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The Order Of Things Matters

Every now and again I get sent a magic trading system complete with equity curve, generally these are some sort of magic system someone is flogging that promises massive returns and never has a drawdown. They are the sort of quit your job with $10,000 and intra-day trade FX and make $10,000 per week. If you have been around markets long enough you will have seen this sort of thing – I have to give them some credit because at least they include and equity curve as opposed to simply quoting some mythical average return like fund managers do. Equity curves do convey a lot of information – they tell you about the trajectory of funds that have been invested. You can get a sense of how bumpy the journey might be and whether you could stomach the trip. However, the thing they do not tell you is the role of luck in achieving those particular returns. The returns a trading system generates and in turn its equity curve are uniquely sensitive to luck – not so much in the sense that the trader may have gotten lucky and run into the largest bull market in history which is entirely possible. But rather they are completely dependent upon the order in which the returns where generated.

To give you a simple example of this consider the chart below. In this chart I map the value of $1 invested in the All Ordinaries and $1 invested in the All Ordinaries but with the returns reversed so the return for 2015 becomes the return for 1900 and so on.

True Vs Reversed

I have plotted these on a log scale so you can get a sense of the journey – you can instantly see how simply reversing the returns changes the track of the curve. The reversed values lag behind the true values for 2/3 of the time, it lags for the first 30 odd years, catches up and then begins to lag again from the mid 1970s’. The true returns have a terminal value of $437,097.87 whereas the reversed values top out at $420,087.87. Simply changing the order costs the system $16,941.77

The same is true small changes in return – in the true return the years 1985 and 1986 were power years. They were the high point of the 1980’s bull run in terms of absolute returns with a return of 44% and 52% respectively but in looking at returns the question needs to be asked as to what the curve would look like if these were just average years of 9% return. Traders tend to spend too much time thinking about all the ways it is going to go right but very little time is spent on what could go wrong.

Expanded

The true values have the same terminal value of $437,097.87 wheres the changing of 1985/86 to average years drops the return to  $264,251.37 – a difference of $172,846.50. Whilst this does make for an interesting through experiment it also has practical implications. Trend following systems are built upon the outlier years – this is what generates their returns. If you miss these outliers then your returns over time will be ordinary. Traders do have a habit of missing these years simply because they are either caught in someone else’s narrative and miss market moves, they dont believe the move when it happens because they have a preconceived view of how much an instrument/position is worth or they are caught by the limiting belief that you can never go broke taking a profit. I lost count of people in the mid 1990’s who thought that COH was overvalued at $3.00. Granted its path to $157 has not been linear but its move to $45 was as close as you can get. This move is gone forever for such traders and will never return.

The same situation applies to the random reordering of returns. The chart below is the true returns compare to a randomly reordered sample of the same returns.

Random

The randomly reordered returns show almost a century of relative under performance including a substantial initial drawdown. So when you look at an equity dont take it as gospel, think  of the ways in which the journey could have changed with a few simple alterations or slip ups along the way. The overall aim of any form of system design is to produce a system that is incredibly robust and which shows profitability over a wide range of conditions and events.  In trading you need a little luck but you do not want to be dependent upon it.

Road To Nowhere

It has been awhile since blog central  had a look at the All Ords and its staggering lack of ability to go anywhere. The full extent of the rubbish nature of the local market at present can be seen below when the performance is compared to the S&P 500 and NASDAQ 100.

performance

This lack of movement becomes more apparent when we chart price movement on a weekly basis.

Ords Weekly

The current consolidation sits within a much broader malaise that saw the market fail to puncture 6000 earlier in the year.

Ords Daily

From a trading perspective there is very little on offer when the index is simply drifting sideways. However, there is always a silver lining to these things in that the longer this compression continues the more interesting the breakout will be.

YTD Performance

As we sail past the half way point of the year I thought I look in the rear view mirror might be interesting to see how a few select markets have performed. There are no prizes for guessing that the local market has been shit.

ytd

 

The Great Idiot Tax Continues

It is at this time of the year when superannuation funds crow about how good they have done and of their inestimable benefit to mankind in general and this year was no exception.  So as is my now annual tradition I thought I would have a look at how good they have done and compare that to the real world where delusions about how good you think you are dont exist. From the article I linked to I took this table which looks at the average return of a a growth fund since 1993.

Screen-Shot-2017-07-03-at-1.44.22-pm

Source – Superannuation returns above 10% for the June Year

This piece acts as a good starting point for comparison with the market. For this I used the All Ordinaries Total Return index which used to be known as the Accumulation Index. It includes not only the price movement of the index but also folds back in the dividends of the index components so it is a good benchmark for simply passively holding an index fund or ETF.

Capture

When the chart of the average return of a growth fund is first viewed it does create an overall favourable impression – there are only three negative years and returns seem overall to be quite robust. It is only when you compare this active management with a passive benchmark that you realise how poor local managers actually do when compared to the index. Remember these are people who are paid to beat the index and as we will see they are paid staggering sums of money. Looking at annual percentage returns is quite crude and does lack a bit of fidelity, you dont actually know what the true performance differential is so I looked at the value of $1 invested into an average growth fund and into the index and got the following.

C2

The market leaves the industry for dead – the market investment would now be worth $9.87 versus the industries $5.91 and for this privileged investors have been ripped off handsomely. The chart below looks at what my guess of the annual fee intake of superannuation funds is. For this I have assumed an average fee of 1.5% to cover not only management fees but also advisor commissions.

c3

So to produce a theoretical return of slightly better than half what the market produced  in the period above the superannuation industry has collected probably close to $310B in fees. So to once again steal from Winston Churchill – never in the field of human endevour has so much been paid to so few for so little.

So What Does This Mean?

In my junk folder I have for the past upteen decades been getting random charts by a group called Chart of the Day. Surprisingly, I dont get them everyday – so the implication that you get a chart everyday that is interesting is perhaps a little bit of an oversell. This morning I go the following piece of wisdom –

c2

This chart as the title suggests looks at the S&P 500 PE ratio back to the turn of the century. Putting aside the obvious gaping methodological flaws such as the S&P500 was only started in 1957 I do always find these sorts of things interesting. Markets and their history should be a topic of investigation for every trader, simply because there is nothing new. Bubbles and crashes have been a feature of markets since they began and the driving force behind such things has always been the capriciousness of market participants. Curious as to what our own market looked like I dug up some data from the folks at Market Index and plotted the local PE ratio against the All Ords to see what I could see.

Capture

On the chart above I dropped a series of vertical lines – the three black ones denote a time when valuations according to the markets PE ratio could be considered extreme, the red one is the GFC. Pundits who look at valuation models work on the notion that markets or their component equities have a fair valuation and deviations from this point indicate that something is either overvalued or undervalued. Decisions are then made upon this interpretations. The first black line is easy to identify – its the 1987 crash.  The second one took me a little while to remember until I remembered the tail end of the 1991/2 recession combined with the banks nearly sending themselves under after property bit the dust.  The third black line is the tech wreck, The question when looking at any methodology is what value does it add to your decision making.  This is an important question since our decision making is bounded by the time we have to make the decision, the amount of information we have and our cognitive ability. None of these components can be infinite so our decision making is always somewhat half arsed. However, we need to add to this the notion of decision fatigue. It is estimated that during an average day we make anywhere between 20,000 and 25, 000 conscious and unconscious decisions and each of these decisions extracts a toll. Decision making is not a free ride, everything has a cost. Therefore efficiency of decision making is of paramount importance. If you have to force a decision then you are merely adding to your own mental loading without achieving anything.

As to whether the chart above tells me anything I dont already now about market extremes is doubtful As to whether it adds anything to my overall view of the world and approach to trading I am certain it doesn’t. But your mileage may vary.

Property Versus Shares

I was mucking around on the Valuer Generals site the other day searching for historical bits and pieces relating to my property when I noticed that the VG kept historical records on their estimation of the median house price in Melbourne. One of the things I have always found difficult in real estate is not the paperwork, the land rats, tenants, maintenance or the incredibly primitive way that houses are actually sold but rather the paucity of data that surrounds their instrument. Reliable and consistent data seems to be very hard to find and this was an issue I found when I was looking the the VG estimations – I couldn’t get them to tie in with other bits and pieces I found. However, only the VG site had any depth of historical information. Imagine trying to deal in a stock that had half a dozen conflicting prices from different sources, none of which you could actually deal in because prices are largely made up and then trying to find out what the price was five years ago only to get another half a dozen differing prices. It seems as if the real estate market is deliberately set up to be obscure and in some ways reminds me of an embryonic options market where those involved either didn’t understand their market very well or were being deliberately opaque.

Out of curiosity I downloaded the VG’s median house price data just to have a look at the trajectory. Because I am frequently bored I like to look at the history and structure of various markets – too few people are actually students of the markets they operate in. As such they miss out on a large number of free lessons that can short cut their process. No one makes original mistakes in their investing, everyone has made the same mistakes before you and the lessons from these mistakes can often be found in the data. Whilst mucking around with the data I remembered that the ASX often produces a comparison between the returns that are generated by various investment categories. I have always thought that these comparisons had a flaw in that they relied upon simple average returns, looking at averages is fraught with danger because they can be extremely misleading. Which is why managed funds constantly quote them.

As an extreme example consider the following investment scenario. I discover a magic fund with brilliant marketing material and on day one of year one I invest $100,000. In the first year the fund makes a return of 100% and I think I am a genius. In the second year the fund loses 50% and naturally I think the fund manager is an idiot but I am consoled by the fact that the year before I made 100%. When I present this scenario to people I ask them what the average rate of return is for those two years and most people answer correctly – it is 25% (100%-50%/2). I then ask how much have I made on my original $100,000 and I generally get an answer in the ball park of $100,000 x 25%pa. These guesses range from $125,000 to $150,000 as people try and do a compound interest calculation in their head. The truth is I have made zero – in the first year I doubled my money and in the second year I halved my money thereby returning me to my starting point. Yet my average return is 25%. This is why when looking at returns we have to be careful about using a long term average to generate an idea of how much we would have made. It is better to look at each individual piece of return data and assign a dollar value to it. This way you can build some form of equity curves which gives you a lot more information as to the trajectory of the value of your investment.

For a bit of fun I decided to take the data from the VG’s site and apply the returns from the All Ordinaries Total Return Index to their initial starting capital of $75,500 and see what the comparison between the two was. In effect I built an equity curve for the median house price and an identical investment into a surrogate ETF.

hp1

I have to admit I was a little surprised at the size of the differential because when you hear talk of comparisons between the two investment vehicles the impression you get is that the returns are quite close and that with the runaway bull market in housing that property has been the place to be for long term passive investing. Plotting data like this enables you to get a sense of the trajectory of price and to me two things are immediately apparent. Equities are more volatile in terms of a passive investment and this volatility is apparent in the impact of the GFC. Property moves a little like a truck, slow and steady whereas equities tend to throw themselves around a little. However, the shocks are not as severe as I thought they would be, 1987 is a blip that doesn’t appear and the tech wreck was a mild impediment. What did do the damage was the GFC and this is the problem with a simple buy and hold methodology.

To compensate for this volatility and to give a more real world flavour to our surrogate ETF I dropped the loss from the GFC to 10% from the historical 40.38% which is reflective of what actually happened when our macro filters kicked in and dropped us out of the market. The result of this simple fix is interesting.

hp2

The dramatically different result is simply a function of controlling runaway losses and not allowing them to have a detrimental impact upon your equity. Such a technique  is not rocket science but it does seem sufficiently difficult that it eludes all professional money managers.

Despite what the data says I am doubtful that it will convince die hard property advocates of anything – people with firm opinions are immune to data and it is hard to break the emotional bond that people have with actually owning something. And that is not really the purpose of the exercise as the advantages of equity investing over property investing are many , manifest and quite easy to elucidate. But is does serve as a salutatory lesson in what the differing mechanisms of presenting returns can tell us. It also tells us in no uncertain terms as to why  the worlds second richest individual is a share investor and not a property investor.

 

 

Where Do Returns Come From?

With the market making a valiant effort to push past 6,000 points I have been pondering where the gains in the market have come from over the past few years. In the past I have looked at the distribution of returns across groups of shares simply from the perspective of demonstrating that within a universe of instruments the majority of returns come from a few entities. Naturally the same is true for trading systems – a few trades each year provide the bulk of the returns. This feature of trading is one of the hardest for new traders to adapt to since they assume that trading is like other jobs where you are rewarded at the same rate for the same amount of effort.

I thought about broadening my horizon to look at the balance of returns that occurs between price movement and dividends across a given market since this data is reasonably easy to obtain and therefore manipulate.  I took a look at the difference between the All Ordinaries index and the All Ordinaries TR index.  As a simple raw observation the All Ordinaries hit its nadir post the GFC on 13/03/09 when its low hit 3090.80, its last full weekly close was on 21/04/17 when it closed at 5885.60 – this is gain of 90.42%. The All Ordinaries TR had a low of 20,858.64 on 6/3/09, since then it has climbed to 55,605.18 – a gain of 166.58%. There is a substantial difference between the long term rate of return of the two indices. Unfortunately, this differential is often seized upon and used by the buy and hold brigade to offer the mantra of simply investing for dividends but I don’t think that is what the data is showing. What the data shows is the naturally higher rate of compounded return from simply folding back dividends into the equation. It doesn’t offer sufficient evidence to support the line of investing simply for the sake of dividends. If it does offer anything it gives a glimpse into the usefulness of a sensible index ETF strategy.

I wanted to take a deeper look at the differential between the weekly returns from the two indices to see if it told me anything deeper about the relationship between the two and the actual stage of the market. The graph below demonstrates the differential between the two dating back to 1996.

differential

As you can see the data is fairly noisy but there are one two points that you can generate from it. There is a distinct scalloping in the data in the early part of the century, my interpretation of this is that this is a natural function of a market running hard. In fast moving markets the majority of index gains come from the generation of dividends. As you move beyond the GFC you can see an expansion in the differential – dividends as a source of internal return have become more important. This is to be expected in lacklustre markets. My feeling is that the downward pulses that you see post the GFC are in part due to price attempting to recover but also are a function of the cyclical nature of dividends.

Whilst interesting I am not certain it tells us anything we didn’t already know. However, when I was preparing this I did have a recollection of an analysis I used to do by hand before the age of boundless and often needless information began to distract me. In the past I used to had chart the All Ordinaries dividend ratio and use it as a measure of market energy and I though ti should be easy enough to see if some clever clogs had done the work for me. And courtesy of two seconds on Google I found someone had. The chart below is from Market Index a group that provide and absolute plethora of markets statistics.

Yield All Ords

As you can see it presents a similar but much clearer picture of the ebbs and flows of the actual dividend yield of the All Ordinaries. As can be seen when price is running strongly the relative impact of dividends decreases – that is the overall yield drops. When markets are doing poorly the average dividend climbs as price collapses. I will leave it to each individual to see if this sort of information adds anything to the veracity of their macro trading decisions.

 

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