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Nothing To See Here

With a free afternoon I decided to look at the individual performance of stocks that make up the ASX 200 since the beginning of the year. What I did was assume that you invested $1.00 into each on the first trading day of the year and then see what their current valuation was. The table below maps them from highest to lowest.


It doesnt take a genius to work out that you will get a spectrum of values ranging from the very good – BGA to the very bad – ISD. The count is mildly positive with 109 stocks being at breakeven or above for the period. You can get a better sense of the distribution by looking at the frequency distribution of values below.

table 2

As you would expect there is a fat hump in the middles and two reasonably even tails. Some did really well, some did really badly and most didnt really do much However, this once again raises the question of the value of this sort of analysis and I would say outside of curiosity there is none. But it passed the afternoon and satisfied my curiosity.

Some Days A Rooster, Some Days A Feather Duster

I was reflecting on this post where I looked at the yearly return for stocks in the S&P/ASX50 and I wondered what one of the more ordinary examples would look like if I played around with missing the best and worst days of the year. I actually knew in advance what the shape of the curves would look like since it is incontrovertible that bad days have a disproportionate impact upon instrument performance than do bad days. This is due to a simple quirk in financial mathematics that a lot of people miss.

I decided to pick BHP since it is still an industry favourite and I got some spam this morning in one of my junk folders telling me how much extraordinary upside BHP had. What I did was look at the actual price performance of BHP and then proceeded to remove the 10 best and worst days from last year. The chart below shows how interesting this is.


I have done this exercise literally hundreds of times and it never ceases to amaze me the impact that controlling your losses has on your performance. If you had managed by some extraordinary feat of luck to remove the 10 worst days then BHP would have actually looked fairly reasonable.


EOFY Dodgy Excel Time

With the EOFY it was time to fire up excel and play with some stock returns. I took the S&P/ASX 50 and decided to see how each stock had fared during the year, this gave me the table below which I have sorted on the basis of highest to lowest.


In terms of observations about the years performance there really isn’t anything that hasn’t been discussed before but some points are worth repeating –

1. Generally the bigger you are the more modest your performance.

2.The older you are the more modest your performance – this tallies with what I have written about stagnation.

3.If you have been buying the largest stocks in the belief that they insulate you from rubbish performance or because you feel you know them then you are wasting your time. This is a constant theme – traders waste so much time because they do not take a step back and actually look at the market as a whole. They are too caught up in myopic thinking that stems from ignorance.

4.It is not surprising that fund managers cannot make money – they insist on sticking religiously to the points above.

5. It was a hard year for stock pickers.

It also wouldn’t be dodgy excel day without a pretty chart.



S&P/ASX 50

I mentioned in this piece that I should get around to looking at the original versus current components of the S&P/ASX 200 just to get a sense of index turnover. As a prelude to this I cobbled together a rough first look at the S&P/ASX 50 to see how much what I would consider to be a fairly stable index turns over. Those cells coloured in orange are original components that do not appear in the current index for whatever reason and those in yellow are common to both the original and current index.. This is a quick effort so I am bound to have missed something but if you needed proof of concept that indices turnover a great deal then this does give you a good idea of how much things change.


ASX 200 Shares Distribution Of Returns

Recently I had a look at the distribution of returns for a system I run – the aim of this was to give a sense of how systems trading works. The rules of systems trading are very simple and are based around the concept of ride the losses, pump the winners and allow time to do its thing. Following on from this I thought it might be interesting to have a look at how the individual returns for shares that make up the ASX 200 were arranged and to see if this had any lessons hidden in the data. From my perspective it did prove to be interesting but then again I find the structural data of the market interesting.  The first thing I needed to do was to calculate the 10 year annualised return of each stock. There are a few caveats with this sort of exercise and they need to be understood before interpreting the data.

1.This is the S&P/ASX 200 as it stands not the S&P/ASX 200 since inception. As a result survivor bias is an issue.

2. Annualised returns can be misleading. It is possible for a stock to do nothing and then have an explosive single year. This single year distorts the data upwards.

3.Likewise it is possible for a stock have an appalling year and have the average result dragged down.

4.Some stocks in the S&PASX 200 have very limited data with some trading for less than 2 years – this gives a distorted appearance to their returns.

5.Do not assume that this is the return generated by the instrument every year. Pay attention to points 2 and 3.

When I tabulated the data I got the following –


Upon initially viewing the data in this raw format the thing that struck me was the amount of rubbish that is listed locally and which for some reason qualifies to be in the markets benchmark index. Several of these companies have limited price history and some have applying price discovery as evidenced by how illiquid they appear. What is also obvious is that many listings have a long term negative pay-off. However, as stated we need to be a little bit careful when making this statement because of the way this data has been calculated. What did surprise me was that I thought that more stocks would have a long term negative return and then I remembered my own warning about survivor bias. The S&P/ASX 200 is turned over regularly so the dogs are flushed from the system and new stocks are added in an attempt to pump the index. My guess is that only a handful of stocks from the original listing of the S&P/ASX 200 still exist (note to self – research project for later) This is why indices have an upward bias.

This data by itself is interesting but doesn’t really convey much information so I decided to have a look at the frequency of returns for the index and this yielded the chart below.


Interestingly, there are more positive returns than negative returns – my explanation for this is once again its a problem associated with survivor bias. However, it is always interesting to see the clustering of results around a given point. In this set of data the mean return is 13.65% with the median being 10.25%. I then started to segment the data into various bins to see how different segments looked. The S&P/ASX 20 showed me an interesting pattern. I did  this because I made the assumption that the S&P/ASX 20 would be the most relatively stable collection of stocks and it gave the following distribution of returns.

top 20

What is interesting to me is how average the returns for the major banks are over this period. Granted the starting point of the data does include pre-GFC highs but this is also a problem for all stocks in this sample. It is also important because with the failing of BHP and RIO advisor’s tend to overweight portfolios with these stocks. Breaking the data into segments lead me to splitting the data into the top 100 of the index and the bottom 100 in terms of market capitalisation. Doing so confirmed something I already knew. Larger cap issues have less room for outsized returns. The top 100 generated an average return of 11.81% whereas the second tranche generated a return of 15.56%.

This is a significant difference and leads me postulate if given the various assumptions inherent in the data if it is not better for investors/traders to hunt outsized the major index components. I know this to be true from my own systems testing and stock selection criteria. The reasons for this are many but my assumption is that it is a function of leverage and stagnation. Smaller issues have a lower market cap and make up the bottom tier of stocks. This leverage enables larger gains. The stagnation element is related to something I wrote last week. Companies have periods of explosive growth and then they begin to stagnate. Larger companies that have extended history are largely moribund in their thinking and business strategy. For example, if you ran one of the big four banks you would know that you would make extraordinary profits by being ordinary. Given that this is your default setting why would you do anything to change this. It is not in your interest as a senior executive or company director to do anything out of the ordinary. If by some whim of fate you were suddenly made CEO of one of the major banks your instruction ot your staff every Monday morning would be to do exactly the same things you did lat week, the week before that and the week before that. You could then collect your $18,000,000 plus salary and bugger off and play golf for the remaining four and half days of the week safe in the knowledge that the money would simply roll in.

From the perspective of the average trader this data does convey some lessons, most prominent of these is that size matters in limiting returns. This data merely confirms what systems traders have known for decades. However, I think there is something more important in being granular in your view of data such as this. It actually tells a lot about the structure of the market you are trading in. Too often both investors and traders merely look an index and assume that its components are probably homogeneous and therefore not worthy of further examination. In fact I have come across many in the advisory index who were unaware that stocks were dropped from and index and then replaced hence the upward drift of an index. They just merely assumed that the index always went up because of some magical reason. What is worse is they also assumed that every stock in that index went up as well. Looking deeply at data removes these preconceptions and puts you int touch with what the nature of the market is. It also and most importantly makes certain that your trading system is performing optimally by giving you an edge in the stocks you look at.

Buy Hope And Pray

Whilst out at lunch the other day I spied a couple reading this article . The article itself is nothing spectacular, it merely notes the fact that one analyst who covers BHP had placed an underweight  rating on the stock back in May. Now I freely confess that despite having been a broker I have no idea what underweight means, other than I dont have the balls to commit to saying its an outright sell. If it looks like a duck, swims like a duck, and quacks like a duck, then it probably is a duck. Underweight is a weasel word if ever there was one. However, the point of this diatribe is not the many and obvious failings of the sell side of the financial industry it is the emotional reaction of the couple reading the article to the suggestion that BHP was a dog. I have to confess the emotional attachment of people to inanimate objects has always fascinated me. We have a strong tendency to anthropomorphism and this magical thinking extends strangely enough to stocks. People have a relationship with stocks that is in many ways akin to internet stalking, they watch their stock from afar, follow its every move and are titilated by even the smallest hint of perceived recognition in the form of a rise in price. My lunchtime companions even suggested that placing a sell rating on BHP was tantamount to treason.

What has is intriguing about the price trajectory of BHP is that it has been so ordinary for so long, but this doesnt seem to have deterred investors both amateur and professional from making complete idiots of themselves by buying the stock. Consider the chart below of BHP. On this chart I have dropped new 52 week highs/lows – this is not a sophisticated tool but like all simple tools it tells an unambiguous story. the last 52 week high for BHP was in February 2014 – since then it has been one way traffic.

Screen Shot 2015-11-15 at 12.07.31 PM

I thought it would be interesting to compare the performance of BHP since the GFC to other members of the S&P/ASX 20, so I generated the following table which looks at the value of $1 invested into the stock on 13/3/09 which I have taken to be the recovery week post the GFC low. interestingly, BHP is not the worst performer. That honour goes to ORG which has managed to obliterate any investment one might have had in the stock. Still, if you held all the way down then thats your problem.

Screen Shot 2015-11-15 at 12.08.42 PM

I thought it might be interesting to graph the rank the relative performance of each stock and see what the pattern of performance looked like.Screen Shot 2015-11-15 at 12.08.55 PM

Narratives combined with emotion are a dangerous combination for investors. We can talk ourselves onto anything if the story lines up with our pre-existing bias and desperate desire to defend our ego.

Charts Of interest 30/10/15

Screen Shot 2015-11-01 at 5.04.03 PM Screen Shot 2015-11-01 at 5.04.12 PM Screen Shot 2015-11-01 at 5.04.22 PM Screen Shot 2015-11-01 at 5.04.32 PM Screen Shot 2015-11-01 at 5.04.41 PM Screen Shot 2015-11-01 at 5.04.50 PM Screen Shot 2015-11-01 at 5.05.02 PM

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