According to Bloomberg the worlds top tech stocks have gained $1.7T in value in the beginning of the year…….
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 –
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.
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.
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.
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.
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.
I recently posted this table of index price correlations.
Its a fairly simple table that looks at the degree to which indices follow one another in their general pattern of movement. As you would expect indices that are closely related share a very high correlation. For example the Dow and the S&P500 share a price correlation of 0.97 which is almost perfect. The natural expectation would therefore be that the returns from these two indices would be the same – investing in one would be as good as investing in the other. However, when looking at price correlations things become a little more complicated. The chart below looks at the value of $1 invested in both the Dow and the S&P500 starting just before the GFC hit.
As you can see there is a constant dislocation between the two. Price correlation and return correlation are not the same, in this instance the return correlation is a few points lower. To make matters more complicated all correlations are influenced by the time period you are looking at. The chart below looks at the value of $1 invested in the All Ordinaries and the Dow over the same time period.
There is a marked difference between the performance of the Dow and the All Ordinaries yet, according to our correlation table the correlation sits at a very high 0.74. What you think you see is not what you get – simply overlaying one price chart over the other would not have highlighted the significance of this difference. But this relationship is affected by the time period selected. The chart below starts much earlier and ends just before the GFC.
As you can see the situation is reversed with the local market belting the Dow. It is obvious that over different times different markets will display different returns and that this difference is not a function of their price correlation. This presents a series of conundrums for investors in terms of which market to pick to invest in and it also causes problems for the notion of diversification. With regard to the problem of which market to pick this can be solved relatively easily by looking at the relative performance of markets on a regular basis. This is actually quite easy to do since both Yahoo Finance and Google Finance offer a comparison function in their very basic charts.
The notion of diversification is a harder nut to crack. Diversification in the sell side of the industry is based around the idea that if things have different names then you are diversified. But this is a simplistic interpretation as you can see simply by looking at returns. It is possible for instruments to have different names but similar returns and if that return is negative then simply picking instruments on the basis of their name is flawed. To give you an insight into the issues that arise from looking too deeply at diversification consider the table below which looks at the differences in the price and returns correlation for the four major local banks.
As you can see the correlations are strong but quite different. If you were to pose the question as to whether the returns from all the local banks would be the same most would answer yes. Yet this answer would be wrong. In answering the question on diversification I have to admit a personal bias – I do not believe in traditional diversification for the reasons outlined above but also because I operate on a different philosophy. I only have a limited number of good ideas and if those good ideas share similar names then I will probably buy all of them. This as you would expect does introduce volatility into the returns as it sometimes goes wrong but it also sometimes goes right.
This does raise the issue of whether this is more of an academic interest rather than a practical one for traders. The issue of being in the right market at the right time is certainly a practical issue as local stock traders have largely been wasting their time since the GFC. The real gains in equities have been in the US as it has enjoyed one of the largest bull markets in its history whereas we have gone sideways with the occasional burst of short lived excitement. With regard to diversification I am ambivalent.
So said John Donne and the same is true of markets. One of the most fascinating features of trading is that markets at times they display interesting interrelationships and that these relationships tell you something about the underlying emotional state of the market. Below are three markets I am currently involved in and all seem at this point in time to be telling a story about how the market is currently coping with a President – Elect who seems to have the IQ of a trout and the stability of a slinky falling down Mt Everest.
As personal disclaimer I am currently long gold, short the USD in various iterations and my short term Dow system just threw me out from my last long position. So this is my story so it reflects my internal bias. What is interesting is that the Dow didn’t power through 20,000. I remember when it hit other “significant” numbers and it just burst straight through – there was no prevarication or hesitation. Yet at the same time the Dow paused gold began to move up and the USD Index began to move down. Whilst is is interesting to try an assemble a narrative from this – it is easier to simply trade the charts and let others build a narrative as to what they mean.
As a final point it is worth looking at how a few major markets have performed YTD – as you can see precious metals seem to be doing a fair bit of heavy lifting.
I came across this piece the other day. It is a good bit of work because it highlights neatly the interplay between returns, risk and drawdown. This triumvirate holds sway over the trajectory of our investing but it is continually ignored by most, as all traders in some way shape or form seek the biggest bang for their buck. The article looks purchasing AMZN from the perspective of a buy and hold investor and it would have been truly stomach churning experience – I can only think of one person who would have done that and it is Jeff Bezos the founder. I thought I would redo one of the charts from the article so the scale was a little clearer.
The chart below looks at the value of $1 invested in AMZN.
What I want to highlight is the decade long wilderness between 1999 and 2009 before the stock makes a new high – this is a very long time between drinks. It is a given that simple risk mitigation procedures such as having a stop ameliorate some of the problems generating by buying and holding and for simple curiosity I thought I would look at how having a simple 52/26 week entry and exit signal would perform. The rules are simple you enter on a 52 week high and sell on a 26 week low. To put this idea under a bit of stress I made the conditions for entry and exit disadvantageous by requiring the buy to be at the high following and the exit to be the low of the following week. The system not surprisingly only generated a few trades and they are as follows –
You will note that I have only looked at absolute dollar and percentage gains based upon purchasing a single share. So it not a truly exhaustive systems test by any stretch of the imagination. What did surprise me was that it only generated a single loss and that the stock has had a near triple digit gain whilst quite mature. The old adage price is irrelevant holds true.
There are probably two points to be gleaned from this. Tales of survivor bias that you hear from buy and hold investors do not reflect the true story because for every AMZN there are probably another ten that at best go nowhere or at worst simply disappear. This is particularly true for stocks that made their first appearance during the Dot Com bubble. And simple ideas work surprisingly well.