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Tuesday, April 12, 2016

What You Pay Matters Less than What You're Paying For

Patrick O’Shaughnessy has a great post, The More Unique Your Portfolio, The Greater Its Potential, outlining how active share is what drives the level of potential before fee excess return for an active manager. If you allocate to active managers... go through it twice. As Patrick notes:

If there is a lot of overlap between your portfolio and the market, there is only so much alpha you can earn. This is obvious. Still, when you visualize this potential it sends a powerful message. Active share—the preferred measure of how different a portfolio is from its benchmark—is not a predictor of future performance, but it is a good indicator of any strategy’s potential excess return.
In other words, active share is an important metric as it shows what an investor is actually paying for (especially true now that the cost of beta is essentially zero). So, while an investor still needs to fully understand and believe in an active manager's philosophy, process, and discipline, the cost paid may be less important in isolation. What may be more important is the cost paid relative to what you are paying for.


You don't always get what you pay for

Using Morningstar Large Blend category data (the most plain vanilla of the plain vanilla), the below charts look at the relationship between fund expenses and active share for funds with active share > 20 to see what an investor is actually paying for. I narrowed the universe down further to funds benchmarked against the S&P 500 and then I did my best to strip out funds with style tilts (i.e. there were some growth, value, and dividend funds that fell in the category). One issue that remains is the below is screened by oldest share class to exclude duplicates, so there is some apples to oranges comparisons going on in terms of share class (though almost all of the funds are A share).

The first chart highlights the weak relationship between expenses charged and active share provided. An extreme example that was stripped out of the analysis that I came across was an S&P 500 index fund charging 1.60% with a 4.75% load.


The second chart compares the expense ratio against expenses normalized for active share (i.e. expenses charged divided by active share). Given the weak relationship between expenses and active share from the first chart, it should be no surprise that higher expenses generally mean higher normalized expenses too. This is a reason why funds with higher fees are less likely to outperform than funds with lower fees... investors are generally not getting a higher active share product for those higher costs.


Things get more interesting when you compare normalized expenses against active share. Here you can clearly see that the normalized expense ratio generally moves lower as active share increases. In fact, some of the cheapest normalized funds are those that have a much higher active share and may charge a slight premium.



Taking advantage of the "fungibility" of funds

While active share is a good indicator of a strategy's potential excess return gross of fees, an investor may not want to take as much relative risk or pay the fees embedded in the highest active share products. The good news is an investor can create a lower cost / lower active share solution through an allocation to higher active share managers and index funds... even if the cost may initially appear slightly higher in absolute terms.

For example... assuming an investor believes the capabilities of manager A and B are identical and has a 20% active share target. Yet:
  • Manager A costs 50 bps for 20% active share = 50/20 = 2.5 bp normalized expense ratio
  • Manager B costs 100 bps for 50% active share = 100/50 = 2.0 bp normalized expense ratio
While manger A is cheaper in terms of the absolute expense charged, manager B is clearly cheaper it terms of the expense per unit of active share. As a result, an investor can allocate 40% to manager B and 60% to a ~0 bps passive ETF, The result is the same 20% active share (40% allocation x 50% active share = 20% active share) at a lower cost (100 bps x 40% + 0 bps x 60% = 40 bps vs. the 50 bps for manager A).


Takeaway

When choosing an active manager, confidence in the team, the process, and the discipline the team has in following that process through various market cycles continues to be of obvious importance. As important is not the cost you pay in absolute terms, but rather what you pay for each unit of the skill they are selling.

Thursday, April 7, 2016

Active Management is Far From Dead

Eric Balchunas has an article on Bloomberg titled The Financial Industry Is Having Its Napster Moment asking "Has the music stopped for the financial industry?", sharing the following chart of flows since 2007.


He forecasts ~$1 trillion in outflows from higher fee active management every 4 to 5 years from here, which he believes will cause a material decline in revenue for investment management firms.
In other words, about $2.5 trillion in assets could migrate out of active mutual funds over the next decade. That money will shift from producing $18 billion in revenue to producing just $5 billion. That’s $13 billion less in revenue in the next decade and upward of $30 billion over the next 20 years. All this could be expedited by the new fiduciary standard—as well as a parallel trend that sees institutional funds moving toward passively managed investments, too.
As I'll outline below, while this is true in a vacuum... it misses an important aspect of what really drives asset growth (hint... for established players, it's not flows).


Flows Do Not Equal Asset Growth = The Industry is Still Thriving

Given this level of flows to passive from active, you would likely guess that the level of AUM for passive solutions would have grown by a much greater amount than active mutual funds, especially following the failure of active managers to protect investors during the financial crisis... right?

Wrong.

Given the huge AUM "advantage" of the much more mature mutual fund business, market appreciation has allowed domestic equity active managers to grow AUM by exactly the same amount within the domestic equity category, almost $1.8 trillion each since March 2009 market lows (data from Morningstar).


And while the Bloomberg article focused on domestic equities, let's take a look at the whole mutual fund / ETF complex..


That's a HUGE jump in AUM (and revenues) for investment management firms and given the huge operating leverage these managers employ (i.e. scale is huge for the bottom line) they are printing money.

So... unlike the music industry that has seen revenues slump as the preference for a high fee record slice has shifted to low fee digital, all while the overall music pie remains small (or has gotten smaller), the preference for a passive slice of the investment pie has occurred while that pizza has grown from a small to a large one.


If you believe capitalism isn't dead (I don't), then overall AUM in domestic equities (and especially across all global assets) will continue to expand... likely faster pace than active will be replaced.

Long live active management!

Monday, March 21, 2016

Buyback Performance Demystified

Earlier this month, in my post Stock Buybacks Demystified I attempted to remove some of the mystery surrounding buybacks, showing they are no different from an economic perspective (if you ignore the impact of taxes and the effects of signaling) than dividends. Given the recent outperformance of dividend paying stocks (as defined by those in the S&P 500 Dividend Aristocrat index) vs. stocks engaged in buybacks (as defined by those in the S&P 500 Buyback index) over the last year, I thought it might be helpful to demystify what has driven the recent outperformance of dividend stocks, share the historical performance of each, and outline some forward expectations for relative performance given where we currently sit.


Background: Buybacks have consistently outperformed since inception 

The first chart shows the growth of $1 invested in dividend stocks vs $1 invested in buyback stocks going back to the inception of the S&P 500 buyback index in early 1994. What we see is pretty consistent underperformance of dividend paying stocks over time that has compounded to 2% / year outperformance of the buyback stocks since inception (note both have outperformed the S&P 500). 


Background: Relative performance is highly mean-reverting
The second chart shows the relative performance of dividend stocks less buyback stocks over 12-month rolling periods going back to the inception of the S&P 500 buyback index. What we see is:
  • Pretty consistent underperformance of dividend paying stocks (most of the relative return series is negative) 
  • Mean-reversion characteristics (when one outperforms the other materially, it tends to bounce the other direction pretty quickly)
  • Dividend outperformance during periods of market stress 

Why have dividend stocks outperformed recently? Valuation differences
In addition to market stress that favors dividend stocks, the change in relative valuations have driven dividend stocks (i.e. it's been seemingly more technical than fundamental). The chart below shows valuations (i.e. P/E) of the dividend and buyback indices as of month-end February and as of a year ago. While dividend stocks were richer by this measure even a year ago, valuations among dividend stocks have held up pretty well. On the other hand, valuations among buyback stocks have gotten materially cheaper, driving the relative underperformance of buyback stocks and creating a huge valuation gap between the two. 


What now? 
The final chart shows the impact of shorter-term (12-month) relative performance between dividend and buyback indices on longer (3-year) forward relative performance. We can see:
  • Pretty consistent underperformance of dividend paying stocks over most three year periods 
  • Mean-reversion characteristics kicking in when dividend stocks have outperformed by the 10% level they have over the last 12-months (when dividend stocks have outperformed by 10% or more over a 12-month time frame, they have underperformed by an average of 6.4% / year for the next three years)

Takeaway
Neither dividend stocks or buyback stocks outperform in all periods or market environments, but under the view that the underlying economy appears to be holding up, current valuations, the historical outperformance of buyback stocks, the mean-reversion characteristics of buybacks (following recent dividend outperformance), and certainly the tax efficiency of buybacks all seem to support a tilt toward buybacks. 

Wednesday, March 9, 2016

Stock Buybacks Demystified

Based on my Twitter feed, stock buybacks seem broadly misunderstood in terms of what they are meant to accomplish (to redistribute excess capital back to shareholders) and the impact they have relative to dividends. As an aside, I also don't understand the following typical complaints:
  1. Buybacks are done when stocks are rich: if the stock of a company performing buybacks is "rich", then why are you owning it to begin with?
  2. Buybacks are often done during bull markets and stop during bear markets: that is due to the fact companies often have higher earnings and more excess cash available to distribute during bull markets

As a result, I wasn't too surprised when the below chart made the rounds yesterday, along with the following implications:
  • Stock performance is higher only because of financial engineering
  • Households are scared of equities and are poor market timers

In this post I'll provide the case for why (ignoring taxes and the effects of signaling):
  • Buybacks and dividends are economically identical
  • Buybacks are an incremental driver of the outflows we've seen from households
  • Why flows (both inflows and outflows) do not relate to demand / why households almost always have outflows

Buybacks and Dividends are Economically Identical

Excluding the potential signaling or tax effects of buybacks vs dividends, buybacks and dividends are identical in terms of overall economic impact. For example, assuming the following:
  • Corporation X has $2.5 billion in excess cash to distribute back to shareholders
  • Corporation X has a market cap of $5 billion ($2.5 billion enterprise value + $2.5 billion cash)
  • Corporation X has 100 million shares outstanding, priced at $50 / share (100 million shares x $50 / share = $5 billion market cap)
  • Corporation X will earn $1 billion ($10 / share) the following year
  • Shareholder X owns 100 shares and has spending needs of $1000
  • 100% of Shareholder X's wealth and income comes from their stock ownership

Situation 1: Corporation X distributes the excess cash to their shareholders via a $2.5 billion buyback
  • Corporation X buys back $2.5 billion of their shares at $50 (i.e. they retire 50 million shares)
  • Corporation X now has 50 million shares outstanding at $50 = $2.5 billion market value (all made up of their enterprise value)
  • With no dividend payment, shareholder X will need to sell 20 shares (x $50) to meet their $1000 spending need, meaning they will have 80 shares x $50 = $4000 in Company X stock
  • Corporation X will earn $1 billion next year or $20 / share given their 50 million shares
  • So... next year Shareholder X will be entitled to a $20 x 80 = $1600 of those earnings

Situation 2: Corporation X distributes the excess cash to their shareholders via a $2.5 billion dividend
  • Corporation X distributes $2.5 billion via dividend ($25 / share)
  • Corporation X still has 100 million shares outstanding, but at an enterprise value of $2.5 billion each share is now worth $25 / share
  • Shareholder X can meet all of their spending needs through the dividend distribution (1000 x $25 = $2500 dividends) and after spending $1000 still has $1500 cash remaining
  • Shareholder X can use the excess $1500 to buy 60 more shares at $25 share, meaning they now own 160 shares at $25 = $4000 in Company X stock
  • Corporation X will earn $1 billion next year, which is $10 / share given 100 million shares
  • So...next year Shareholder X will be entitled to $10 x 160 shares = $1600 of those earnings

A table summarizing the above example (click for larger image)


What has changed? 
  • # of shares outstanding
  • Price of shares outstanding
  • Earnings per share
  • Shareholder X becomes a net seller of shares in the buyback scenario

What has stayed the same?
  • Enterprise value of the firm ($2.5 billion)
  • The overall level of earnings ($0.5 billion)
  • Earnings Shareholder X is entitled to next year ($1600)
  • Overall market value / demand for Company X stock from Shareholder X ($4000)
  • Overall net purchases of the stocks ($1.5 billion)*
* In the buyback scenario, $2.5 billion is bought back by Company X, but if all shareholders acted like Shareholder X, they would sell $1 billion for their spending needs ($1.5 billion net purchases); in the case of dividends, of the $2.5 billion distributed, $1 billion is spent, and the same $1.5 billion is used to buy back shares with the excess cash.

Household Flows Don't Matter / Should be Negative

As highlighted above under 'what has changed', household outflows are in fact impacted on the margin by the form of capital distribution (i.e. whether it is received via buyback or dividend). In the case of a buyback, households are simply creating their own dividend through the sale of shares. Given the two situations are identical in terms of overall demand for Company X stock (demand at time 0 was $5000 worth of stock, post spending it was $4000), we can see why flows really don't matter.

In fact, while the initial chart circulating through Twitter highlights the negative flows from the household sector for stocks from 2008-2015, what may be a surprise is that the overall level of stocks held by the household sector (i.e. a better measure of demand) jumped from $5.4 trillion at the end of 2008 to more than $12.7 trillion over that same time frame (as of 9/30/15  - the latest z.1 report), a normalized increase of 37% of GDP to 70% of GDP.

But a key point is that household net flows for a mature / functioning economy should be negative... when markets have positive returns, investors put in less money today than what that investment should compound to when they make withdrawals in the future. Thus, it should be no surprise that net flows from the household sector have historically been negative over all longer periods of time going back 60 years, while the amount of stock held by the household sector has continued to move higher.



To summarize... the form of distribution really does not matter and buybacks are not evil... the next time you hear someone state buybacks are the cause of the run up in stocks, try replacing the word buyback with dividend.

"Stocks are up because of a huge increase in dividends" sounds a lot less controversial than "stocks are up because of a huge increase in buybacks", though they are both identical signs that the performance has been driven by an improvement in fundamentals and an increase in cash flows.

Wednesday, February 17, 2016

Combining Momentum and Dollar Cost Averaging for Smoother Results

Josh Brown (i.e. The Reformed Broker) recently shared the aptly titled post How to Make Volatility Your Bitch highlighting how dollar cost averaging into a volatile market can lead to higher overall returns:

Door number one – you spend 15 years putting $1000 into an investment every month for 15 years, with the possibility of seeing that investment get cut in half twice.
Door number two – you spend 15 years putting $1000 into an investment every month for 15 years, with the same annual performance of what’s behind door number one, but no drawdowns.
Which would you choose? 
On the surface, you’d choose door number two. Of course, who wouldn’t? 
But it’s the wrong choice. The trick here is to remember that you’re adding to the investment at a rate of $1000 per month. That’s when you realize that door number one, with it’s twin 50% crashes, is the better option.
His point is an important one for long-term investors... you would rather pay less (than more) for a security today if it is worth more in the future and for long investment horizons that has typically been true. So in general, regularly contributing to your retirement (or other long-term goals) is good practice.


The Caveat

BUT there is a caveat... dollar weighted returns are only better than time weighted returns if the dollar weighted price you paid was lower than the price at the ending date. As dailyVest outlines (bold mine):
In contrast with a time-weighted approach, the dollar-weighted rate of return calculation method does measure the size and timing of cash flows, as well as the investment performance. Thus,
  1. Periods in which more monies are invested contribute more heavily to the overall return – hence the term “dollar-weighted” 
  2. In this case, investors are rewarded more for larger investments made during periods of greater price appreciation 
Given we are so close to the all-time high in the S&P 500, chances are each dollar invested over the last 15 years was below (or well below) the current price, resulting in more investments having greater price appreciation and dollar weighted returns > time weighted returns. So... rather than looking at just the current 15 year period, let's go back and look at 15 year periods ending 5 and 10 years back (which end at less of a peak) to see how well the same door number one vs door number two worked out.

Note: the analysis below shows $1000 invested each month in the S&P 500 and in a return stream with identical 15 year time weighted returns, but with 0% volatility.


Dollar weighted returns > Time Weighted Returns over Most Recent 15 Years



Dollar weighted returns < Time Weighted Returns over 15 Years Ending 2010



Dollar weighted returns < Time Weighted Returns over 15 Years Ending 2005

In these examples we see just how important the ending point is in determining which return stream "wins", as well as how important the end date is in the overall growth of the $180,000 contributed (which is a reason why investors generally should derisk as they approach retirement). It also outlines why dollar cost averaging into a solution that can protect against the downside may be beneficial relative to a buy and hold strategy by limiting the amount of dollar weighted contributions made at poor entry points.


Dollar Cost Averaging in a Capital Preservation Strategy: The Case for Momentum

Because I have the urge to compare all buy and hold strategies with momentum, the below replicates the above charts and adds a momentum equity curve with this simple rule at month-end:
  • If S&P 500 > 10-Month Moving Average, then S&P 500
  • Otherwise, Aggregate Bonds
Similar to what is shown in the charts above, the charts below outline the growth of $1000 a month into the S&P 500, an identical time weighted return series with zero volatility, and the above momentum strategy.




The momentum strategy provided much more consistent dollar growth in all three time frames and in these specific windows materially outperformed both a buy and hold and 0% volatility iteration (this will not necessarily be the case in all periods - especially in up and to the right equity markets). So... perhaps it's the combination of consistent contributions and a strategy more focused on capital preservation that can more easily make volatility your bitch (without the volatility).

Wednesday, February 10, 2016

Avoiding Bear Markets to Improve Risk-Adjusted Returns

Ben Carlson of A Wealth of Common Sense has a recent post, When Global Stocks Go On Sale, outlining that it is typically a pretty good time to be buying when the MSCI World stock index is in a 20% or greater drawdown.

His insightful takeaway and chart outlining the historical drawdowns and forward performance of the index is below:

There were only two times out of the ten bear markets where stocks weren’t higher one year later. Only once were stocks down three years later. And there was never a period where stocks weren’t higher five years after initially falling 20%. The paradox of investing is that the best times to put your money to work are often when things seem like they’re never going to get better.

While I in no way disagree with his insight, especially for a buy and hold investor thinking of selling, I thought it would be fun to share the completely opposing strategy that avoids these periods of distress, as well as one that avoids stock exposure after even one month of negative performance.


Avoiding Extended Drawdowns May Improve Risk Adjusted Returns

As I outlined in a previous post, Using "Normal" Drawdowns as a Timing Signal, an investor who sold their S&P 500 allocation whenever the S&P 500 index was in a drawdown of 10% or more, and instead held bonds, had similar long-term returns as a buy and hold investor, but with materially less risk and drawdowns.

A similar situation has played out for investors allocated to stocks within the MSCI World index when drawdowns were less than 10% or U.S. treasuries when the MSCI World was in a drawdown greater than 10%, while a 20% threshold wouldn't have held up quite as well as the 10%, but would have provided roughly similar returns with less risk than a buy and hold investment.



What gives?

The reason for the improved risk-adjusted performance has been the power of momentum within the MSCI World index during drawdowns. When the MSCI World index ended a month at a roughly 10% drawdown, it often moved lower... sometimes much lower. At a 20% drawdown, only 2 of the 5 times was this in itself a decent short-term buying opportunity (highlighted in green). The other 3 times presented a better opportunity further down the road.



Buying Only at the Peak

Taking this "drawdown avoidance" to the extreme, let's see how an investor in the MSCI World index would have performed if they only bought when it was making new end-of-month highs. In this example, an investor is only holding the MSCI World index if the previous month was at an all-time high, otherwise U.S. Treasuries.


While there were long periods of relative underperformance (and this is an extremely high turnover strategy), the resulting performance and lower risk offers some insight into how a strategy that is less exposed to risk, yet can avoid loss of capital, may actually be able to improve absolute and relative performance.

Wednesday, January 27, 2016

The Case For High Volatility Strategies

Which investment would you prefer to invest in to diversify your existing stock allocation? 

Asset A with an expected:
  • 3% annualized return
  • 3.5% annualized standard deviation
  • 0.00 correlation with your existing investment
Asset B with an expected:
  • -5% annualized return
  • > 50% annualized standard deviation
  • 0.00 correlation with your existing investment

Easy question right? Perhaps not.

Asset B may actually improve long-term returns and reduce risk at the portfolio level, whereas an investment in Asset A may just be a drag on performance. This post will walk through an example, outline the math behind the results, and hypothesize how an investor may want to think about this phenomenon. Going one step further, I will outline how this may, in part, explain the low volatility anomaly (one example being that low-volatility stocks have produced higher risk-adjusted returns than high-beta stocks in most markets studied).


Example 

The chart below outlines the performance of an investment in the S&P 500 (using actual monthly S&P 500 returns) going back to the SPY ETF inception (I was being lazy), as well as a monthly rebalanced allocation consisting of a 90% weight to the S&P 500 and a 10% weight to:: 
  • Asset A: which reverts monthly from negative to positive performance (-0.75%, +1.26%, -0.75%, +1.26%), compounding to a 3% annualized return at a 3.5% standard deviation; in the chart below this assumes price action of $100 to $99 to $100 to $99... but with an incremental 3% return built in.
  • Asset B: which reverts monthly from negative to positive performance (-15.4%, +17.2%, -15.4%, +17.2%), compounding to a -5% annualized return at a 56.5% standard deviation; in the chart below this assumes price action of $100 to $85 to $100 to $85... but with an incremental -5% negative return built in.

Despite the 8% annualized outperformance of asset class A vs B that compounded to a 100% gain in asset A and a 70% decline in asset B, the 90% stock / 10% allocation to asset B results in a combined portfolio with higher returns, a higher sharpe ratio, and a lower drawdown relative to an allocation to the positive returning asset A (the benefit of the allocation to asset A was the reduced standard deviation as that allocation reduced risk asset exposure by 10%).


We see below how much asset B benefits the portfolio when both both asset A and B are on equal return footing.


What gives?

William Bernstein wrote a great piece on the topic back in 1997 titled The Rebalancing Bonus. The whole article is worth a read, but he shares the following formula that calculates the "bonus" received by rebalancing across asset classes.

RB1,2 = X1X2 {SD1SD2 (1 - CC) + (SD1 – SD2)/ 2}

RB = rebalancing bonus
X1 = allocation weight to asset 1
X2 = allocation weight to asset 2
SD1 = standard deviation to asset 1
SD2 = standard deviation to asset 2
CC = correlation coefficient

Before you complain too much about the math, I'll walk through the equation from left to right (assuming "all else equal") with the applicable points to the example above in bold:
  1. A more balanced blend (i.e. a closer to 50/50 weighting) will provide a greater rebalancing bonus
  2. A higher standard deviation of either asset class will result in a greater rebalancing bonus as an investment gets "more bang for your buck" 
  3. A smaller correlation (or negative correlation) will result in a greater rebalancing bonus 
  4. A larger difference in the standard deviation of the two asset classes will result in a greater rebalancing bonus
In the case of a higher volatility solution, bullets #2 and #4 both result in a higher rebalancing bonus and higher return (all else equal). In my example, the rebalancing bonus went from roughly 10 bps given a 10% allocation to asset A to 150 bps given a similar 10% allocation to asset B.
Asset A: RB = 90% x 10% [~15% x 3% (1-0) + (~15% - 3%)^2/2] = ~10 bps
Asset B: RB = 90% x 10% [~15% x 56% (1-0) + (~15% - 56%)^2/2] = ~150 bps
Given the 10% allocation, the return differential of 8% (the 3% return for asset A less the -5% return for asset B) x the 10% weight is only 80 bps, significantly lower than the benefit of the rebalancing bonus. In this example, in order for the two blended portfolios to have a similar return, the return gap between asset A and B must be closer to 14%.


Implications

From an asset allocation perspective, the above has a number of implications.

For one, it certainly makes the case for an allocation to strategies uncorrelated to an existing portfolio that have higher levels of expected volatility. In my opinion, the most obvious strategy that is largely under-allocated to is managed futures and to a lesser extent certain hedge fund styles and (until the last 10 years when everyone piled in)... commodities. I would also note that return expectations for the traditional diversifier, core bonds, are quite low and volatility of those returns are anticipated to remain low (thus a rebalancing bonus near 0), thus an allocation to a high volatility diversifier only needs low (or potentially negative expected) returns to make sense.

Another potential implication of the positive impact higher volatility strategies have on the rebalancing bonus is that this may partially explain the low volatility anomaly seen within asset classes, such as within stocks and bonds. When viewed in isolation, the outperformance of less volatile asset classes seems like an anomaly, but when viewed within a broader portfolio construct it makes sense that these lower volatility investments may need a higher expected return to draw in investors.

Using the rebalancing bonus formula and the following inputs which go back to the December 1990 inception of the S&P 500 High Beta and S&P 500 Low Volatility indices, we get the following rebalancing bonuses despite the higher correlation of the S&P 500 High Beta index with the S&P 500 index:
High Beta: RB = 50% x 10% [14.4% x 28.2% (1-0.89)) + (14.4% - 28.2%)^2/2] = 35 bps
Low Volatility: RB = 50% x 10% [14.4% x 11.0% (1-0.75)) + (14.4% - 11.0%)^2/2] = 11 bps
Pretty close to the 38 bps and 12 bp rebalancing bonus they provided in reality. In this instance, that 26 bp differential makes up ~25% of the excess performance a 50/50 S&P 500 / low volatility blend over that time frame.

Monday, January 25, 2016

Are Stocks Cheap? Checking in on Current Valuations

I'll leave it to others to chime in whether forward P/E's are useful or not given the fact they typically overstate earnings and I'll ignore that earnings may be at a cyclical peak (more on the latter here). As an aside, technicals in the market are filthy, as most short-term signals I look at are providing caution (example here). BUT, based purely on current forward P/E's relative to their own history, both large growth and large value stocks look awfully attractive if you are of the belief that the recent market noise is just noise vs. a sign of recession.

How attractive?

The below chart plots all quarterly forward P/Es against the forward 5-year annualized returns of both indices going back to the 1979 Russell inception of each. As of January 15th, the forward P/E of large value breached 14x while large growth went sub 17x, both historically great valuations to be buying at for the longer-term.


Highlights include that the average forward 5-year annualized returns of large value / growth were 13.9% / 16.1% when the P/E was below current levels and only 7.6% and 5.5% when above (and growth has never had a negative 5-year return when the forward P/E was this low).


Monday, December 14, 2015

Tweeting High Yield: A Round Trip in Investor Sentiment

With high yield all the rage these days, I thought it might be worthwhile aggregating tweets / posts going back to the beginning of this credit cycle to outline where we've come from and to share some thoughts on where we might be going. Curious if this format is helpful or too disjointed.



Backdrop... how did we get from the distressed 2008 (a 20%+ index yield), to sub-10% yields and a risk on mentality?

In March 2009, corporate bonds appeared to be a screaming buy and the Fed had an outsized impact getting spreads (and yields) much lower - much quicker than I thought was possible.

Once things calmed, why was there a reach for yield? Because it was the only place where yields were high.
Why are Investors are Reaching for Yield?: Because high yield is just about the only place you can get yield... http://t.co/EFGqZMR7
Despite the reach, I didn't mind high yield back in 2012 when rates backed up to 8% given where we seemed to be in the credit cycle (i.e. early).




When did things get frothy? I'd say early 2013 when Yields went sub-5%

Yields went from over 8% to under 5% within 6 months. At that point (and since), I could not get my head around high yield valuations.
Especially when viewed relative to stocks, once the yield on high yield bonds < earnings yield on stocks.
I was far from the only person who saw the froth in high yield
High yield sentiment seemed formed by the strong 5 year performance of the asset class. But perspective on how that return was achieved appeared missing:
Interesting back and forth in comments of this tweet. Some very smart people couldn't see a situation I thought / think has a decent probability. High yield underperformance even without stock underperformance given extreme valuations of high yield.


High Yield Sentiment Flashed Warning Signs in 2014 - Very Briefly

The sentiment shift and my view that high yield investors could be well over their ski's became very apparent when high yield "sold off" just 2% in fall 2014 and investors viewed that as abrupt:
Despite that "sell-off", yields in the lowest quality segment were still absurdly rich, but investors calmed their fears and dove back in, despite crazy yields.


Recent Views: The Sell-off was Expected - It Doesn't Seem to Be the Crisis Others Want to Make It

Which gets us caught up to this year when I brought my blog back after a three year hiatus and I jumped right into an area of the market I felt was misunderstood:
Yet, DESPITE my views of how mispriced things were, until financials within high yield become more stressed, I am less concerned about the recent sell-off's impact on the overall market (though things can / do change quickly):
This is supported by the perspective on where current yields are (yields still aren't all that high) and relatively contained within energy:
In times like this, perspective is much needed (i.e. things haven't been bad by historical standards):
If an allocation to high yield is to be made, note that lower quality high yield has not led to historical outperformance:

Wednesday, December 9, 2015

It's Generally Smart to Avoid Credit Risk

I've previously outlined that high yield credit risk is typically less ideal than simply gaining credit exposure through stocks and rate exposure through bonds. Now Larry Swedroe outlines the case for avoiding investment grade credit risk altogether.

There are many well-documented anomalies in finance. Among them is the surprisingly small return that investors historically have earned for taking credit risk in fixed-income markets—the default premium, as measured by the difference in returns between long-term Treasurys and long-term corporate bonds, has been only about 0.3%—and that stocks with a higher risk of defaulting on debt have produced lower returns.
Going back to 1988, which is as far back as Barclays breaks down the returns of the Long Corporate Bond index into the contribution from credit and rates ex the spread, the return from the credit component has actually been slightly negative at -0.09% annualized vs the 8.12% return for a like duration Treasury bond. A similar story plays out in intermediate corporate bond space, where the credit spread contributes only 0.37% of the 7.24% return for the Barclays Corporate Bond Index since 1988.


The story is more nuanced than "credit always underperforms the yield" as yield is generally a great predictor of future returns, but yield should generally be viewed more as the ceiling for future returns than actual future returns. The issue is when there is stress in the market, such as during the financial crisis when 13 year cumulative performance (the rough duration of the index) of long corporate bonds underperformed the yield's "predicted" return by almost 80% (the 13 year forward performance starting in 1995 ended during the 2008 meltdown).


My general view of credit is to avoid it unless you feel you are being more than fairly compensated. Even if you miss shorter periods of relative outperformance (vs treasuries), allocating only when credit looks like a screaming buy will likely result in a much better long term return profile. In the case of long corporate bonds, allocating only when the spread of long corporate bonds to treasuries was greater than 200 bps (something that occured just 20% of the time), returned 1.1% annualized more since that same 1988 start.


Tuesday, December 1, 2015

The Case for an Allocation to Dollar Based EM Debt

While the underperformance of high yield bonds since my post The Case Against High Yield has certainly made high yield bonds more attractive (yields went from sub 6% to north of 8%), I still prefer the risk/return profile of a stock/bond allocation (more here). For those that are looking for a higher yielding fixed income alternative with limited currency risk and the potential for U.S. interest rate diversification, dollar based emerging market fixed income may be an interesting alternative.


What is it?

The Barclays EM USD Aggregate Index is a flagship hard currency Emerging Markets debt benchmark that includes USD denominated debt from sovereign, quasi-sovereign, and corporate EM issuers. The index is broad-based in its coverage by sector and by country, and reflects the evolution of EM benchmarking from traditional sovereign bond indices to Aggregate-style benchmarks that are more representative of the EM investment choice set. 
At present the index is made up of ~75% sovereign debt and ~25% corporate debt, all denominated in the dollar, while the below table breaks down what it looks like in terms of country composition. Interesting to note that while only 2% of the portfolio is composed of Venezuelan debt, that 2% contributes more than 10% of the overall yield of the index (in other words, the higher yield is certainly not risk-free), but the index does provide a pretty wide breadth of exposure.



The EM Debt Relative Valuation Story

The case for EM USD Aggregate exposure is simply a relative valuation story. Despite periods of heightened turmoil within emerging markets over the last 20+ years, the yield to worst of the portfolio has generally been a pretty good predictor of future returns (note six years is used for the forward return projection given the duration of the index has fluctuated between around 5-7 years going back to the 1993 inception).

At the current yield of almost 6%, forward absolute returns are likely to be favorable.


The higher yield becomes more intriguing when viewed relative to the yield of the U.S. Aggregate Bond index. The below chart is similar to the one above, but is shown with yield to worst and forward returns relative to that of the U.S. Aggregate Bond index. In this case the relative yield advantage has been a pretty consistent precursor to future outperformance. A notable exception is the underperformance in the mid 1990's which coincided with six year returns impacted by the 1998 emerging markets crisis (though the yield advantage was sub 2% then vs almost 4% today).


While emerging market bonds are certainly not risk-free, the US dollar denomination protects an investor from a direct impact should the dollar continue to strengthen (though countries trying to pay back dollar denominated debt with a weakened currency are certainly indirectly impacted), while relative yields that are near all-time wide levels vs the US Aggregate index seem attractive given the "emergence" of emerging market countries on the global economy over the more than 20 years since the index incepted. Add in the potential diversification benefits moving some fixed income away from US monetary policy may provide and you get what is in my view a strong case for a strategic allocation to emerging market fixed income.

Monday, November 23, 2015

Valuations Do Matter (Even Over Shorter Time Frames) / Momentum Driven Valuation Timing

I often read that valuations don't matter over the short-term (a case often cited against market timing). Over very short periods (hours, days, etc...) this certainly may be true, but while there can be a lot of variability around month-to-month or year-to-year performance, I completely disagree with the sentiment that it doesn't matter. That said, there are better ways than just using current valuation levels for an investor to time markets which I will outline below.


Debunking the Case that Valuations Don't Matter
Before getting into my analysis supporting the theory that valuations do in fact matter a LOT, especially when you use the information embedded in momentum as support, I did want to highlight a recent research paper / article outlining valuations don't matter that simply appears to be wrong. 

Larry Swedroe from ETF.com shared research in an article Valuation Metrics In Perspective outlining the research done by Javier Estrada which concluded that one year forward performance of the S&P composite from 1899-2014 was independent of preceding valuations:
when the current P/E was between 10.4 and 13.3, the one-year forward return was 7.3 percent. When it was higher, between 16.4 and 18.9, the one-year forward return averaged 11.7 percent. And when the current P/E was above 19, the one-year forward return averaged 10.0 percent.
While I completely agree that one year returns are noisier than 10 year returns, the above results truly surprised me. While any one year period could have much higher / lower returns (i.e. the range of outcomes should be wider), over long periods of time the average one year forward performance when valuations are cheap shouldn't be that different than the average 10 year forward performance (it should average out to roughly the same figure).

Given my surprise, I decided to recreate the results in the paper using Shiller CAPE for my P/E over that same 1899-2014 time frame. The results in the table below make it likely that the analysis in the paper is either flawed (they do use a weird 3-month lag for earnings) or is simply wrong (my personal view). As the table outlines, the results not only show that high valuations = lower returns over one year, but the average one year return is even more dependent on valuations (on average) than ten year returns. You can also see that the range of outcomes is much more dispersed over one year than ten years.



Back to the Original Point of the Post... Momentum to Time Value
I had run the analysis below prior to my attempt at recreating the above data, so my methodology is a bit different. In the example below, I use data going back to 1881 (which is aligned to the inception of the Shiller CAPE time series) and I separate one month forward returns by whether or not the S&P composite had a one year backward looking return that was positive or negative. Below are the same data points in a table form and in a scatter plot.

What is easily discerned is that regardless of momentum, cheap valuations are generally good for returns (though extremely cheap and negative momentum has had some severe volatility - see standard deviation for periods when CAPE < 10), while high valuations have generally resulted in pretty strong returns when momentum is strong. Valuations seemingly only matter when valuations are high and momentum has turned negative (as seen by the negative absolute performance for the three buckets when CAPE > 15 and momentum is negative).



My takeaway... the underlying trend of the market is often as, or more, important than stock price levels, but performance will eventually catch up with how much you paid. BUT, momentum may help protect an investor when markets are rich and turn, as well as help keep an investor appropriately in the market even when valuations appear stretched.

Tuesday, November 17, 2015

The Mean Reversion Case For (and Against) Strong Future Returns

Bull thesis: 15-year S&P annualized returns ending 9/30/15 came in at just under 4%. The average forward return since 1915 when returns were that level (or lower) was 15.5% annualized over the next 15 years with a standard deviation of only 2%

Bear thesis: the 15-year starting point came when the previous 15 year annualized returns were just under 18% (i.e. we are still working off extreme valuations)




The counter argument to the bear argument can be seen in the chart below which compares the same 15 year historical returns on the x-axis with 30 year forward returns on the y-axis. As outlined in my previous post, returns tend to smooth out over 30 years, thus it matters a lot less what you pay for stocks over 30 years than over 5, 10, or even 15 years because more of the return is composed of fundamentals (i.e. dividends, buybacks, etc...) than multiple expansion / contraction as compared to shorter periods. Thus even extreme valuations have historically delivered 30 year returns I think most investors would find acceptable at the moment.


My take? Right between the two. I am not nearly as scared by current valuations, peak margins, etc.. as bears (especially over longer time frames) and I am not remotely a bull either. That said, I'm also not worried about a short term correction that would likely create a much better buying opportunity in the future.

Monday, November 9, 2015

Making Time (Even More of) an Investor's Best Friend

Ben Carlson of A Wealth of Common Sense blog (and author of a great book by the same name), had a recent post Playing the Probabilities outlining that time has been an investor's best friend (for those investors that have had in some cases quite a bit of time), pointing to the following table.


He also shared some pretty amazing stats, including:
The worst total return over a 20 year period was 54%. But the worst 30 year total return was 854%.
While I certainly agree with everything he outlined, he did ask the following question.
Has anyone figured out a better way of compounding your money in stocks beyond increasing your holding period? Not many.

Challenge accepted!

Simple rules (for more... see Meb Faber's record downloaded white paper):
  • If the S&P composite TR is > 10 month moving average, stay in stocks
  • If the S&P composite TR is < 10 month moving average, move to bonds (in this case 10 year treasuries)
The equity curve.


And the updated table with some additional bells and whistles (note there are some very slight differences with the returns Ben produced, but the message is identical).


While there is no free lunch (in this case, an investor gives up some upside over shorter time frames), using these basic momentum rules resulted in no negative returns over any ten year period and actually increased the long-term returns over this 1926-2015 time frame, making time an even better friend for an investor.

Thursday, November 5, 2015

GMO Flows Turn Negative - An Ominous Sign for Risk Taking

As unnecessary as it may seem, contrarian investment managers need to be even more consultative with their clients than managers more aligned with market sentiment, otherwise clients won't be able to handle the extended periods of relative underperformance a contrarian investor is likely to face from time to time. In the case of GMO, while the long-term performance of many of their strategies is pretty strong (and tend to materially outperform when markets turn), the performance captured by their investor base is typically quite poor.

One example being the GMO Benchmark Free Allocation III, a fund in which the average investor has underperformed the fund by 3-5+% over 3, 5, and 10 years. As a result, despite a 16th percentile Morningstar rank in terms of fund performance over 10 years, investor performance only ranks 74th.


In other words, their investor base historical zigs when they should zag... adding money to the contrarian GMO after markets have tanked (when they should be taking market risk) and piling out of the contrarian GMO after markets perform well (when they should be taking risk off the table). Thus, it was relatively alarming to see that funds flows at GMO have been negative $4.2 billion over the twelve months through 9/30/15, including almost $3 billion of outflows the last two months of the third quarter alone.



How poorly have investors timed GMO? Let's take a look at how well a model doing the exact opposite of GMO flows would have performed going back 20 years.

Rules:
  • If twelve month flows to GMO funds are positive, allocate the next month to stocks (S&P 500)
  • If twelve month flows to GMO funds are negative, allocate the next month to bonds (Barclays Agg Bonds)

The result of which is more than 100% of the S&P 500 with almost half the volatility and drawdown.

I have a ton of respect for the way in which GMO manages money (their guts to be massively contrarian if that is their view) and I think their thought leadership is about as good as it gets in the industry. The challenge is GMO knows they are smarter than their clients, leading to a more-or-less 'take it or leave it attitude'.

But what good are strong long-term returns if an investor is unable to capture them?

Wednesday, October 28, 2015

What Exactly Does the VIX Tell Us?

Most investors know of the VIX Index, but not as many understand what information the VIX provides an investor. Here is my attempt to provide an initial outline of what it is and why the information embedded within the figure is so powerful.

VIX Defined

The CBOE has a white paper that provides a ton of detail into the VIX calculation, but the origin of the index describes what it is at a high level well enough:

In 1993, the Chicago Board Options Exchange® (CBOE®) introduced the CBOE Volatility Index® (VIX® Index), which was originally designed to measure the market’s expectation of 30-day volatility implied by at-the-money S&P 100® Index (OEX® Index) option prices. The VIX Index soon became the premier benchmark for U.S. stock market volatility.
Ten years later in 2003, CBOE together with Goldman Sachs, updated the VIX to reflect a new way to measure expected volatility, one that continues to be widely used by financial theorists, risk managers and volatility traders alike. The new VIX is based on the S&P 500® Index (SPXSM), the core index for U.S. equities, and estimates expected volatility by averaging the weighted prices of SPX puts and calls over a wide range of strike prices.
In summary... the VIX is a reflection of the market's expectation of future market volatility.


The Math Behind What the VIX Level Means

As Eddy Elfenbein of the great Crossing Wall Street blog outlined back in 2012, you can take the current market's expectation of future market volatility (i.e. the VIX), to determine the expected range of future stock market returns.
The 3.46 denominator is simply the square root of 12, which takes the VIX from an annualized figure to a monthly figure given there are 12 months in a year.


You can also vary the blue "confidence bands" in the chart above (in the chart above, the one standard deviation bands "should" capture 68% of outcomes). For example, from statistics we know that 1.96 is the z value of a 95% confidence interval. We can convert this 1.96 level to a monthly value as follows:
1.96 / (square root of 12) = 0.57
We can then take that 0.57 value and multiply it by the VIX to determine the bands that (in theory) contain that 95% percent of outcomes. For example, with a VIX of 20, the equation is:
20 x 0.57 = 11.3 or 95% of all outcomes over the next month should be within a +/- 11.3% return
In reality, the VIX typically overstates the level of market risk and understates the results within the band. As the chart shows below, the confidence bands described by the VIX understate the "capture rate" of the bands, especially at higher confidence levels. In the chart above, one standard deviation bands should capture 68% of outcomes, but instead have captured 85% of outcomes.


Conclusions

Neither of the following conclusions should surprise readers of the blog, but they are:
As a result, while an investor can utilize the VIX to scale equity weights to smooth out returns, an investor should rarely be buying volatility protection.


Wednesday, October 21, 2015

Utilizing the Value of Value to Make Value / Growth Tilts

Back in August I outlined why I thought the plain-vanilla value premium had been compressed to the point growth had and was likely to continue to outperform in my post Death of (Plain Vanilla) Value - Long Live GARP. This post is meant as a follow up and suggests a few frameworks as to how an investor might allocate based on the given "value of value".


Backdrop: Value of Value Matters

Value historically outperforms growth when stocks making up the value indices are beaten down relative to growth. Thus, it should be no surprise that value materially outperformed following the Internet bubble as value stocks were massively cheap relative to growth stocks (see below). The issue over the last decade plus is that many investors have piled into value ignoring the driver of value's historical outperformance, resulting in a "value discount" that is historically narrow.


The next chart outlines in more detail why the discount matters, with the starting "value discount" on the x-axis and the subsequent 7-year excess return on the y-axis. You can see the linear relationship between the "value of value" and future value excess return to growth. When the discount is high, outperformance of value vs growth is likely. When the discount is low (which happens to be where we currently sit), underperformance of value vs. growth is likely.




Putting the Insight into Action

While there are a multitude of ways this insight can be put to use, below are a few simplistic ways that only require making a reallocation at most quarterly and has been more likely to require a reallocation once every 3-5 years. Note this analysis does have a huge data mining issue as we know in advance that a 25% and 30% value discount are thresholds where growth and value have diverged in the past - though less of an issue today as we are near all-time tight levels. As an aside, as the charts below show it wasn't until the late 1990's that the performance of the Russell 3000 Value and Growth indices diverged. It was only when investors initially flocked to growth (and later value) that we have seen distinct differences in the "value discount" and in subsequent performance.


Model 1: Long-Only

Rules (reallocate the portfolio quarterly - ignores transaction costs):
  • If the value discount is narrower than -25% (i.e. growth is cheap), allocate to the Russell 3000 Growth Index
  • If the value discount is between -30% and -25%, allocate to the Russell 3000 Index (i.e. don't tilt growth or value)
  • If the value discount is wider than -30% (i.e. growth is expensive), allocate to the Russell 3000 Value Index



Model 2: Long-Short

Rules (reallocate the portfolio quarterly - ignores transaction costs):
  • If the value discount is narrower than -25% (i.e. growth is cheap), long position in the Russell 3000 Growth Index / short position in the Russell 3000 Value index overlayed on the Russell 3000 Index (i.e. generate alpha on top of the Russell 3000 Index through the long/short)
  • If the value discount is between -30% and -25%, allocate to the Russell 3000 Index (i.e. don't tilt growth or value)
  • If the value discount is wider than -30% (i.e. growth is expensive), long position in the Russell 3000 Value Index / short position in the Russell 3000 Growth index overlayed on the Russell 3000 Index