Sunday, December 31, 2017

Do you really want to live with a 60/40 allocation in 2018? - Follow the numbers and you will likely underperform


Numbers and statistics are a funny thing. They usually don't lie and are not fake. You can misinterpret them, but numbers tell a story and it is the job of the investor to either accept the story or come up with an alternative. 

For many investors, there has been a strong adherence to the 60/40 mantra. The mantra says that 60/40 will protect an investor in a downturn and provide strong risk-adjusted returns in all environments. There can be variations on this theme, but 60/40 has often served as the core allocation for many investors. For 2017 this portfolio combination was again a winner.  The 60/40 SPY/AGG (using index total returns) combination generated a return of 13.74% in 2017 which was above the average of 9.86% for the last 30 years.



It is natural to keep with this winning strategy, but it might be good to run some scenarios to see what you will need in 2018 to generate the long-term average annual return for a 60/40 combination. Do not expect a repeat of 2017. 

We used the average forecast for 2018 equity returns (SPY) from a Bloomberg survey as a base equity number and worked backwards to calculate the returns necessary from the AGG index to generate the average annual 60/40 return over the last 30 years.  In this case, the Aggregate index would have to generate over 15% next year. That is unlikely to happen. It would be over 2 times the average AGG return and just under 2 standard deviations from the average over the last 30 years. This return would be without the help of the higher currents yields over the last 30 years. 


Even with the expected SPY returns, just to generate a 6% return for the 60/40 portfolio would require a significant decrease in rates since yields are so low. To get the average return for a 60/40 portfolio, equities will have to exceed the high end of the SPY estimates from analysts and the Aggregate bond index will have be close to its average return of 6.5%. Even to reach expected portfolio returns of 7% will require above average returns in equities and a positive return year for bonds. Most of these scenarios are not likely. 

One of the only likely scenarios for better returns is for asset allocation adjustments based on strategy diversification focused on absolute return. This means high alpha strategies or dynamic beta strategies that can exploit moves in equities and bonds regardless of their market direction. If you want to beat your target return and the 60/40 average, you will need to look outside the usual asset allocation box. 

This investing is easy! Everything positive for 2017, but unfortunately we are starting a new year



Many pension and endowments are going to post double-digit returns for 2017. Most have exceeded their actuarial expect return assumptions of 7%. Family offices and general investors have also posted good returns for the year. 

Equity returns for US, international and emerging market portfolios all generated over 20% and bond portfolios were still able to post positive returns. It was a great year with any 60/40 stock bond combination generating more than ten percent. The only asset class that did not perform well was commodities.

The frustration for many was being too conservative at the beginning of 2017 and holding alternatives for diversification. Of course, 2017 began with significant political uncertainty and growth concerns. We suggested that the market would face the bimodal risk of either secular stagnation or a growth resurgence. We got the growth resurgence, but it was a surprise for most investors. 

Well, you can put 2017 in the books and you have start a new year tomorrow. We will likely be surprised again, but investors cannot bank on positive surprises as an investment strategy. 

A coherent strategy will be to place emphasis on two factors - valuation and momentum. Momentum tells you to stick with the current allocation while valuation states to take money away from the most overvalued asset classes. You can stick with momentum in the short-run but a more strategic focus will think about valuation as the driver for 2018 and increase diversification across asset classes and strategies.

Saturday, December 30, 2017

Forget about overvalued equities - Forecast survey expects 6% - Optimism still exists


Perhaps the market analysts making 2018 forecasts for the SP500 did not get the memo on valuation. The equity markets are overvalued by most any measure, yet the median forecast is still expecting a 6% total return in the year-end Bloomberg survey. 

In context, the unconditional mean return for the SP 500 since inception is 9.7%. For the last 30 years, the average return is 12%. Hence, the median for 2018 is about half of the shorter-term average and 1/3 less than all time averages. Clearly analysts are not expecting 2018 to be an average year.



However, if we look at the periods when the market was significantly overvalued like 2000 and 2008, the conditional forecasts suggest that this 6% forecast is very optimistic.


The composition of the SP 500 has changed significantly over its lifetime and dividends provide a good cushion for returns, but these 2018 forecast numbers seem aggressive. 

For an investor who wants to bet against the consensus, the first line of defense is diversification across assets classes and investment styles. Additionally, downside protection can be generated through the use of option strategies that protect against any worst-case scenario. 

A significant part of investing is always about competing against market consensus. So, do you believe the consensus?

Friday, December 29, 2017

A dollar-funding problem? - Cross-currency basis swaps signal temporary dollar shortage


Global macro traders look for outliers. The good ones have a disciplined approach to review and process lots of cross-market relationships looking for the few that may be out of place. These are the relationships that need capital and for those that can provide the funds, there is a reward. 

Think of these cross-market outliers as tremors that may indicate something more going on, an indication of a greater potential financial dislocation. This is especially the case when there it may occur in markets that are supposed to be liquid like the foreign exchange markets.


International trade flows are dwarfed by the capital flows that are used to lubricate global banking and financial intermediation. When the system is working well, interest rate differentials between international and domestic markets are minimal. In a well-functioning system where there is no financial stress, covered interest rate parity holds. When there is shortage of funds in the systems, prices will signal the stress points. Debt and loan markets have exploded with strong borrowing demand. Much of this funding is still in dollars, so dollar swap funding is important to facility the flows.


The short-term cross-currency basis for many currencies has signaled stress again this year although there has been a significant snapback in the last week. The three-month basis is now similar to where it was last year after an extended divergence. The negative levels for the cross-currency basis swaps suggest that this has been another end of year of dollar shortages. Basis Lows were hit at end of November 2015, December 28th in 2016, and December 15th this year with the worst negative basis in 2008 and the end of year 2011.


There is always a certain level of window dressing and fund stresses at the end of the year, but we are concerned with these basis swap dislocations even if there are not signs of financial stress in other markets. First, we are dealing with very liquid markets. Second, basis swap dislocations are a sign of larger funding problems during times of stress. Third, and most important, the Fed tightening and balance sheet unwind will create conditions for a dollar shortfall. 


We have seen that when there is a dollar shortfall with Fed tightening, EM debt markets become stressed. While we still view EM as the place for greatest return opportunity, the cross-currency basis points to an investment tremor. Tremors are not a quake or an indication of a true market-funding shortfall, but it is a signal that is worth following.



Thursday, December 28, 2017

Add mental models for the new year - Broadening your thinking process will improve your productivity





Most New Year's resolutions focus on the physical,  "I will exercise more, eat less." A better resolution should focus on mental muscles like, "I will add some mental models to my thinking."  This may help better manage time and effort and allows you to undertaken tasks more efficiently.

So what is a mental model? It is a process or way of thinking for solving a problem through using a representation of the environment. Because there are different ways to represent the environment and use intuition, there are alternative mental models. Some are focused on specific tasks while others may be more general. 

The great chart below provides a number of examples of effective mental models. Some are easily employed for investment decisions while others may help with productivity and work flow. For example, the 80/20 prioritization mental model may not solve a complex problem but may help direct time and effort. 


For the new year, I will be trying to incorporate some new mental models in order to broaden my thinking of investment problems 

Wednesday, December 27, 2017

Mean reversion is not the same as contrary thinking. A big confusion for many managers


".... equities have to go down because the market has risen too fast" 

"The business cycle has to turn down since the recovery has lasted so long....."


"The bad performance of value investing will improve next year...."


"Financial condition have been very good which means that it will likely get worse in the future"


"What goes up must come down, and what goes down must come up!"



Disagreeing with the consensus or trend is not always contrary thinking or mean-reversion thinking. It is sometimes being different for the sake of being different. 

Too much of mean-reversion commentary especially at the end of the year is based on the idea that being different is insightful but it is often without the insight. By being contrary to the current market, the analyst can be considered prescient. Herb Stein may have stated that, "Things that cannot go on forever, won't", but that is not a forecast.

I take a different view that is grounded in the fundamentals of trend and momentum. Trending markets are likely to continue to trend. I always remember the adage from my old mentor John Henry, "Trends will last longer than expected." There will a reason for reversal, but without a strong narrative based on fundamentals, it is best to stick with the trend. Markets will get extended, but trying to find the top is difficult. Being cautious or risk averse is fine, but that is not the same as being contrary or basing a view on mean-reversion. 

So for the new year, read the forecast, take in the views, but remember that just stating that markets will mean-revert is not the basis for a good forecast. 

See,

Tuesday, December 26, 2017

Gresham's Law of Modeling Average - This is an important research idea on why there can be excessive volatility


No single model is perfect. It will have flaws. It will not capture all market behavior. It may fail during turning points. The parameters may be wrong or the values have not been measured correctly. 

Consequently, it makes sense to employ a number of models which can be averaged. The forecast from the average will do better than the forecast from any one model. An ensemble of models can offset many of the potential flaws from using a single model. Additionally it may make sense to adjust the parameters of any model as new information is gathered.

The literature on this issue is huge, yet a recent paper has me thinking about the impact of model averaging on the price process. See "Gresham's Law of Model Averaging" by In-Koo Cho and Kenneth Kasa in the American Economic Review. What is good for individual forecasting may not be good for the market as a whole. 

The authors show that if there are two mixed models, one with stationary and another with time-varying parameters, the fear of parameter instability can become self-fulfilling. The feedback from the time varying parameter model on the market will lead to instability and drive out the stationary model which in the long-run may still be correct. Learning or adapting may cause instability. 

The use of time varying parameter models can have the unintended consequence of generating excessive price volatility on the market being forecasted. Call it chasing the tail of parameter estimation error. 

Even though model averaging makes sense for any market participant, the result, when more forecasters follow this strategy, is that good models are driven from use and prices will bounce around with parameter changes. Trying to correct for estimation uncertainty may harm markets. There is no good solution for avoiding to this problem other than estimate the right model, but that is always easier said than done. Interesting food for thought as many quants recalibrate models at the end of the year.


Slicing the pie for better allocations using managed futures


At the end of the year, investors will review their asset allocation decisions. Often investors will think about their pie chart exposures to different asset classes and strategies. Too often the focus is on asset class allocations and not enough on strategy differences. The problem with asset classes is that correlations may change significantly in a crisis with the usual problem being a movement to one. Diversification is not present when you need it.

An alternative approach is to focus on strategy diversification which may not be as susceptible to correlation increases because strategies adapt and may not be wed to long-only exposure. A perfect example is managed futures which over the long-run has shown to have the characteristics of increasing return, cutting volatility, increasing the information ratio, and cutting drawdowns as seen through a simple study for close to 17 years.



Cutting the pie slightly differently through including strategy diversification may be an easy way to create value in the new year.

Saturday, December 23, 2017

Looking at commodities from both a macro and micro perspective - Different stories


The narrative for holding an allocation to commodities by looking through a macro factor lens may generate different conclusions than taking a micro market-specific perspective. While the macro perspective is good for setting longer-term strategic allocations, the micro perspective helps with tactical decisions on where or how to put money to work in commodities.



We are commodity bullish given the following:
  • Economic growth rising around the world. Expectations have also increased for 2018 and economic surprise indices are all bullish. Survey data shows economic optimism and for the US fiscal stimulus will provide a tailwind for growth.
  • The dollar has increased in value for 2017 which is negative for commodities, but forecasts for a continued dollar are mixed. We are more dollar positive, but the consensus is more mixed and the dollar is still off its previous high over the last three years.
  • Relative asset class value suggests that commodities are cheap relative to financial assets. The large gains in financial assets have not carried over to real assets like commodities. Housing has moved higher in many countries but commodity inputs suggest continued malaise. This relative return difference is likely to close. 
  • Inflation may not be hitting 2% central bank targets but the general trend and direction for expectations is tilted higher. The NY Fed Underlying Inflation Gauge (UIG) has reached the highest level since the Financial Crisis.
Our views on a micro basis are also positive but more tempered:
  • Trend and momentum indicators have been more mixed when looked at on a commodity-specify level although given the low correlation between commodities this is not always surprising. 
  • Some markets like oil have moved to backwardation which have a large effect on commodity index performance, but core agriculture markets are still in contango. We will note that commodity indices based on carry (long backwardation/short contango) had strong positive gains this year.
  • Inventories have declined but still are not indicating any commodity shortages.
  • CAPEX has fallen in many commodity sectors This investment decline has placed a constraint on future supply; nevertheless, this is a longer-term effect that will not affect tactical trading but will allow for tightening of the supply/production overhang.
A half way approach to commodity investing is to invest in a factor-based index instead of a volume/production weighted long-only index. Alternatively, scaling exposure into commodities is another approach for gaining access. Still, commodities are poised to offer better return potential for investors.


What we look for with trend-following beta - A simple set of rules


Trend-following as applied to managed futures has been around for decades, yet there is no universal agreement on what is or should be a trend-following benchmark that can serve as the strategy beta or as a trend-following strategy factor. A trend-following benchmark can be used to measure the factor beta of any manager. I can be defined as the core returns that an investor should expect from holding an investable portfolio in this strategy space. The benchmark returns should be expected performance from this strategy assuming there is no specialized manager skill.

Trend-following beta should not be a peer group of managers. A peer group may include specialized skill such as risk management, market selection, or filtering mechanisms and may be polluted by the use of a wider set of strategies than just trend-following. 

A peer universe can serve as a comparison but should not serve as the benchmark for any beta measure. There is nothing wrong with peer group comparisons, but it is not a well-defined strategy factor. It is a cluster of similar managers which may be highly correlated. This does not mean that peer group comparisons are not useful nor does it imply that managers should not try and beat a peer group. This means that any index like the SocGen CTA or BTOP50 index should serve not serve as a benchmark. A benchmark should be constructed through a set of rules that can be directly replicable and easily understood.

A trend-following benchmark index should rather represent a set of rules that can describe the investment strategy and can be employed to create a beta factor. We would like to outline what a trend-following benchmark should look like if it is to be used as the index for the strategy beta of any manager. I will note that momentum seems to have been more universally accepted and has a clearer definition through academic research. As an absolute return strategy that has not been created by academics, there is less clarity on trend-following rules definitions.

Keep it simple - A trend-following benchmark for measuring beta should be simple; easy to describe with how it is constructed and how and when it will generate returns. It should be investable and have characteristics that will be found to be useful to investors so they actually want to hold the benchmark index. A simple benchmark should not include filters that determine what trends to avoid. It is not "passive", but it has only a limited set of rules.

A limited set of markets - The gains from diversification are asymptotic. As the number of markets increase, the marginal gain from further diversification moves to zero; consequently, the number of markets that should be included in a benchmark should be adequate to provide the maximum diversification but limited so as to minimize transaction costs. 

The benchmark should be diversified - A good trend-following benchmark should include all major asset classes including equity indices, fixed income, rates, foreign exchange, metals, energy, and commodities. Nevertheless, there is a problem with determining the asset class allocation weights. 

Style consistent - If the benchmark is supposed to capture the returns from trend-following, then returns should only be generated from trend-following without any other investment styles or added features that are unrelated to trends. There should not be any filters that determine which trends to hold. There should not be a mixing of strategies between trend or momentum, nor should there be value indicators.

Timing range - The trend-following index should include a range of trend models since trends may have different time ranges. Nevertheless, trends employed should not be so short-term as to be distorted by liquidity or transactional cost effects.

Portfolio construction - The index should include all major asset class sectors and timeframes through an unbiased allocation procedure. Given the variety of markets and volume of trading, an allocation procedure that is based on volatility equalization is reasonable. Trading should be limited to the largest market so there will not be any distortions from size effects. Rebalancing should be well-defined.

Is there a single index that can serve as a trend-following benchmark? The jury is still out in terms of broad acceptance, but we believe there are some candidates. Nevertheless, there are more large managers who are offering low-cost stripped-down trend-following programs which they describe as a trend-following beta programs.  We think there is a place for these products for investors, yet there is also a place for alpha enhancers, albeit the burden is on these alpha enhancers to show that they can beat the benchmark index. 

If there is a defined benchmark, then managers can be compared based on their characteristics and not just peers. It is important, given the number of players in this space, to truly define and measure what is the actual trend-following beta or factor for a manager. Investors need to know whether a managed futures manager is a trend-follower or something else. 

Friday, December 22, 2017

Is a bond bust more likely than an equity sell-off? Look for alternatives


The major drumbeat of asset class overvaluation has focused on equities, but perhaps a scarier place to invest is holding long duration bonds. Both asset classes may be overvalued, but a close look at the economic fundamentals may suggest that greater concern should be with bonds.

1. Economic growth is biased upward - Current forecasts for US growth are still below 3% but last two quarters have surprised the market to the upside. Announcement surprises have all been positive for the last few months.  Survey data are all positive about economic growth. Generally, stronger growth translates into higher yields.

2. Central bank normalization - The Fed normalization, with reduction in its balance sheet and an expected three rate increases next year, places upward pressure along the yield curve. The curbing of bond purchases by the ECB next year and Bank of China attempts to tighten credit markets places upward pressure on global rates. 


3. Flat yield curve - The flattening of the yield curve from Fed normalization has the added impact of reducing the carry gain from holding longer-duration bonds. The can have the effect of reducing bond demand.


4. Inflation bottom headed higher - The inflation numbers may not be hitting 2% for PCE, the preferred index for the Fed, but the CPI is now at 2.2 up from a low of 1.6% mid-year. The NYFed Underlying Inflation Gauge (UAG) is at the highest levels since the Financial Crisis.


5. Debt financing strong supply - Debt financing continued in 2017 at a feverish pace as debt issuers hit the market before expected rate increases. 2018 will continue to see strong supply both from private and Treasury debt needs. The Fed will not be the buyer of last resort.


6. Risk premium bottom - Bond risk premiums continue to stay at unusually low levels. Any normalization will cause rates to rise relative growth and expected inflation.


7. Volatility of bonds - The MOVE bond volatility index followed the VIX and hit all-time lows in October. While volatility may stay low, the changing structural dynamics with central banks being sellers not buyers suggest that there is room for more volatility. 

In a less safe bond world, cutting duration is a good start for portfolio protection, but looking for diversification through other strategies like global macro/managed futures may be a better bet. 


Managed futures is not trend-following but it is close - Broadening the product spectrum has added strategy complexity


I would not be the first person to engage in the lazy thinking that managed futures are synonymous with trend-following. For many years, there was little wrong with using both terms to mean the same thing. The majority of managed futures are still trend-following. 

We ran a non-overlapping regression using five years of monthly data to measure the trend-following beta of the SocGen CTA index, a peer group index of managers. As the proxy for trend-following, we used the CS Liquid Beta managed futures trend-following index. The number suggest that for a widely followed peer index, the beta is about the same since 1998 at approximately .7.

However, the last year or two suggests that there is a growing gap within the managed futures strategy space that will not be picked up within the trend-following beta of a peer group of mostly large managers. 

The reason for the business strategy gap has been the growing marketing push to sell trend-following as a low cost quant strategy. We are now seeing some leading firms market their trend-following strategy at flat fees of 50 bps. The same firms may have other futures-based strategies at higher fees, but actually market a "pure" or "stripped-down" version of trend-following as a low cost product. 

This product pricing push has really changed the revenue model for these firms. There may be more money coming into managed futures but it is at lower fees; consequently, there has been a move to develop new strategies that are explicitly marketed as non-trend-following quant or trend-following plus. 

These products have added models attached to a core strategy. Complexity has been added to either justify higher fees or to differentiate product offering from the core trend product. Hence, it is becoming harder to analyze managers in this strategy space. Investors will have to implement some form of core-satellite between trend and non-trend managers to access the best managed futures alternatives.

Wednesday, December 20, 2017

Back to basics for trend-following - It is all about what it does to the portfolio


It does matter what an investment strategy will do on a stand-alone basis; however, it really matters what an investment will do when added to an already diversified portfolio. For any strategy allocation decision, it is all about the marginal contribution to portfolio return and risk. Most investors know this intuitively, but they often do not focus on the marginal portfolio contribution in practice. 

The charts below are from Credit Suisse Asset Management (CSAM) who has developed the CS liquid beta managed futures trend-following index which offers a low cost trend-following program alternative. It is a close proxy for a pure trend-following beta program. 

A simple way to measure the marginal contribution is to add trend-following to a 60/40 stock/bond portfolio while preserving the original proportions of the mix. This would lead to a 54/36/10 allocation if 10% is given to the trend-following program or a 48/32/20 allocation if 20% was given to the trend-following program. 

Whether returns are higher or lower will be situational based on relative equity and bond performance versus the trend-following program. In the last year, the allocation away from equities would have been a drag on performance, but depending on the program chosen, the fixed income reallocation would have generated about the same or better return. 

The real gain from adding trend-following comes from the diversification benefit which would have pushed the efficient frontier outward to increase the number of risk and return combinations.

Performance would still maintain the same pattern when trend-following is added to the portfolio, but the return lows would be smoothed-out and not as deep. The gains from reducing drawdowns will last for a very long time. Not losing money is still one of the best ways to ensure future success. Trend-following that will allocate away from losing positions is still the easiest active strategy for cutting drawdowns.

While trend-following may not have generated superior performance this year to a buy and hold portfolio of equities, we are starting the new year in less than two weeks and it requires asking a simple question. Would you prefer holding your existing stock/bond mix or would you like to gain some active exposure through trend-following. The trend-following programs will give your portfolio asset allocation tilts during times of uncertainty.


Sunday, December 17, 2017

Buy in a drawdown? Focus on future and not the past performance


Managed futures, as a hedge fund strategy, have moved off of its max drawdown since June, its worst drawdown in the last five years, as measured by the SocGen CTA index. For some investors this type of drawdown means an exit from the strategy; however, some of the broader data on manager selection suggest a different approach. The idea of being careful about making investment decision based on a drawdown is consistent with the mean reversion performance analysis of winners and losers. 

See, 


Some things to think about before using a drawdown as a decision variable:
1. Style performance is dynamic. The best (worst) style today may not be the best (worst) tomorrow. The real question is whether it has added to portfolio diversification.
2. Momentum strategies have been subject to steep drawdowns. The reason so many have not gotten on the momentum bandwagon has been the strong downdrafts in performance. If a deep drawdown has occurred, there is less reason for it to be sustained.
3. Trend-following success is conditional. If there is no price dispersion (trends), trend-following will not make money. Breaking down performance attribution is critical in a drawdown.
4. Trend-following does better the there is greater risk-aversion or a "risk-off" environment. The last year has been a risk-on period which generally means lower returns for managed futures. A drawdown should not be surprising and investors likely made money with their risky asset portfolio.
5. Managed futures as a long/short strategy will not have drawdowns based on out of favor asset classes. The turnover in the strategy suggests that any bad trade ideas have already been purged from the portfolio.
6. Many managers cut leverage and thus volatility in a drawdown. There can be a debate on the efficacy of following this risk management strategy but many managers will reduce risk exposure in a drawdown and thus limit future loses.
7. Data show that there is mean reversion in manager performance. See our post on the details of holding losers not winners on a go forward basis.

Bad performance should not be rewarded with slow response, but the reasons for the poor performance should be studies closely. Think of all of the due diligence required for a buy decision. The same careful analysis hold be done on an exit; however, the process may have to be done much more quickly when real dollars are at risk.


Buy the losers and sell the winners? Reverse your thinking and be careful about avoiding or exiting losers


The general view is that an investor should pick good managers who have had a track record of success, but a more nuance look at data suggests that buying when good managers underperform can be valuable. Whether past performance provides some indication on future success has been one of the key issues facing any investor. We now have an interesting perspective from Brad Cornell, Jason Hsu, and David Nanigian in their Journal of Portfolio Management paper, "Does Past Performance Matter in Investment Management Success"

The authors followed a simple rule that is consistent with the behavior of many investors. They looked at the previous three-year returns and then buckets the performance into winners and losers. They then tracked the performance over the next 36 months relative to a benchmark. What they found may surprise many investors. 

The losers did better than the winners. Hence, a switching strategy of selling losers and holding winners may actually lead to poorer performance. Now a consultant or advisor who advocates looking to increase allocations to losers may be viewed as crazy, but the data make a compelling case that an "off with their heads" approach to poor performance is financially dangerous.



The reason for this mean reversion is still not completely clear, but the flow of money into winners may affect performance is a leading candidate. Herding hurts the future performance of past since new money flow dilutes limited alpha.  

So what should be the key take-away from this research? Do your homework and make your judgements on manager allocations through criteria that look beyond performance. The authors suggest some criteria focus by other researcher such as: fund manager compensation, fund manager ownership and commitment of capital, a high active share, outsourcing of non-investment services, having PhDs in key portfolio roles, and a strong positive firm culture.

Some preliminary evidence suggests that this same behavior applies to hedge funds within the managed futures and global macro space. Our view is that performance is one measure of success, but it should not be the definitive criteria for any allocation decision. Know your manager beyond the numbers.









Saturday, December 16, 2017

Not buying the Fed package - The Fed, yield curve, and bonds


The take-away quote from Yellen this week, "The relationship between the business cycle and the yield curve may have changed." There was little supporting evidence for her view. It is the hope of the Fed that further rate increases which may further flatten the yield curve will not reverse the current course of the economy.

The curve has not inverted, which is the actual sign of a potential recession, but has only flattened, albeit the flattening has occurred at a fast and more consistent rate in the last year. Bonds still do not have a risk premium, expected inflation has not appreciably changed over the last year, and higher growth has not moved the real rate of interest higher. Long bonds are not following the script of moving higher with the Fed normalization.

A link between a flattening yield curve and recession may be just correlation and not causation, but there are good stories for why there is a link. First, a flattening yield curve reduces the profit potential for banks. Banks may be less important with financing, but the shape of the curve does matter for their profitability and lending behavior. Second, a flattening yield curve changes the risk profile for moving out on the yield curve. There is less term premium for holding duration. Investors will less likely hold risker assets when you can be compensated with a high cash yield. 

The meaning of the flattening of the US yield curve in the context of the Fed forward guidance is important. The Fed dot plot suggests that there will be three rate increases in 2018, another two or more in 2019, and likely more in 2020. Bonds yields suggest that traders do not believe the plots. The Fed has creditability with their longer-term forecasts. 

Additionally, bond traders still have in the back of their minds the view that an overvalued stock market will need Fed support sooner than later. Another reason to put less stock in three Fed increases for 2018. In a global context, a tightening Fed coupled with continued savings around the world suggests that dollar denominated bond demand is still high.



There are a couple of implications for global macro and managed futures players. First, the big down trend in bond prices is not upon us. Strong trend signals are still not apparent and bonds may not be a place for greater profits at this time. Second, the further resolve of the Fed suggests that opportunities in the rate markets are increasing. Trading short rates has been relatively dormant for year, but Fed trends have led to price trend opportunities. Third, the continued rate increases provide a nice tailwind for returns on cash. Cash rates are now covering management fees for many funds. There will be fixe income opportunities but more will be in the front-end of the curve.

Factor risk premium differ across countries - Make sure you are factor diversified globally


Equity factor risk premium ranks change through time. The best performing factor premium today may not be the best premium tomorrow even if there are long-term gains across major factors. Most investors would agree with this statement; however, the dispersion of factor performance is more complex.

The paper "Factor-Based Investing: The Long-term Evidence" by Dimson, Marsh, and Staunton does a great job of summarizing all of the work on the key factors used for investing. It is a must read and easily accessible for any investors, but there is one table that caught my attention. This is a table of the equity factor return premiums in the US and UK across time. First, it clearly shows that the ranking change through time, but looking at the overall rankings from 2008-2016 across countries was especially interesting. 



Notice that the worst performing factor in the US was momentum and the best performing factor in the UK was momentum. All of the other factors seem to have relatively similar rankings and returns. The largest return gap for a factor between the US and the UK is with momentum. The risk for momentum is with sudden stops or drops in performance which may be associated with crashes, but the US-UK gap over ten years is large. 

My conjecture from this table is that risk factors have to be diversified globally and that this is especially the case for momentum. Global macro managers who diversify around the world with equity momentum and trend-following are accounting for the fact that momentum is not the same across countries. Global diversification of risk factors makes for good portfolio construction. 

Friday, December 15, 2017

Commodities versus financial assets - There is value here




Equities are overvalued! Bonds are overvalued! In the minds of many investors everything is overvalued given central bank distortions, yet there may be an exception. Look at commodities. The difference between financial and real asset could not be larger. Financial assets have steadily moved higher while commodities have fallen or at best moved sideways relative to risky stocks in the last 5+ years. This relationship has applied to all equity indices around the world to varying degrees. 

One can argue that commodities are more closely tied to current economic growth while equities are tied to valuations of long-term discounted cash flows as one explanation. Equities have positive carry while commodities as measured through futures have seen mixed carry with market contango for the last few years; however, there are now switches to backwardation in some markets. The business cycle relationship with commodities suggests that these real markets peak late in the cycle not when there is a new surge in growth. Still, on a relative performance basis, commodities as an asset class looks like a better value.


  


Nevertheless, there is a word of caution when we recast the data relative to global stock indices. US equities have exceeded previous highs and shown the most consistent gains since the Financial Crisis (FC). Over the long-run emerging market (EEM) equities have shown the strongest performance, albeit levels are still below highs. Global stocks (EFA) still have a ways to go before reaching prior highs. Globally, the financial to real asset story is slightly weaker.


Commodities have been more susceptible to deep declines as seen in 2008 and again in 2014 and have not moved in the same manner as equities; however, stronger global growth and better relative valuation makes this a more attractive asset class alternative for investor consideration.

Thursday, December 14, 2017

How much machine learning is your quant using? Not clear, if you have not defined terms




The current buzzword used with quant investing is the term "machine learning". Many quants may like to appear smarter by peppering their strategy discussions with comments like, "We use machine learning to create new and enhance our existing models." Yet many investors don't fully appreciate that machine learning is a term that refers to a broad set of approaches to data analysis.  Many of these techniques have been around for decades. Machine learning can be an all-encompassing term. 

At the top of the machine learning taxonomy is the split between supervised and unsupervised learning. Supervised learning is what is done in most cases where there is inferring of a function from data input to a specific output. It involves training input data to reach a target outcome. Unsupervised learning is associated with searching and grouping of input data to find relationships even if there is no specific target output. Within supervised learning there is a breakdown between classification of events and the regression or mapping of sensitivities between input and outputs. Unsupervised learning can be defined as searching for clusters or similarity within a data set.


Understanding how managers go about their data analysis and which techniques they employ to tease-out information on market behavior is the real issue. How are they trying to classify data to find signals on return? How are they using regression to forecast or fit valuations to fundamentals? Or, how are they searching data without a specific relationship model in place? Do they use the right technique for the right problem? Will a different technique provide new insight on the same data? Investors have to ask why a specific technique will provide more useful results.

The key use of a broader set of machine learning techniques is that it can open up or restructure data sets to find new relationships and patterns that are not immediately obvious through linear regression, traditional time series, or simple rules. For example, the use of decision trees and categorical analysis may be helpful with finding non-linear relationships that may not immediately apparent in trends or regression. 

Given inexpensive computing power, large data sets, and new statistical techniques, new recurring patterns in prices may be found. Of course, the flip-side to these atheoretical approaches is that data mining is done to excess without regard to sampling bias or the power of the tests. This is why digging into the details of what a manager may mean by machine learning is so critical. If a manager cannot effectively explain the value of their techniques to an investor, then it not likely to these tools will do their job.