Some Simple (But Useful) Math

Mar 22, 2012 in Volatility

A member wrote a question which spurred some thinking. Essentially, they wanted to understand not just the calculation of the sharpe ratio -- but why its a good metric.  So this blog starts on that topic and hopefully can serve as a reference -- it then extends into a longer piece which are thoughts that should help a few people that want to pursue this angle in general.

We are of the belief that you do not want to get bogged down in too much detail beyond this type of math below -- understanding the basic math can be very helpful in your ability to understand investing -- just as understanding the game of Bridge or Poker might be better if you have a basic understanding of the math associated with a deck of cards (total number of cards, number in each suit and how this translates to some basic understanding of odds).


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First -- let us state that the Sharpe Ratio is not a perfect measure by any means (hint: there are no perfect measures) --- but it is a useful framework to start with because it factors in volatility well.  High volatility is best associated with large drawdowns, a major concept to keep in mind.  What do funds with huge losses all have in common?  They were all very volatile.  

Drawdowns are obviously your enemy.  Trust though that you CAN control your drawdowns somewhat by controlling your volatility.  Sometimes you hear how investors want to pair their trades with short-selling.   Few investors actually do well with this structure, especially when they pay a big fee.  Yes, it's important to hedge your volatility --- but no, you do not need to pay a big fee to a short-selling fund just to reduce your volatility.  You can reduce volatility anytime you want and as much as you want and the cost is (should be) very, very low -- they are called short-term bonds.  (It goes beyond this post but you can do quite well by using some basic short-term credit strategies -- and then pair these with your core equity/risk strategies that are designed to deliver long-term return).   

You DO need to take on some volatility in order to generate some return -- but nobody who actually knows what they are doing is going to be impressed with making X% if you do it with massive drawdowns. 

So let's look at some basic math:


1) Compounding returns:

Let's start at some very basics, skip this and go to #2 volatility if you so desire.

Let's start with a 10% annual return. To convert 10% to a daily return, you only need to know how many days there are in a year.  Most traders will know that there are 252 equity trading days in most years (fixed-income and foreign markets like China have far fewer trading days due to extra holidays but this actually doesn't matter because the ETFs are open for trading on exchanges even when the underlying market is closed). 

So to take 10% down to a daily level in a spreadsheet:  place 0.10 in cell A2 and then in B2 type  =(1+A2)^(1/252)-1.  Now you have a daily figure of approximately 0.0378%.  Then practice by reversing it back to the annual 10%.  It may be easiest like this   =((1+.0378)^252)-1 = 10.000%.  

How is this equation useful?  Well, for each $100,000 in your account, a 10% annual return would be about the equivalent of 40 bucks a day (lower in the early days and higher in the later days due to compounding for an average just under $40).   $40 doesn't seem like much does it?  A lousy ~$40 on my $100,000?  Yes, that is what 10% a year feels like from a daily compounding perspective (40x250= $10,000). 

Let's move to a monthly figure as we find this to be a very useful timeframe, you will see why when we discuss volatility.  252 trading days in a year means 252 / 12 months = 21 days per month, on average.  Let's take 10% and convert to a 1 month (21-day) figure =((1+.10)^(1/12))-1 = 0.797% per month.  

Just to do the math a different way,  let's go from daily to monthly (from above) =  (1+0.0378)^21 = ~0.797% per month.

So that is return basics, now let's look at volatility.  If you can just get these 2 items (return and volatility math), you will have the 2 major Sharpe Ratio components:


2) Volatility is a slightly different animal but isn't as hard to think about as you suspect.  If something goes up a little bit ON AVERAGE each day (like the stock market) but not steadily compounding as in example above, then there is a different dynamic to understand.

Volatility grows not at time -- but at the square root of time.  It sounds scary but its not.  All that means is that even though the market moves around a lot on a day to day basis, if you look at it over a longer time period, much of those days offset each other and so you can't compound volatility like before because volatility relates to the path taken rather than just the final return result.   Think of it as more 3-dimensional analysis.  It's trickier -- but it's very important information.

Whenever you take the square root of a number greater than 1, the number is going to get smaller so  =sqrt(12) is obviously a lot smaller than 12.  This reduction is taking into account the fact that up and down days (volatility) offset each other to a significant extent.  So up +10%, down -10%, down -10%, down -10%, up +30% is an awful lot of movement but only gets you back a little positive.  The final return of +4.2% doesn't tell you about the path taken. 

  
So if we look at the S&P 500, we can say something like -- over the past X years, the annual volatility has been about [16%] (brackets just mean fill in whatever number you would like, this is just an example). 

Volatility is always discussed as an annualized amount.  But we can of course convert 16% to a daily figure using an equation like this  =(0.16)/sqrt(252) = ~1%.  

So given a forecast of 16% volatility, we would now not be at all surprised to see the market go up or down +/-1% tomorrow.   There are of course many other possibilities --- but we would not 'expect' it to move 5% --- because if it did, that would be too far from our baseline range of expectations.  Yes, it could happen but then that would be associated with rising volatility far above the example 16% used -- our 'forecast' of 16% was wrong in that case.  Remember, we are just trying to understand the mechanics of the Sharpe Ratio, we are not trying to forecast 1-day movement. 

Now, what practical use does this all have?  Well for one, don't try to pinpoint exactly why the market went up or down 1%, it's not a significant enough move when you are looking at it in the context of a few months.  Yes, there may be some headline news that day -- but many times the market will do the exact opposite of the nature of the morning headline.  It's just short-term volatility in play and it's normal.  

We think it makes a lot of sense to look beyond daily movement and toward longer windows of time -- but not too long.  A lot of modules on the website are set-up with this in mind.   We need a time-period that allows some of that offsetting volatility to work itself out.  There is no single magic time-period but various academic papers that we've been reading for the last 15 years point to 1 to 12 months as a good time-period to study.  Shorter than 1 month is too noisy and at 12 months or longer, the edge evaporates.

So let's take an example.  Let's assume a 2% 1-month return and 16% ANNUAL volatility estimate. 

First annualize the return:  =((1+.02)^12)-1 = 26.8%.    A quick comparison of 26.8% to 16% = 1.68,  that is a measure of return divided by a measure of risk.  You are risk-adjusting the return into a pretty straightforward ratio.  That is, you are incrementally penalizing the return if it took a wild path to get there.   If that makes sense to you on a conceptual level then you are 95% of the way there.

You should quickly see that 26.8% is an unsustainably high number.   You may have a great year  where you do far better than 26.8% --- but you won't be able to compound at this number for the long-term.   So here is the logic, since 26.8% annual is unsustainable -- that means 2% a month is unsustainable (it's the same number).   And while you may have many months far better than +2%, in order to get a good return/risk situation, you are going to have to also spend a little time thinking about the volatility -- you won't be able to do a good sharpe ratio based on return alone (over the long-run).  And of course, the real reason to control volatility is so that you don't experience large drawdowns.

Now let's look at a leveraged fund as that is an interesting phenomenon to think about.  A leveraged fund is DESIGNED to have 2x or 3x the daily return of the unlevered fund.  The funds will re-balance daily (if necessary) to achieve the next days targeted change.  As you should be able to see from above, we know it's a bad idea to trade off 1-day movement because historically (and consistent with the concept of volatility), the market doesn't go up at its long-term rate, it goes up and down in offsetting nature and then only over a larger sample period can we see a return figure that is not so highly skewed.  

What this means is that leveraged funds rebalancing daily is a serious long-term flaw and should not ever be held for significant periods of time.   This by the way is exactly what the trading in these funds indicates as most of the leveraged funds have low assets relative to their high daily volume amounts.  There is much trading during the day and then a lot of people flee before the close. 

Back to the Sharpe Ratio.  The ideas in this blog have gone into some math but should be thought about more conceptually in our opinion.  So to speak from these basic concepts, you could put this into a practical statement in a few different ways (just by re-arranging the key factors):

1.  We target a return of 10% a year.  We target volatility just below that.  (This implies on a conceptual level a reward-risk ratio of 1.0 or better).

2.  Volatility has been about 10%, we think we should be able to do a sharpe ratio of 1.0 so that means we think we will do 10% in return or better.

3.  We target a sharpe ratio of 1 or better -- we are tactical so volatility will fluctuate up and down -- we aren't going to micro-manage it but we can say that we will manage volatility such that it is no higher than the [S&P 500] index volatility.  This means/implies that our drawdowns should always be lower, too.

etc...

Now, let's pretend you were rotating between 3x leveraged funds.  How would you rationalize this if in a client meeting??

"Our funds volatility has been running 45% so hey, if we can just do a 0.5 sharpe ratio -- then expect us to compound at 22.5% per year"

Uhh no, the meeting would end there and you would be laughed out of the room.   Again, you can and should have some big individual years that put up some nice performance numbers if you execute your strategy well.  But we aren't talking about that, we are talking about long-term compounding.

Lastly, let's make sure we end on some common sense.  Don't get too mathematical about all this.  At the end of the day, this is about growing your account balance.  Making new all-time highs in your account balance and not having large drawdowns is efficient money management.  If you do that, who really cares whether you did X% or X+1%?  Don't lose sight of the big picture by getting too bogged down in the exact math.  An understanding of the concepts is needed -- an exact knowledge of every nuance in the calculations is not.

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A Look At Some Bond Market Segments

Mar 14, 2012 in Bonds

Let's take a look at some bond market action.   The bond market it far larger than the stock market so this should not be a topic you should just skip -- this is what 'the world' actually owns with majority of its money -- the bond market.   Even if you are primarily an equity person -- understanding some bond market basics will help you to understand what is going on overall in the markets.

While bonds mature at par and always pay a positive (nominal) return to maturity, intermediate and long-term bonds are very much subject to material drawdowns.

If we define the bond market to be the Barclays Aggregate index, let's take a logical look at what has happened when this key index drops.   That is, let's calculate how each segment does on both a relative and absolute basis when bonds get nailed.

As you can see in the table below, through last nights close, the bond market has lost at least -0.23% in daily total return change 101 times over the past 3 years (we effectively solved for the last 100 here).    Obviously, longer-maturities do worse and investment grade bond ETFs are not immune to this -- LQD has dropped on 89 of those 101 days and show a slightly larger loss than the overall Barclays Aggregate ETF return.

We prefer to show many of our big picture themes in our Allocation Board portfolios -- as talk is ultimately cheap.   On Feb 16, we lowered duration further than we were and mentioned in the dated comment the overvaluation of intermediate and long-duration bonds.  We moved incrementally towards a very defensive bond position.    In both E-ETFRE and E-ETFRS, allocations that both hit new all-time highs yesterday -- we are essentially all junk-bonds with a mix of very low duration fixed-income ETFs.  Junk bonds are down slightly today -- but that shouldn't be a big shock as the correlation of junk bonds to the overall bond market is not strongly negative on big down days.   Indeed, while junk bonds tend to rise on big bond market loss days -- as seen below, it's not a vastly dominant percentage of the time --- and that is certainly reasonable and very much consistent with very low correlated assets. 

 

The screenshot above is from the tools page app called Down Day Stats. It is on the Tools page with a number of other useful apps (some of which are free). Try it for other key markets --- like treasury bonds (TLT) vs Gold (GLD) vs Silver (SLV).   Or see how emerging markets act on days the dollar rallies (UDN goes down).  What is 'normal' behavior?   You need to have facts to understand what is 'not normal' as this can be very helpful in understanding what is actually going on.    If it all seems complex, that is because it is kind of complex.    The Euro, junk bonds, bank stocks, tech stocks -- how do they all interact??   Basing your investing on these key ideas is what money management is about, in our opinion --- not gambling on the next Netflix (NFLX) or Green Mountain Coffee (GMCR) earnings report.

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PIMCO Total Return Comparison

Mar 01, 2012 in Bonds

2011 was a dreadful year for PIMCO Total Return.  How bad?  Of ten major indexed intermediate bond ETFs (all with significant assets), PIMCO beat none of them.  These are essentially pure-play segments so this indicates very poor rotation by Gross during 2011.   Clearly the main problem was during Q3 when Treasuries (and the US Dollar) rallied very hard against them.

 

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