Share this Post on Twitter

Thursday, August 18, 2016

ETFs May Actually Make Weak Players Weaker

Mike Mauboussin's latest write up Reflections on the Ten Attributes of Great Investors is a must read for investors (while his recent podcast with Barry Ritholtz is a must listen). And while I broadly agree with his ten main points, I thought it was worth discussing details / providing an alternative take to his main point under the section 'What's Next?', which touches on the impact passive / ETF asset growth will have on the ability for active investors to outperform.

His specific thesis (#'s and bold mine):

Thirty years ago, index funds were less than one percent of assets under management, and today they (along with other passive vehicles such as exchange-traded funds) are about one-third. Think of it this way: (1) For you to have positive alpha, the industry’s term for risk-adjusted excess return, someone has to have negative alpha of the same amount. By definition, alpha for the market must equal zero (before fees).
So you want to compete against less-skilled investors because they are your source of alpha. (2) It is disadvantageous for you if the weak players flee the market (selling their stocks and buying index funds), or if the least capable professional investors lose assets to passive funds, because it means that only the smartest investors remain in the active game. The truth is that weak players, whom the strong players require to generate excess returns, are fleeing at a record pace.
To paraphrase, (1) the market is a zero sum game (relative winners only exist if there are relative losers) and (2) less skilled investors are going passive, resulting in the amount of negative alpha available for remaining investors shrinking.

Is the Market a Zero Sum Game? Yes and No.

I agree with the view that the market is zero sum when looking at dollar-weighted performance (i.e. if everyone is fully invested and there are no flows, then if some investors are up relative to the market, then others must be down, resulting in net returns equal to the market less fees). BUT investors are known to be horrific market timers, regularly underperforming the return of the funds in which they invest.

As a result, the negative performance gap between the performance of funds / ETFs and the performance of investors can be captured by fund managers and/or investors, meaning that it is theoretically possible that all funds outperform their benchmark, while all investors underperform the funds they allocate to (the zero sum nature should be viewed in time-weighted performance across all asset categories, rather than dollar-weighted performance within one category, as flows exist in the real world). This was the essence of my favorite investment read of 2016 Active vs. Passive Investing and the “Suckers at the Poker Table” Fallacy.

Are There Less Weak Players in the Market When They Go Passive? Sure Doesn't Seem So.

Weak players may simply be making poor investment decisions elsewhere. The chart below shows the performance of mutual funds and ETFs across a variety of U.S. styles (growth, blend, value), as well as market caps over the last five years per Morningstar methodology as of June 30th, 2016. Note that I think the total return and investor return calculation used by Morningstar is flawed* (see footnote below), but it is the only source I am aware and is used (fairly or not) throughout the industry (so I'll use it here, but if readers have a better source let me know). Note that ten year data was unavailable for 4 of the 6 ETF categories, though reported investor underperformance was similar in scale for large blend and large value. 

Using this calculation, the average total return of ETFs was higher than mutual funds across categories over the last five years, BUT ETF investors massively underperformed both the return of the ETF and the investor return of similar style mutual funds over the same time frame.

What Might This All Mean?

It means ETFs may actually make weak players weaker. ETFs have certainly enabled irresponsible investors to more readily negatively impact their performance through the following capabilities previously unavailable / less prevalent within mutual fund structures:
  • Trade asset classes intra-day and after market
  • Build negative carry portfolios through shorting
  • Invest easily with leverage in horrific structures
  • Performance chase in highly esoteric / niche areas of the market 
  • Become asset allocating tourists (vs manager selection tourists)
Investor returns in the chart above would indicate ETFs have seemingly ADDED to the alpha pool within these plain-vanilla categories, which does not include the more esoteric asset classes / structures that have already removed billions of dollars of investor capital from their wallets. As a result, the poor timing by ETF investors may make more alpha available for those institutions and investors that can act as a counter to this poor market timing.

But given most investors have underperformed within mutual funds as well, where has all of this excess performance gone? I need to think more about what I am going to say next, but what if it is has gone to the companies themselves in the form of stock appreciation and senior level stock compensation? If investors have been poor timers this cycle, then perhaps companies that have consistently bought back their own stock have been good market timers?

Footnote: Morningstar Total Return and Investor Return Calculation

I believe there are a lot of issues with Morningstar category total return calculations, as well as Morningstar category and fund investor return calculations.

Category total return calculation: see here for their methodology, but in a nutshell the calculation averages the total return for each fund within each style universe irrespective of asset size and benchmark; a tiny fund with poor performance holds as much weight as a huge fund with outperformance. As a result, it is theoretically possible for the average dollar weighted investment in funds to outperform the index even if the category underperformed.

As is the case of the Morningstar Intermediate Bond category… for the five years ending 6/30/16, the average fund underperformed the Barclays Aggregate Bond Index. BUT, the dollar weighted fund return outperformed the index and funds benchmarked against the applicable Barclays Aggregate Bond Index outperformed the index further. Narrowing the focus to institutional share classes added another 20+ bps to excess performance. Note in this analysis I used the most recent share class AUM, which may overstate the relative performance given investor performance chasing (i.e. AUM is likely overstated for the best performing funds).

Investor return calculation: I've written about the issue of comparing time weighted and asset weighted returns previously. There is an additional issue, similar to the one within the total return calculation outlined above, which is the calculation seems to average the investor return for all funds in each category. As a result, a tiny fund with tiny poorly timed flows may have an oversized impact to the category average. For example... if a $10 million fund received a $5 million outflow, which was then allocated to a much larger fund, the investor return of the small fund could be severely impacted by the outflow, while having an immaterial impact to the larger fund. Averaging the two at a 50/50 weight would mean the flow could have a huge impact to the category average, while being a wash in reality. Finally... investor returns for ETFs also seem to include flows related to short investors / hedgers within these ETFs, but my view is this is less of an issue as shorts that underperform still add alpha to the overall "alpha pool" in a zero sum world (i.e. shorts / hedgers that lose money have negative alpha... a boon to other investors).

Smell Test: In terms of whether the direction of the investor returns looks right, I did take a look at flows and returns within the Large Blend ETF category going back five and ten years. Over the last ten years, the slope of the regression between 12-month flows and the 12-month historical return was positive 10x (i.e. if returns were -10% in the previous 12-months, then the regression assumes outflows of -$10 billion over that same window). On the other hand, the slope of the regression between 12-month flows to the Large Blend category and 12-month forward returns was negative at -10x (i.e. if flows were -$10 billion, then the slope of the regression assumes future returns were positive 10%). In other words, investors zigged after markets zigged, then markets zagged, which would lead to poor investor returns.

Tuesday, August 9, 2016

What if Factors Rarely Matter?

Back in December I wrote that It's Generally Smart to Avoid Credit Risk outlining that more than 100% of credit's excess performance over time has come when the level of credit spread was extreme.

What if the same were true for well known investment factors?

Taking a Look at the Small Cap Premium

The chart below takes the average market cap of the 30% largest companies within Fama French data and divides it by the average market cap of the 30% smallest to form a ratio.

The next chart takes the above ratio and ranks them by percentile. For example, the 50th percentile is a 172x ratio, which is slightly above the current 148x ratio.

The Market Cap Ratio as a Predictor of Small Cap Relative Performance

Now is where things start to get interesting... taking the ranks in the above chart and plotting the average excess small cap forward performance by starting decile bucket (i.e. all ranks 0% to 9.99% are within bucket 0... 10% to 19.99% are within bucket 10....etc...), we can see a definitive pattern. When the ratio is low (i.e. there is not a large gap in the market cap between small and large firms), there is perhaps only a minimal opportunity for small companies to catch up to large caps leading to small cap underperformance given their higher risk; when the gap is high, there is perhaps a lot of room to catch up - leading to small cap outperformance.

The next chart shows the stand-alone forward performance of small and large stocks within the same buckets as the chart above, rather than the variance between the two. Note the relative performance by market cap ratio has been driven mostly by the performance (bad and good) of small caps when the ratio is low or high, rather than the performance of large cap stocks.

Potential Implication: What if Extreme Gaps Drive the Whole Premium?

Now is where I believe things get very interesting. An investor making an allocation to large stocks in 97% of all instances (i.e. making an allocation only when the market cap ratio was north of 425x which marks the highest 3% of all instances and only includes a portion of the Great Depression and the Internet Bubble), would have received 75% of the small cap premium since 1926 with significantly less volatility. An investor making an allocation to small caps only when the month-end ratio was north of 175x (the median) would have done even better than small cap stocks alone, returning 13.1% vs. the 11.8% of small caps since 1926.

Additional analysis is needed before any claims can be made, but if investment factors generally lead to consistent excess performance only at extreme levels, it questions the benefit of having a strategic allocation to these factors irrespective of the market environment.

Thursday, August 4, 2016

Consolidated EconomPic Posts

Bob Seawright of the great blog Above the Market, recently put together a consolidated list of his past posts which was a great way to find his older work. I thought it might be similarly helpful for new and old readers alike if I provided a list of links to various posts from EconomPic since coming out of blogger retirement just about a year and a half ago (I had more posts than I thought).

For those interested in a consolidation of the below EconomPic posts (as of March 2016) in an easy to read / printable pdf, please sign up with your email should you come across a pop up banner when visiting the blog or shoot me an email at Thanks for the support!



Friday, July 29, 2016

The Case for Hedge Funds / Creating an Ideal Liquid Alt

A hedge fund is simply a go anywhere investment vehicle that attempts to provide excess returns to cash with a low correlation to traditional asset classes (i.e. they are vehicles that attempt to provide alpha). Hedge funds and liquid alternatives have taken a lot of heat recently, much of it deserved, but in this post I'll outline the case they are being incorrectly evaluated. Specifically, this post will outline the benefit of a hedge fund that can provide excess performance to cash with low correlation to stocks / bonds even if it provides only a minimal level of excess return. In addition, I will share how an investor can effectively utilize a hedge fund (or liquid alternative) within a broader portfolio, which will touch upon why hedge funds should rarely be judged by their level of absolute performance.  

Why Have Even the Best Hedge Funds Disappointed Investors?

The below equity curve is for the actual returns of a hedge fund, which I'll label Hedge Fund X, that I pulled specifically because the since inception returns have:
  • Consistently been uncorrelated with those of stocks (0.13) and bonds (-0.10)
  • Been in excess of cash
  • Underperformed a 60/40 portfolio

In other words, Hedge Fund X has had very strong risk-adjusted performance, yet has provided only average absolute returns when compared with stocks and/or bonds. As a result, an investor that reallocated from stocks and bonds to fund the allocation to Hedge Fund X wouldn't have accomplished much. While the allocation did improve risk-adjusted returns of the overall portfolio, it came at a lower overall return and (as the chart shows below) it hardly moved the needle in terms of the return path. Given the amount of incremental due diligence required to make the allocation and the high fees paid (which can now make headlines for public investors), there may be buyer's remorse for the allocation (this despite the luck that was likely involved in finding a hedge fund that was able to produce such remarkable risk-adjusted performance ex-post).

The Case for Leverage: Hedge Funds as an Alpha Overlay

Rather than carving out an allocation to a hedge fund from stocks and/or bonds, an investor can make an allocation while maintaining their broader asset allocation through the use of leverage. The resulting economic exposure can be viewed as a traditional beta portfolio with the hedge fund as its alpha source. The structure could be created a number of ways, including selling out of stocks and overlaying stock futures over the existing bond portfolio (an example of a mutual fund that does that here). The end result is a strategy that provides an investor with a fully invested exposure to a stock / bond allocation using only 50 cents on the dollar, freeing up proceeds for Hedge Fund X with the remaining 50 cents.

Using a hedge fund as an alpha source is typically performance enhancing as long as the hedge fund outperforms the investors borrowing cost (i.e. cash rate, which is currently ~0%) net of fees. This may provide an investor the ability to reduce the risk of the underlying beta portfolio without reducing the expected return of the overall portfolio. In the example below, the stock allocation was reduced to a "less risky" 50% allocation, yet the combined portfolio was still able to outperform a 60/40 blend on both a risk-adjusted and absolute return basis due to the incremental return provided by the Hedge Fund X.

A performance comparison since the financial crisis may be more interesting as this has been a period in which the absolute return of Hedge Fund X has materially lagged. This has been driven by the strong performance of stocks and bonds (allocating away would have been a huge opportunity cost in hindsight) and due to the low level of yield on cash (see here for more details as to why that's a drag). Since the March 2009 bottom, Hedge Fund X returned only 4.4% annualized (underperforming the 18% return for the S&P 500 and the 4.8% return for aggregate bonds), yet the allocation utilizing Hedge Fund X as an alpha overlay continued to add value in the form of higher returns and lower risk when utilized with a 50/50 beta portfolio. 

Hoping for Innovation in Liquid Alternative Space

As outlined above, the real benefit of using uncorrelated return streams often comes if they are utilized as an alpha enhancer via an overlay, especially true in the current environment as financing costs have a low hurdle rate of ~0%. This benefit becomes a challenge for liquid alternatives that are structured as "cash plus" investment vehicles as record low cash rates are a drag to performance (rather than a low hurdle). As a result, my hope is the industry moves back towards an alpha / beta model that was popular pre-crisis where investors can gain access to cheap beta within the fund itself. Better yet, perhaps as an alpha overlay over a dynamic beta structure that has a historical track record of outperformance.

Friday, July 22, 2016

What Drives Momentum Performance?

Mar Vista Investment Partners has a really interesting research piece out The Price You Pay which has a great table outlining the benefit of an asymmetric return profile (i.e. having more market exposure during up markets than down markets).

It is a mathematical truism that superior down capture in negative periods provides more capital for compounding in the ensuing positive periods. Using S&P 500® Index monthly total return data for the last thirty years, the chart below demonstrates the expansive value created by preserving capital in the down periods even with subpar returns in the positive periods. Each column shows the ending amount of capital with $100,000 invested in the S&P 500® Index over thirty and ten years with various combinations of monthly up and down capture.

As shown in the table above, even a 10% difference in up / down capture can provide a material impact to returns and the path of returns.

This also happens to explain why momentum works... with a traditional momentum model where you are in either stocks or cash, you will rarely capture 100% of the market's upside (the strategy is not always going to be in stocks when the market is up), but you will by the same math almost always improve the downside capture. The chart below shows the rolling three-year upside and downside capture figures for a basic momentum model and shows momentum often, but not always, provides favorably asymmetry (i.e. upside capture > downside capture). Over the longer-term (50 years), the upside has been 70% vs downside of 56%.

It is the low downside capture during market sell-offs that has driven the strong relative and absolute long-term performance of this momentum strategy. Again... while there have been many periods in which this momentum strategy has underperformed, over the longer-term it has slightly outperformed the S&P 500 since 1966 (10.2% vs. 9.7%) and with a much lower standard deviation (12.7% vs. 18.9%). The chart below shows the tight relationship between three-year excess performance and three-year upside minus downside capture, which points to why momentum strategies have largely underperformed since the 2008-09 correction when markets have generally moved higher.

The momentum investment opportunity becomes even more interesting if you can pair the above return stream with a strategy that may improve up capture during positive market environments, but I'll save that for another day.

Tuesday, July 19, 2016

Why Does Crime Feel Exponentially Higher, When It's Materially Lower?

Every day we hear about some new horrific event taking place globally. The common view is that crime is rampant and the United States is headed in the wrong direction. But that view is not supported by facts. In a Citylab article related to the recent level of crime, it was noted:

"The story is actually better than we all anticipated it would be," says John Roman, a senior fellow at the Urban Institute's Justice Policy Center. "Violence is down a little bit. Property crime is down a lot… and all of this suggests that crime in America is continuing to move in the right direction.”
Over the longer-term, the decline in violent crime is even more notable. Despite the United States 21% population increase since 1995, violent crimes were down -35%, translating into a decline in violent crime per capita of almost 50%. Rape, murder, and robbery were all down over this period in absolute terms and down a lot in per capita terms.

Source: FBI

Why Does it Feel Like Crime is Getting Worse?

My theory is the perceived increase is related to the way information flow has made the world a much smaller / more local place.

One exaggerated example: if 150 years ago you lived in a 500 person town and your local newspaper maybe covered news from a 10 town population of 5,000, the sample size for your local crimes was that 5,000 person population. With a violent crime rate of 1000 per 100,000 people, you might hear about 50 crimes per year (or about 1 a week). If the crime rate were to move lower, given the same population, the decline would be felt via the reduced number of headlines in the local paper (see example A below). However, if a new paper came to town sharing news from a much larger base population, despite a material decline in overall crime, the number of crimes being reported might spike to 1 per day (outlined in example B).

Our Local Community has Grown Exponentially

Recently, we haven't just experienced a small increase to the size of our local population (from a town to neighboring towns, to our state, or even to our country), but the entire world (or the entire universe if you really want to freak yourself out). In addition, when news flowed through print, it was limited by what could be reported by space (only so much paper) and timing (only news could be reported as of a certain time each day before it became old news). Now, the cycle is infinitely larger and continuous.

If I was scared of being attacked by a homeless man, there is news for that. If I was scared my children would be abducted, there is news for that. Hell... if I were scared of the dangers of Pokemon Go, there is plenty of news for that. When the world becomes your local neighborhood, a 50% decline in the violent crime rate over the last two decades still means there are tens or hundreds of thousands more opportunities each day to freak out (note the y-axis in the chart below is in log form because of the exponential nature).

This won't stop any time soon. People have evolved over thousands of years to seek out scary news and freak out about things we perceive can harm us. Combine this evolutionary characteristic with the for profit nature of news organizations (fear sells) and strawmen politicians build utilizing some randomly occuring event as "proof" it happens all the time and you have a recipe for disaster.

They key is to remember that if properly weighted, only a tiny fraction of news would be bad news. Just imagine the sentiment if it that was how the world was accurately perceived.

Friday, July 15, 2016

The Case for Avoiding Bonds During Disinflationary Environments

I made a short-term case for bonds in a recent post given my view that low rates may be disinflationary, despite my view that they have a "horrific risk / return profile" over the longer-term. This post will highlight that what matters over the longer term is the level of inflation over the entire life of the bond (rather than the current inflation rate).

To drive this point home, the chart below outlines the ten year realized real yield of a ten year treasury (the nominal starting yield less the inflation rate over the forward ten years), as well as the realized real yield assuming the current 1.6% yield was the starting yield over each of these periods (going back 140 years).

It may be surprising to learn that the real realized yield was above 0% (i.e. treasuries provided a positive real return for an investor) in ~75% of these rolling ten year periods vs ~25% if each period had a starting yield of 1.6% (those numbers are ~80% and 0% - not once - assuming rates had started at 1.6% over the last 50 years). What may be more surprising was that the best time to have purchased bonds historically was when there were higher levels of inflation present. The chart below outlines the historical (i.e. backward looking) ten year inflation rate (x-axis) against the forward ten year realized real yield (y-axis).

Why have bonds been a better buy when historical inflation was high and worse when historical inflation was low or negative?

Similar to how the market is currently pricing in record low yields given the view of low inflation over the next ten years (likely based upon the low inflation we have experienced over the most recent ten years), the market has historically repriced the yield of Treasuries much higher when we've experienced high levels of recent inflation. Thus, when inflation mean reverted higher from low levels / lower from high levels, the rates (in hindsight) did not properly reflect the forward inflation environment. If we were to look at the predictive power of 30 or 40 year bonds, the predictive power would be even worse (good luck with that 0% yielding Japanese Government Bond).

Thus, despite the low level of historical inflation, current (historically) low levels of nominal yield suggest that bonds are in fact likely as wretched a long-term investment as they seem.

Thursday, July 7, 2016

Global Bonds are a Speculative Investment / The Case for Bond Speculation

Back in 2010 I had a post titled On the Value of Treasuries, where I outlined the total return for Treasuries was potentially greater than the (at the time) 2.71% yield given the very steep curve. The summary was that if the yield curve didn't change over a one year horizon, the total return for an investor was closer to 4.2% (the 2.7% yield plus and additional 1.5% coming from the ten year Treasury rolling down from a 2.71% yield to the 2.54% yield of a nine year Treasury). Not a bad trade at that time.

A Whole New World

Fast forward to today and you have a very different situation. Not only is the yield on the current ten year Treasury about half that 2.7% yield (1.39% as of yesterday), but the yield curve is very flat (the nine year Treasury yield is only 4 bps lower (1.35% as of yesterday) as the back-end has been smushed (a technical term).

As a result, the roll down math (assuming the yield curve does not change) is as follows:

Current yield + change in yield x duration = 1.39% + 4 bps x ~9 years = 1.75% total return

The Resulting Risk / Reward Profile for Treasuries is Horrific

As I'll outline with some simple bond math, a lower yield and a limited roll benefit makes the case for Treasuries much more speculative in nature... either speculative that rates in the United States will continue to move towards or through 0% (we've seen it in places such as Japan, Germany, and Switzerland) or speculative that cash will continue to yield less than the current 1.39% for years to come.

What we do know with certainty is that bonds will provide a nominal return roughly equal to their yield over a period of time roughly equal to their duration. In the case of Treasuries, that means with certainty Treasuries will provide a cumulative return close to:
  • 10 Year Treasury: (1 + 1.39%)^9-1 = 13.2% over the next 9 years
  • 30 Year Treasury: (1 + 2.15%)^22-1 = 59.7% over the next 22 years 
That doesn't mean Treasuries won't provide an outsized return over the next year, but any return in excess of its yield is like squeezing blood out of a turnip limiting its future return potential. The chart below outlines the potential returns for the ten year Treasury following various levels of 12-month performance. This short-term return could, in theory, be 15%, but that would just mean forward returns for the remainder of the duration will be negative (again... the cumulative return cannot move away from that 13.2% figure).

The Risk / Reward Profile for Global Bonds are Even Worse

Japanese Government Bonds (JGBs) show how much juice has already been squeezed out of some global bonds. There is a 40 year JGB that started 2016 yielding 1.42%, which now yields just 0.08%. The below chart uses the same framework as the chart above, but for the 40 year JGB at the start the year.

The result is a monster 50% return year-to-date (even more in US dollar terms given the yen rally), but this comes not at the expense of most of the returns for the next 29 years... ALL of the return for the next 29 years. In other words, to invest in 40 year JGBs effectively yielding 0% you are speculating that yields will move further negative or that cash rates will remain negative (on average) for a period that may be close to (or longer than) the rest of your life.

The Case for Speculation / Low Rates May Be a Self-Fulfilling Prophecy

All of that said, the challenge in this current market is that while the risk / reward profile of global bonds is horrific, speculation of even lower rates may turn out to be self-fulfilling if ZIRP is not partnered with increased fiscal spending.

Why? Because the juice has effectively already been squeezed out of the turnip, meaning future returns in global bonds are gone. This "should" have provided a bump to the global economy and global inflation by taking all the returns (and consumption) and pulling it forward, but it has not.

Why? Perhaps these gains have accrued to institutions that own these bonds (central banks, insurance companies, pensions, etc...) that won't spend it / are already on the hook for liabilities in excess of their assets, while investors who already lack the belief forward stock returns will be near the 8-10% they have historically provided, now project their future interest income from their cash or bonds to be at or below 0%.

The result is the need for individuals to save MORE, not less, for retirement, for a house, for a car. With no institution (corporate, government, etc...) willing to spend this increased savings in the form of investment (which seems like a smart thing to do as the hurdle for a positive NPV project is effectively 0%), deflation becomes a much larger possibility... in turn creating the possibility of even lower rates.

Tuesday, July 5, 2016

How the NBA Destroyed League Parity by Increasing Compensation Parity

The Economist has an interesting article If You Can't Beat 'Em, Join 'Em on the NBA's disaster of a collective bargaining agreement "CBA" that has effectively ruined the chance of parity in the NBA, something it was meant to foster (hat tip Lawrence for the article) and set the stage for Kevin Durant joining the 73 win Golden State Warriors (note: I am a huge Warriors fan and still not happy that he left Oklahoma City from a basketball standpoint... though I am excited to watch Durant play more often).

Two of the main issues outlined:

1) Players are entitled to 51% of all basketball related revenue

Under the current CBA, the players are entitled to 51% of all basketball-related income. So when new money comes in, the team salary cap goes up—and when a lot of new money comes in, the cap goes up a lot.
2) The max any player can receive is capped (at roughly $24 million, depending on service time which I'll ignore)
That gave every team an extra $24m of cap space to sign whoever they pleased—an amount that just happens to be nearly as large as the individual maximum. 

What Does this Mean to the Compensation Structure of the NBA?

In simple terms... each team NEEDS to spend a certain amount of money (I say "needs" as the maximum amount each team can pay is likely below the clearing value if it were a free market) and the amount of money that can be paid to any individual player is capped. My guess is the result is players in aggregate make less, but that non-stars make much more (with the decrease being more than 100% compensated by the stars).

The below is a framework for my guess as to how players would be compensated with no collective bargaining agreement (i.e. a completely free market), with a cap of the current $94 million per team with no player cap, and the current cap of $94 million per team with each player being capped at ~$25 million / year (in the real world there are a lot of nuances in these numbers and they aren't meant to be exact).
  • No CBA: in a free market, teams will simply pay more for talent as a whole (which is what we saw before there were huge penalties invoked in the latest CBA to team's that paid more than their allotment), which would also allow rookies to make more than the limits the league has imposed. 
  • Team's (not players) capped: there has been a lot of research already pointing to stars being underpaid in the current structure due to the max contract each player can receive. Given only 5 players can play at any time and the fact that one player can turn a team from mediocre to the world champs (unlike in a sport such as football), true stars should make multiples more than anyone else and would likely receive the marginal dollar if an owner had to choose.
  • Team's capped + max player contract: if a player would have otherwise made more in % of a team's payroll in a capped structure, then by definition limiting these stars to a certain $$ amount would increase the amount all others could / need to receive. 
Throw in the additional detail that team salary levels spiked when current stars were already locked into below market rate contracts and you have all the ingredients you need for massive overpaying of below average players.

Back to the Economist with the impact...
The real lesson from Mr Durant’s decision is the same as the one provided by Mr James’s original move to Miami: that the NBA’s CBA is a train wreck. At the very least, the maximum contract needs to go—it reduces the entire art of team-building into a sycophantic exercise of courting superstars who cannot be paid what they are worth.
It should be no surprise that if you can't compensate a player much closer to what they are worth if they stay (or in other words... penalize a player more for leaving), they will be more likely to leave an existing team for other forms of compensation (including improved odds of a championship, a more ideal living location, friendship, other forms of compensation, etc...), all of which are more likely only at a handful of teams in the league... including Golden State.

Thursday, June 30, 2016

The Case for Momentum in Expensive Markets

Charlie Bilello, one of my favorite follows on Twitter, analyzed the relationship between market valuation and future returns (over various time horizons) in a recent post Valuation, Timing, and a Range of Outcomes. The post contained some very insightful tables, such as the one below, where he shows that valuations matter... if you pay less for stocks, you will generally be provided with higher returns (on average) over almost all time frames.

The Case for Momentum

In a previous post Valuations Do Matter (Even Over Shorter Time Frames) / Momentum Driven Valuation Timing, I highlighted a similar point and in addition took a look at how stocks performed at various valuation levels when 12-month returns had been positive or negative. The takeaway (highlighted in the table below) was that market returns were generally strong when stocks were:
  • Cheap (with positive or negative momentum)
  • Expensive with positive momentum

To bring this full circle, the tables below replicate Charlie's analysis, but also compares those results with the average forward return for a momentum strategy with the following rules (note the returns in the table below differ slightly from Charlie's - not sure what data he used, but I used data from Ibbotson's):
  • If the CAPE of the S&P 500 was within the bottom 50th percentile (which happens to be less than 17x), allocate to stocks; otherwise...
  • If the S&P composite had a one year backward looking return that was positive, allocate to stocks 
  • If the S&P composite had a one year backward looking return that was negative, allocate to cash (t-bills)
Click the table for a larger view

The case for trend following when markets are expensive becomes abundantly clear when viewed in chart form... when markets are cheap, an allocation to stocks resulted in returns that were on average exactly the same as a buy-and-hold strategy over the short-term and returns that were broadly in-line with those of a buy-and-hold over longer periods. When markets were stretched, momentum protected an investor from severe drawdowns over shorter periods and allowed returns to on average compound over longer periods.

At a current CAPE in the U.S. approaching a level that would put it in the top 10% of most expensive levels over this time frame, a momentum strategy may prove to be a good mechanism for investors to time exposure to an expensive U.S. market.

Monday, June 13, 2016

The Brutal Math of a 60/40 Portfolio

Think only a bear market can keep returns of a 60/40 near 0%... think again.

Given the huge opportunity cost of allocating to cash or bonds at current yield levels, even generally optimistic return assumptions for stocks are enough to keep portfolio level returns near 0% real. The goal of this post is to set the stage for a future post where I hope to share potential solutions that may improve potential returns with a similar risk profile as a traditional 60/40 and to set proper expectations of what a 60/40 allocation dragged down by low yields may provide.

After-tax real return forecasts (see below for the formula used in the calculation)

  • Let's say you assume stocks will return 6% nominal going forward. 
  • After tax returns (assuming gains are taxed at the more favorable 20% capital gains tax rate) = 4.8% (6% x [1 - 20%]) 
  • After tax after inflation returns assuming a forecasted 2% inflation rate = ~2.8% (4.8% - 2.0%) 

  • Bonds will generally (best case scenario) return their yield (current yield to worst of the Barclays Aggregate Bond index = 2.0%) 
  • After tax returns assuming the less favorable rate applied to coupons (and a 35% tax rate) = 1.4% (2% x [1 - 35%]) 
  • After tax after inflation returns assuming the forecasted 2% inflation rate = ~-0.6% (1.4% - 2.0%) 

  • 60% Stocks = 2.8% x 60% = 1.68% contribution 
  • 40% Bonds = -0.6% x 40% = -0.24% contribution 
  • Total return = 1.45% real 
Throw on the ~1% fees many financial advisors charge and/or the lower yields many investors are accepting by taking less duration risk / diversifying U.S. bond exposure to even lower yields abroad and an investor may break through the 0% threshold even with a 6% stock forecast. This coming from an allocation that has a historical standard deviation of roughly 10% over time.

Initial takeaways 

The math above outlines the importance of:
  • Shielding returns from taxes whenever possible 
  • Keeping fees as low as possible (or ensuring you get something for your fees)
  • Seeking alternative sources of return (whether through allocation or alternative asset classes that now have a very low hurdle rate relative to bonds to be included)
  • Minimizing an allocation to negative real return asset classes

Tuesday, May 17, 2016

Can We Predict Forward Alternative Investment Performance?

My friend Ben from A Wealth of Common Sense poses the interesting question, How Should Alternative Investments Be Benchmarked? Please go read his post for a number of interesting thoughts on that topic. In this post, rather than rehash his arguments, I'll go a different direction and articulate what drives the performance of alternatives (i.e. hedge funds / liquid alts) to see if we can predict forward performance (which in a backwards way, may provide some insight into how an investor might think about benchmarking performance).

Low Interest Rates = A Headwind for Absolute Hedge Fund Performance

What's interesting to me is that one of the main reasons I regularly hear why investors are allocating to hedge funds, is actually a reason why hedge fund performance has been so disappointing. Back to Ben's post for a specific example in the form of a comment from his reader (bold mine).

Interest rates are too low and stock market volatility is too high so we have to hold some alternatives in our client portfolios. 
Low interest rates are actually a reason not to own alternatives. People seem to forget that alpha is a "cash plus" return stream. The excess performance of a hedge fund (with 100% of returns driven by alpha) will be a certain %, which added to the cash rate gets to the total return generated by the hedge fund. The same skill that generated a 10% hedge fund return when cash rates were 5% (5% alpha + 5% cash), only generates 5% when cash rates are 0%. As a result, all else equal, the lower the cash rate, the lower the relative performance of hedge funds.

Predicting Alternative Investment Performance

While interest rates are an important consideration when making an allocation to an alternative manager, they are not enough to predict future relative performance vs a traditional stock / bond allocation. One key is to look at the level of risk premia (both stock and bond risk premia).

Risk premia is:
The difference between the expected return on a security or portfolio and the "riskless rate of interest".

In my analysis I calculate the expected return on stocks and bonds as follows:
  • Stocks: CAPE yield (1/CAPE) - Source
  • Bonds: Long Term Bond Yields - Source
I then subtract out the riskless rate of interest (which I define as the average 7 year forward t-bill yield) to get the premia, while the 40/60 blend is a 40% equity risk premium / 60% bond term premium blend (given the average hedge fund beta is ~0.40). In this analysis, I forecast future cash rates moving up 50 bps / year to 2% and staying there (more on this later) to get the stock and bond term premia for forward years that have not yet occurred.

In terms of predicting alternative fund vs. stock / bond performance, the opportunity cost of allocating to a hedge fund is largest when these premia are high and lowest when they are low (or negative). As you can clearly see from the chart above, stock and bond risk premia were very low in the late 1970's / early 1980's when cash rates were elevated, high in the mid 1980's when Volcker was able to control inflation pushing down cash rates, subdued in the 1990's when stock valuations got extended, then huge in the mid to late 2000's after the financial crisis that pushed stock valuations lower and cash rates to 0%.

Given the above framework, you can see how the mid 1990's were a great time to be in hedge funds from both an absolute perspective (cash rates were high) and relative perspective (the equity risk premium was low), while the exact opposite situation has been true for the last 7+ years. The chart below takes the above starting risk premia levels since 1994 and plots them against the relative forward performance of the Credit Suisse Hedge Fund Index (I can only get returns starting in 1994) vs. a 40% stock / 60% allocation.

What Can We Predict from Here?

Given this analysis, it appears the opportunity cost of hedge funds is once again approaching "fair value" given the much lower bond term premium, BUT an allocation will be largely dependent on an investors view of the direction of cash rates. It is less important to relative performance whether cash rates are currently low, but whether they will stay low. Should they move much higher than the 2% modeled in my analysis, then now may actually be a good time think about alternatives.

Sunday, May 15, 2016

The Smoother "PATH": PutWriting At The High

The analysis presented below combines two separate frameworks that were previously outlined:

The first post outlined how avoiding bear markets (by only holding equities when they were near an all-time high) has lead to very strong risk adjusted returns, going so far to show the great performance of a strategy that only allocated to stocks when they were at an all-time high. The second post outlined what an investment in put writing provided and pointed out the surprising fact that selling puts on the S&P 500 has actually resulted in better risk-adjusted returns when markets were calm than when volatility was heightened (and premiums were higher). I recommend reading either / both if interested in more detail.

When Does Put Writing Make Money?
Selling puts make money when the premium collected is greater than the decline in the market (relative to the strike price) from the time the puts were sold to the time the puts expire (i.e. ignoring financing costs if you sold an at-the-money put, collected a premium worth ~5% of the notional value of the S&P 500, and the S&P 500 went down less than 5%, the trade was profitable). Thus, despite the lower premiums collected, selling puts more consistently make money when market volatility is low because the market is much less likely to decline (i.e. selling insurance to safe drivers is more lucrative even if you charge a lower premium).

Going one step further, the chart below shows all forward one month returns for the CBOE PutWrite Index and the S&P 500 Index only for periods when the S&P 500 reached an all-time high the previous month-end going back 25 years.

Highlighting the consistency of returns for both the CBOE PutWrite Index and S&P 500 when the previous month-end value of the S&P 500 was at an all-time high:

CBOE PutWrite
  • 11.1% annualized geometric returns
  • 4.6% annualized standard deviation
  • 2.9% annualized downside deviation
  • Positive 79% of the time
S&P 500
  • 8.5% annualized geometric returns
  • 10.3% annualized standard deviation
  • 4.8% annualized downside deviation
  • positive 60% of the time

The PATH Model
The model below is a simplified / lower risk version of one I've kicked around for a while, but it has resulted in an interesting historical path of returns.

The rules:
  • If the S&P 500 ended the previous month at an all-time high, allocate to the CBOE PutWrite Index (PATH = Putwrite At The High)
  • Otherwise, allocate to Aggregate Bonds
  • Levered version = 2x levered allocation to the CBOE PutWrite Index at-the-high financed at the applicable cash rate

The caveats of how this will perform going forward should be pretty obvious, namely that the path of the S&P 500 is hugely important.
  • If there is no new S&P 500 high, the strategy will simply sit in bonds (and yields are much lower)
  • If there is a huge market downturn in the period that puts were sold, that loss may take a LONG time to make up (especially at low yields)
Caveats aside, the relative historical returns of the non-levered model were ~2% higher annualized than bonds with an almost identical risk to the index, while the 2x levered version provided returns that were "stock-like" (~4% higher than bonds), but with a 60% lower standard deviation and a 90% lower max drawdown than the S&P 500.

At some point in the likely distant future, I hope to put out a white paper with a much more in-depth background and details of the broader model. For now, please feel free to reach out to me with any thoughts on Twitter @econompic.

Wednesday, May 4, 2016

Growth or Value in a Low Growth Environment?

Financial Advisor Magazine recently published an article by the CIO of LPL titled 'Value Comeback' making the case for Value. There were some interesting points in the article connecting the recent growth outperformance with lower interest rates and/or oil, but the following point on low growth being a driver of the growth outperformance did not make much sense to me.

One of the main arguments against value (and in favor of growth) in recent years has been the slow global growth environment. When there is not a lot of growth in the economy or corporate profits, then it logically follows that the market would pay a premium for the companies that are generating growth (what we have referred to as motorboats, which can grow without a macro tailwind, as opposed to sailboats, which need economic growth to grow). 
The data support this. Over the past 25 years, when economic growth is slow (real gross domestic product [GDP] below 2.5%), growth outperforms value by an average of 4.1%, and beats value two-thirds of the time.
This caught my eye because in a previous post I noted that despite the outperformance of growth over the past 3, 5, and 10 years, it wasn't multiple related (i.e. investors have not paid a larger premium for growth stocks). In fact, growth stocks have gotten relatively cheap by the measure of forward P/E (the current premium is about 20% for growth vs. the 30 year average of ~40%). In addition, I always assumed that growth did better during strong periods of high growth because that's when optimism tends to be highest and growth stocks become bid up during that exuberance (i.e. the roaring 90's).

So I ran the numbers for the same period that was reflected in the article, looking at Russell 3000 Growth and Russell 3000 Value (all cap indices) as my growth and value proxies. I then annualized the performance for each quarter when real GDP was < 0%, between 0% and 2.5%, between 2.5% and 5.0%, and 5%+. The results show value has historically done better in all environments EXCEPT when growth was quite high (above 5%).

Much of the historical outperformance of value did coincide with much higher valuations for growth stocks, so past outperformance of value vs growth may not happen even if growth remains sluggish going forward. In addition, the depressed price of commodities (due to the global slowdown) may make value (where most energy companies are classified) as the place to be if the economy expands quickly, However, it does clearly show that economic growth has been good for stocks in general and high levels of economic growth have historically been especially good for growth stocks.

Friday, April 29, 2016

Know What You Own: Alternative Funds Edition (Streamlined)

For the full wonky version of the below, please go here.

Below are the objectives / investment strategies of two different “alternative” funds pulled from the prospectus and/or annual report for each, along with high level details of how they were actually positioned as of their most recent semi-annual reports on 12/31/15. In this post I'll leave historical performance out of it (one has been horrific, one has been solid - which makes sense when you realize they are opposing strategies), as this post is meant to highlight the importance of looking under the hood, only allocating to strategies that you understand, and ensuring that the manager follows what has been outlined in their prospectus. This is especially important when it comes to alternative funds with less defined limitations (though in the case of Fund A... they seem to simply ignore these limitations).


Fund A: 
  • The Fund seeks to achieve long-term capital appreciation, with added emphasis on the protection of capital during unfavorable market conditions (page 1).  
  • The total notional value of the Fund’s hedge positions is not expected to exceed the value of stocks owned by the Fund (page 3).
Fund B: 
  • "Always hedged, all the time, using put options" (source)
  • "With no reliance on market timing or stock selection" (source)
To summarize, Fund A states it it can not have negative market exposure, while Fund B is described as always being hedged to the market (i.e. implies a 0% market exposure).


The chart below is my attempt to simplify the payoff structure of each Fund inclusive of all the options they have bought or written (go here for a full breakdown of the fund positions). Fund A sold deep-in-the-money calls that effectively neutralized the stocks held, leaving only the puts (meaning it is short the market). Fund B is a bit more complex, but is long the market on the way up (though less than 100%) and exposed to the market (though less than 100%) on the way down (with a relatively neutral position when the market is down ~5-10%).

  • Fund A states it can not have market exposure of less than 0%, yet was materially short the market 
  • Fund B implies it is has 0% market exposure, yet was materially long the market
So the two funds basically have:
  • The exact opposite stated investment strategies as one another
  • The exact opposite positioning as one another
  • The exact opposite positioning as their own stated investment strategy
And we wonder why there is investor confusion / disappointment?

As an aside... I find Fund B's strategy interesting.

Know What You Own: Alternative Funds Edition

Warning... I got way too wonky in this post. If you want a streamlined version of the below, go here.

Below are the objectives / investment strategies of two different “alternative” funds pulled from the prospectus and/or annual report of each, along with detailed analysis of how they were actually positioned as of their most recent semi-annual reports on 12/31/15. In this post I'll leave their actual performance out of it (one has been horrific, one has been solid - which makes sense when you realize they are opposing strategies), as this post is meant to highlight the importance of looking under the hood, only allocating to strategies that you understand, and ensuring that the manager follows what has been outlined in their prospectus, This is especially important when it comes to alternative funds with less defined limitations (though in the case of Fund A... they seem to simply ignore these limitations).


Fund A: 
Objective: The Fund seeks to achieve long-term capital appreciation, with added emphasis on the protection of capital during unfavorable market conditions. It pursues this objective by investing primarily in common stocks, and uses hedging strategies to vary the exposure of the Fund to general market fluctuations (page 1).  
Investment strategy: The investment manager expects to intentionally “leverage” or increase the stock market exposure of the Fund in environments where the expected returns from market risk is believed to be high, and may reduce or “hedge” the exposure of the Fund to market fluctuations in environments where the expected return from market risk is believed to be unfavorable (page 3). 
To make it abundantly clear how this fund defines leverage and hedging, the prospectus states.
Leverage: "The maximum exposure of the Fund to stocks, either directly through purchases of stock or indirectly through option positions is not expected to exceed 150% of its net assets" (page 3). 
Hedging: "The total notional value of the Fund’s hedge positions is not expected to exceed the value of stocks owned by the Fund, so that the most defensive position expected by the Fund will be a “fully hedged” position in which the notional values of long and short exposures are of equal size" (page 3).

Fund B: 
Objective: The Fund seeks income and growth of capital (page 1).  
Investment strategy: "The Fund's core strategy has been and will always be to purchase an underlying hedge at 100% of the notional value of the underlying through corresponding LEAPS put options and proportionally write shorter-term options against the long underlying equity ETFs and LEAPS puts. Premiums received from writing options represent income-type positions that are designed to take advantage of time decay and help pay for the cost of the hedge"(page 2). 
Elsewhere, Fund B is described as:
  • "Always hedged, all the time, using put options" (source)
  • "With no reliance on market timing or stock selection" (source)

To summarize... fund A states the fund can have exposure ranging from 0% to 150% (i.e. cannot go short), while Fund B is described as always being hedged the the market (i.e. implies a 0% market exposure) and uses the time decay (i.e. they are long theta) to pay for the hedge.


While I hoped to stay out of the weeds with regards to option pricing, the tables below break down the fund exposures inclusive of the options bought or written as of 12/31/15. While funds often mark options at market value (i.e. how much you can buy or sell them at that day's close), economic exposure is what matters to an investor (i.e. if the market moves up or down, what is the impact to the portfolio). As an example, stock futures are marked-to-market every day, meaning they have a market value of ~$0 at the end of the day. Yet I think we can all agree that a fund with 3x notional exposure to stocks (i.e. triple levered ETFs) have 300% exposure to the stock market, not 0%.

To calculate the real exposure, you first need to calculate the notional exposure of the options and delta adjust them to account for how in or out of the money the options are (delta is the degree to which an option is exposed to shifts in the price of the underlying asset). Walking through the table columns from left to right...
  • Contracts: # held or written as of 12/31/15
  • Expiry: when the option contracts expire
  • Index Value: the value as of 12/31/15
  • Strike: the level at which the stock is in- or out-of-the money; puts are in-the-money when they are below the strike, calls when they are above
  • Notional: contracts x 100 (notional value of $100 times the index value per the terms of these option contracts)
  • Delta: the degree to which an option is exposed to shifts in the price of the underlying asset (a deep in the money call option has a delta of 1 meaning it provides the same exposure as the underlying stock market, while a negative sign indicates it is short that exposure which results from buying puts or selling calls); note I assumed an 18% volatility figure to calculate the options for all periods, which is right around where the VIX was as of 12/31/15 as is close enough for this.
  • Delta Adjusted Exposure: this is the economic exposure that investors are actually exposed to

Now let's take a look at what exposure the two funds had as of 12/31/15 (if anyone sees an issue with any of my calculations, please let me know).

Fund A (source)

Delta adjusted option exposure of -$900 million relative to the $600 million market value of the fund's stocks as of 12/31/15 = net -$300 million market exposure (i.e. it was 50% short the market as fund AUM was ~$600 million).

Fund B (source)

Delta adjusted option exposure of around -$500 million relative to the ~$1.27 billion market value of the fund's stocks as of 12/31/15 = net $777 million market exposure (i.e. it was ~60% long the market as fund AUM was ~$1.3 billion).

Long Option Exposure

Short Option Exposure / Net Fund Exposure

The chart below is my attempt to simplify the payoff structure of each, ignoring the time decay of options held (time decay is a huge drag on Fund A and a substantial tailwind for Fund B). Fund A sold deep-in-the-money calls that effectively neutralized the stocks held, leaving only the puts and a net short position. Fund B is a bit more complex, but is long the market on the way up (though less than 100%) and exposed to the market (though less than 100%) on the way down (with a relatively neutral position when the market is down ~5-10%).


  • Fund A, which states it can have market exposure between 0% to 150%, was materially short the market 
  • Fund B, which implies it is has 0% market exposure, was materially long the market
So the two funds basically have:
  • The exact opposite stated investment strategies as one another
  • The exact opposite positioning as one another
  • The exact opposite positioning as their own stated investment strategy
And we wonder why there is investor confusion / disappointment?

As an aside... I find Fund B's strategy interesting.