph and Noah effects. The "Joseph effect" is the simple idea that there is long-term persistence. As stated in Genesis, seven good years may be followed by seven bad years in Egypt. Overall, the world is a stable and the past can tell us something about the future. That may be a good working assumption, but the world may also see the "Noah effect". Financial markets may be stable, but every once in a while, there will be the great flood of a crash and everything will be washed away. We don't know when the great flood will come, but we need to be prepared for these tail events. 

Portfolios structured for the Joseph effect may go a very long time without a problem, but then the flood of a major downside shock will come. However, thinking that the 100-year flood is coming every year is not helpful. A portfolio will miss opportunities. There needs to be balance between building with normal tools but also looking at extreme value and expected tail loss statistics to track what is possible, albeit not likely.

Investors should go through the exercise of thinking about Joseph and Noah effects and whether they are prepared for both.

See "Noah, Joseph and Operational Hydrology" in Water Resources Research, 1968 for the original work on the Joseph and Noah effect as well as a discussion on the use of the Hurst range or statistic. 

Building COVID19 scenarios for investors - Think about time effects

Working through economic recovery scenarios and their impact on any portfolio requires deep thinking of time dimension effects. Talk has focused on the shape of the recession, which is a timing issue. The construction and adjustment of a portfolio will be based on the path of recovery. 

At the extreme, a short recession with a quick return to normal suggests holding more risky assets or maintaining the status quo. A damaged economy with a long recovery suggests a more conservative portfolio focused on a near-term cash heavy portfolio. There are a number of in-between paths.

We break timing into three periods. The short-term or immediate will be the reversal of the shelter-in-place lockdown. We are working through the state start-ups right now. 

The intermediate will be the more formal restart of businesses. The Lockdown may be lifted but supply chains have to be managed. Production has to resume, and consumers have to spend. Stores have to staff and deal with any residual rules. 

The longer-term is associated with the impact on consumer and business behavior. Will firms invest? Will consumers return to their old habits? How will firms and households deal with their debt overhang and adjust balance sheets? If the world has changed, the long-term problem is the behavioral impact of the pandemic.

The speed of adjustment and time within each phase will vary, but the length of any transition will impact the recovery swoosh. The swoosh will require a dynamic portfolio adjustment plan.

One way to support this scenario building activity is to use a dashboard approach. Boston Consulting Group (BCG)  in their posting "How Scenarios Can Help Companies Win the COVID-19 Battle" presents a mock-up of what a COVID-19 dashboard may look like. By setting up a tracking system, better inputs can support any decision.

The big need for scenario analysis during this pandemic

What will happen next quarter, for the rest of the year, or for the next two years? You are not going to find out by forming some expected return or volatility measure based on past data. There is no easy way to compare an unexpected future with the pre-COVID-19 past. There are only uncertain futures given the uniqueness of the "Great Lockdown". That does not mean that the future will behave irrationally, nor does it mean that we have no idea what the future will hold.

Investors need a different way of thinking about what may happen in the future and how we should be prepared for these alternative realities. We are not facing measurable risk in the conventional sense of volatility (standard deviation) but rather uncertainty that can be given probabilities. 

A useful framework for scenario analysis has been outlined by Peter Schwartz in The Art of the Long View: Planning for the Future in an Uncertain World. There are other books on scenario analysis, but this is a good read from a strong expert. 

Scenario analysis is more involved than just developing optimistic, base, and pessimistic scenarios. We will not do the outline justice, but here are some of the simple steps for approach a problem that needs scenario building. 


1. Identify focal issue and or decision - What is the decision you want to make? As stated by Schwartz, begin "from the inside out"? For an investor, it could be thinking through the downside for risky assets, or it could be answering the question of corporate bond risk. Provide scenarios for problems that matter.
2. Key forces in the local environment - List the factors that will influence success or failure for that decision.
3. Driving force - Explore the drivers for those key forces. Try and think of the information that you don't have but wish you did.  
4. Rank by importance and uncertainty - Develop a ranking system for those key drivers. The ranking should be on two levels, those drivers that are most important and those that are most uncertain.  
5. Selecting scenario logics - The logic of scenarios will be driven by the key drivers and the uncertainty that is faced. The focus is on the uncertain drivers of that will impact specific decisions. 
6. Fleshing out the scenarios - There is a narrative that describes how these drivers will work through the real economy or the financial system. Think of this as the flow through system. 
7. Implications - From these scenarios, there is a focus on implications. What will other investors and firm do if the scenario arises? What will be the reaction to these scenarios?
8. Selection of leading indicators and signposts - There should be catalysts or markers that will give you some indication that your scenarios are beginning to play out.

I will say that developing scenarios is harder than some quant work. Imaging a world that does not yet exists is not easy, but even provide a foundation for future empirical work.

See also: 
Pierre Wack and deeper thinking about scenario analysis

Ride the trend but remember what is possible

Using scenario analysis to help with asset allocation - A simple solution to a complex problem

Scenario Analysis for Systematic Managers? Absolutely.

A simple taxonomy of diversification - All diversifiers are not alike

Accept the investment environment - Just use the right tools to describe it, cases and scenarios

Total Portfolio Approach - Willis Towers Watson survey shows increased investor use

Even before this global pandemic shock, there has been a shift in thinking for how to best run a pension or sovereign wealth fund. There is movement from the classic strategic asset allocation (SAA) model to what is being referred to as the total portfolio approach (TPA). Sovereign wealth funds have been at the forefront of employing TPA, yet there has been limited description and discussion of this model. 

The most accessible work has been done by Willis Towers Watson (WTW) through their Thinking Ahead Institute in "Total Portfolio Approach (TPA): A global asset owner study into current and future asset allocation practices". However, there is no standard definition or agreement of what is a TPA approach as made clear from the results of the WTW survey. It is an elusive target for analysis, no different than some discussions of the "endowment model". Nevertheless, the core thinking focuses on holistic goals with aligned interests of governance and management to dynamically achieve better performance.  

WTW presents a simple comparison between SAA and TPA. The total portfolio approach embraces risk factors over asset classes and focuses on fund goals over a benchmark portfolio. Hence, effort is redirected to total return over alpha above a benchmark. The monitoring and governance of the portfolio also changes. Since a board is not monitoring the portfolio against well-defined benchmarks under TPA, the oversight or management flows more directly to the CIO. The board focuses on whether the CIO meets the overall portfolio return and risk goals.

More work needs to be conducted on this approach to make it better definable and measurable, but TPA may be more than just a fad. Both large and small firms are using it, and when faced with significant changes in the investment environment, this approach may be more responsive to shifts in the investment environment and increased uncertainty. 

Alternative Risk Premia (ARP) return dispersion continues in April

Alternative risk premia accessed through total return swaps continue to show wide dispersion across asset classes and styles. While these long/short strategies are generally believed to be low volatility factor exposures, there can be wide differences in return when there are large focused shocks to financial markets. The last 12 years have been relatively stable and has not represented a full business or financial cycle. Extrapolating from this stable period would have given investors a false sense of security. 

Short volatility focused strategies have seen sharp declines consistent with a volatility spike. Value and size ARP have done poorly during this downturn. Similarly, commodity carry has done well given the huge demand and supply shock to the oil markets. These targeted ARPs may provide good focused return if you are on the right side of a shock, but that requires prediction and dynamic adjustments. For investors that want an uncorrelated set of exposures to market beta, a diversified approach albeit adjusted for risk and market regimes is a better play. 

Futures market says negative rates coming to US

Fixed income investors are generally pessimists. They see trouble even when the sun is shining. They are skeptics and paranoid. Never trust anything the government or central bank tells you, and if they deny too much, it is all the more likely to occur. 

The rate futures (Fed fund, SOFR) markets are now pricing the chance of negative rates for 2021. The prices just moved beyond 100 for highs. Eurodollar rate options are showing volume and pricing that give negative rates a real albeit low probability of occurring. Anyone who does not think this is a real probability is just fooling themselves. Markets have been surprisingly fast at discounting future Fed policy better than the Fed itself. 

We have seen the Fed balance sheet explode in days not months beyond anything seen during the initial GFC surge. We have seen new Fed programs that were just dreams during the last crisis. All Fed programs are on steroids. Fed Chairman Powell has stated he will do "whatever is necessary"; his variation on former ECB president Draghi's "whatever it takes".

The ECB has been running negative interest rates since June 2014. The BOJ has been doing the same since January 2016. The global bond markets are awash with negative rate bonds. Ken Rogoff, Harvard professor and former chief economist for the IMF, has made a strong case for negative interest rates. In his view, there is no downside. It is just an engineering problem that has to be managed. 

Whether a continued crisis with a slow rebound, market gridlock, or continued war financing, there are any number of scenarios where negative rates could become a reality. Why not for the US?

    

Fed corporate bond purchase programs announced, now need follow-through

The Fed announced corporate investment grade and high yield bond purchase programs in March and increased their size on April 9th. The programs will include the purchase of bond ETFs. The reaction has been strong and swift. Spreads have tightened, money has flowed into bond ETFs, and corporate bond issuance has been high. Since the March 11 declaration of a pandemic by the WHO, $300 billion of new bonds have been issued or almost 3 times the amount for the same period in 2019.

More information has been provided by the New York Fed, on the corporate purchase programs, see New York Fed Releases Additional Information on Primary Market and Secondary Market Corporate Credit Facilities in Preparation for Series of May Launches, but the plans have not been activated. 

The market has discounted much of the cash flow problems and the significant increase in default probabilities. Defaults will likely be much higher than the GFC, yet spreads have moderated. The current strategy is to take a chance and hope that the Fed will take you out of your troubles. It is a strategy, but it based on the pricing of risk through central bank liquidity and nothing to do with the underlying value of companies. What the Fed gives, the Fed can takeaway. 

Credit issues more about financial flexibility than financial leverage

The clear focus of many analysts during this crisis has been on industry analysis and the overall leverage of the firms. More highly levered firms in the wrong industries are at the financial risk during the "Great Lockdown". The data are readily available on leverage, but that is not the whole story. The real issue is whether firms have more financial flexibility not relative leverage. 

Two firms may have equal leverage, but the one with the refinancing of debt further in the futures will be less risky. The debt schedule matters. You don't want to be a CFO for a company that has to refinance bonds and cash flow is impaired. Those firms that have managed to lower cash reserves also have less financial flexibility.

Firms that have placed more emphasis on financial flexibility have often been penalized by activist investors, yet flexibility is a valuable risk management tool when there is a cash flow crisis. Without jumping into the stock buyback controversy, buybacks reduce financial flexibility because firm excess cash is reduced. Even worse is if debt is used to facilitate the buyback.

We should see more value with firms that have high flexibility, but the issue is complex. With the crisis upon us, any policy that attempts to reduce the current liquidity problems should have a greater impact on firms that have less flexibility. 

The Fed is trying to support credit markets through liquidity and buying programs which should reduce constraints on less financially flexible firms. The Fed buying programs are not about bailing out corporate bondholders but allowing new debt to be issued in a more friendly debt environment. If bondholders are suffering major losses on their existing positions, new debt will not be able to come to market at reasonable spreads. Financial gridlock will disrupt those firms with less flexibility. The lender of last resort should provide capital to those firms that needs funds and have good collateral. This idea now extends to firms that have flexibility issues from cash flow lockdown problems but have good businesses.

A cross-sectional event study can highlight the problem of low flexibility and the benefit from a stimulus program. The results are suggestive of the relative value associated with flexibility. See the new paper "How valuable is financial flexibility when revenue stops?  Evidence from the COVID-19 crisis". There is value from increased financial flexibility, but companies and investors did not demand that from firms over the last few years. That near-sightedness is now on display in current equity behavior. It is well-worth the read to frame the flexibility issue and provide investors with the right credit focus beyond just leverage. 

Gold - Representing investor store of value fears

Money serves as a store of value especially during uncertain times. Gold can also serve as a store of value especially when money is being cheapened. A combination of zero interest rates, risks with yield chasing, and central bank balance sheet explosions has created a good gold environment. Demand as jewelry has fallen significantly, but ETF demand has soared. 

Cash versus gold is not an either-or choice. Just a small exposure to gold across a wide investor base can prices rising.  The declines in financial stress, volatility and policy uncertainty during April coupled with some expected relaxing with lockdowns may soften gold demand, but the financial environment will continue to look attractive. 

Global monetary experiments create cash value uncertainty and demand for store of value diversification.

Large CTA return dispersion - Wide differences based on size

Small CTA return performance was better for the first quarter in a wide return dispersion world. Concentrated positions or nimble trading, some managers were able to take advantage of the large market dislocations.

Market focus has been on stress and uncertainty not fundamentals - This will change in May

Macro fundamentals are important but changes in many these economic data over the short run will unlikely explain a large portion of the variation in asset prices over the last sixty days. Changes in risk and uncertainty have had greater impact on markets. The reversal in these risk and uncertainty measures may have been the key performance driver last month, but that will change as the focus moves to the lockdown reversal logistics. 

Take a look at some of the variables that drive risk perception (risk-on/risk-off thinking) versus the real economy. These variables seem to have had a greater impact on return for April. The real economic continued to slide lower in April.

The VIX spike peaked in March and is on a steady decay. Volatility is still high by any measure versus most of the post-GFC periods but there is a move to normalization. The Fed financial stress is still above the average score, but is also normalizing. The Chicago Fed financial conditions index is following the same pattern as the stress index. Equity and policy news indices are still high but have peaked or are stable. 

There were overshoots with many of these variables. These overshoots have been reversed by the large infusion of new money from Fed, but now the focus will be on earnings and cash generation. 

Manager alpha and manager selection alpha - Two different investment skills

Is there such a thing as selection skill? If there is manager skill at identifying opportunities, there is also investor skill at identifying good managers. Both can represent alpha, but the skills are different. Managers produce alpha. Investors identify alpha.

Some investors are better at choosing managers than others. Other investors may be poorer alpha selectors but better portfolio builders. Manager choice may be more involved than just measuring alpha. Is there a good way to describe that skill beyond simple attribution?  

All the focus is on measuring alpha for a manager, but investors have to find and allocate to these managers based on how they may fit within a portfolio. Manager selection is a different skill than picking stocks. It is macro portfolio focused not security focused. 

The due diligence analysis for a manager is similar but not the same as picking a good portfolio of stocks. This difference can be formed as a question. Could an analyst that can identify alpha investment opportunities easily transfer those skills to picking managers and could a good manager picker make a good investment analyst? It seems like that these skills are transferable, but this similarity should not be viewed as a given.

Attribution and performance analysis of pension funds and investors can measure skill at building a portfolio. You are a good investor because your choices generated alpha versus a benchmark, but investor skill is more than the sum of manager alpha choices. Investor skill is also about asset allocation and manager fit - the bundling of managers to create a portfolio. 

Investors have the added issue of choosing between active and passive (benchmark) investments. Their skill may include not playing the game of manager selection. Additionally, investors have the added burden of having to build a portfolio across all asset classes. Lower alpha in the right asset class may be more valuable than more alpha in the wrong asset class. 

Picking managers and building an investor portfolio has a different skill set than manager skill. It is important to recognize the difference in investment skills before there is an attempt to pass judgment how portfolio performance. 

Trend-following managers - Paid to be with the crowd?

A quick look at CTAs for the first quarter show wide dispersion across managers. Nothing like a little volatility to separate the performance of managers, yet there is an interesting research piece from last year that generated some counter-intuitive results for CTAs. Following the momentum factor crowd actually generates better performance than trying to be different within the CTA space. (See "When it pays to follow the crowd: Strategy conformity and CTA performance") 

The general view, backed by empirical tests, is that hedge fund uniqueness translates into higher returns. Managers who do not move with the crowd show better returns as measured by low R-squared with benchmarks, low correlation with peers, or high SDI (strategy distinctiveness index). The SDI is equal to 1 - correlation(ret(i),ret(cluster)). Those hedge funds who make more or greater idiosyncratic bets show higher skill and returns. 

The authors of "strategy conformity and CTA performance" test whether this hypothesis also applies to CTAs. It should be expected that they will follow the same pattern as other hedge funds. Since CTAs are usually trend-followers, it would be natural to analyze distinctiveness versus a momentum factor benchmark. This paper's empirical analysis shows a different conclusion than normal. Those managers that have more uniqueness underperform in general. CTAs that have low (high) SDI also have high (low) time series momentum beta. 

Note the high average return, Sharpe ratio, and alpha for low SDI firms. These returns persist over time, so the cumulative effect is large. Those firms that have low SDI have a high time series momentum beta while the more unique managers do not have any beta with momentum.

CTAs in the low SDI quintile have high positive returns when momentum is up and negative returns when momentum is down. There is a minimal momentum effect in the high SDI group.

The CTAs that do not follow the crowd or follow the time series momentum factor are not rewarded for their uniqueness. Momentum seems more prevalent in active futures markets, so trying to exploit other factors is for many managers a loser's game. This has proved to be the case during the strong market dislocations in the first quarter of 2020.