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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.

 

 

The World Is Changing

irrelevancy

Source – Bloomberg Gadfly

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.

 

Timing Is Everything

It is no secret that I am not a fan of fund managers of any kind, be they the more exotic style of hedge fund that exists as an idiot tax for those who invest in them, the standard vanilla equity investment fund or the legally mandated rip off that are superannuation funds. My objection is simple, if you are going to take billions in fees  from people then you had better deliver something other than perpetually under performing the market. In a puff piece that looked somewhat like a marketing exercise Morningstar the ratings agency has named the top investment funds in Australia and the list was picked up by Fairfax and covered here. I have copied the list of top funds below.

1491953010731

Source – Fairfax

The article talks about the value of investing in the number one ranked fund since it has outperformed the index over the last 10 years – this point got me thinking about using 10 years as a point of comparison and the notion of starting points in general. As a general point I find selecting 10 years interesting since it makes certain that the funds are compared against the index during and post the GFC and it doesn’t take a genius to work out that the average return from the index since that time has been poor.

I decided to have a deeper look at the impact of starting times upon portfolios by digging up some data on the All Ordinaries Accumulation Index which is now referred to as Total Return Index since it includes the return from both gains in the index and dividends. Starting in 1960 I looked at what your average return would be to the present day if you had started investing at a given point. For example if you had invested in an index linked fund in 1960 your average return up until the present date would have been 13.53% whereas if you had begun your investment journey in 1994 your average return would be 9.99%. This might not seem to be a substantive difference but over time it adds up to a small fortune.

Capture

What interested me when I looked at this data was the remarkably consistent nature of the returns – they are all positive. Whilst, this is to be expected it is nonetheless interesting that the index doesn’t put together strings of negative years and this is shown in the raw data that I will look at later. What is also evident is that there seems to be a tailing off in average returns which is more obvious when this data is plotted as a chart.

r2

The reason for this drop off can be found in the raw data as shown below. This data is the true return for the index for each year in the sample.

r1

In this table I have highlighted each year where the return was over 25%. You can see a cluster of such returns in the 1970’s and 1980’s with a drop off in 1990 and 2000 and since 2010 there has not been such a year. In performance outliers count disproportionately and when they are lacking things look bleaker. I have no real explanation as to the rationale for the drop off in returns although I would surmise it may be simply due to a lack of funds flowing into the market due to the real estate boom. My recollection of the lift in 1991 and 1993 where due in part to the pent up recovery in the market post the 1987 crash but also that real estate struggle under the regime of high interest rates so we had an asset rotation underway.

Despite this drop off in returns over the past decade there is still no compelling reason to buy a managed fund. However, it is important to note that these are average returns over deep time – it in no way diminishes the importance of not being in the market when the market has nothing to give you. What also amazes me about fund managers is that they believe timing is so hard when in fact with simple mechanical rules it is remarkably easy as I have already demonstrated here using the ETF STW. Trading is only as hard as you make it or in the case of fund managers it is only as hard as you want to make it appear.

 

 

 

 

Winners and Losers

I have been thinking some more about the issue of short selling and the problem faced with the upward bias of equities. Armed with excel I decided to look at the average gain as a function of the average loss for the stocks in the S&P/ASX 200 – once I had the data it was a simple matter of dividing average gain by average loss for the past year and seeing what the data said.

Gain_Loss

I have colour coded the data in the following way – those coded blue are above average, those coded orange are below 1. The higher the number the better the performance of the stock in terms of its average gains versus its average losses over the past year – this doesn’t necessarily mean that the stock was a runaway winner in terms of trend trading but rather it had a strong propensity to make good its losses. It should also be noted that all this does is tell us a little bit about the past and nothing at all about the future. Ideally, if you were a stock picker you would want to look back and see a high number and a strong propensity to trend over the long term – the winner for this period of data is WHC.  In terms of losing stocks a ratio of 1 could be interpreted as the stock simply meandered during the year and congested and a ratio of below 1 is a bad sign.

What is interesting to me is the strength of the upward drift in stocks. However, it does need to be considered that the S&P/ASX 200 is a biased sample size since stocks are in effect selected for their upward drift. My guess is that if I were to repeat this list next year some 20% of these stocks would baring some miracle have been dropped from the index and replaced.

 

 

Even The Best Stock Pickers Cant Beat Bots

BlackRock shook the world of active management on Tuesday when it announced that it had fired five of its 53 stock pickers. BlackRock will also move $6 billion of the $201 billion invested in traditional active management to quant strategies.

The announcement may not sound earth-shattering, but it augurs a larger trend: Traditional active management is dying, but perhaps not for the reason you might think.

 The evidence has piled up in recent years that the vast majority of active managers fail to beat the market net of their fees. A common reaction is that beating the market is too difficult and that it’s therefore a waste of time and money to try.

But just the opposite is true. As I’ve previously noted, the problem is not that active managers fail to outperform the market; it’s that they keep that outperformance for themselves through high fees. In the meantime, index providers have turned traditional styles of active management — such as value, quality and momentum — into shockingly simple indexes run by computers. Those indexes have beaten the market and are now widely available to investors as low-cost smart beta funds.

Smart beta has proved to be a popular alternative to traditional active management. According to Morningstar, investors pulled $313 billion out of actively managed mutual funds over the last five years through 2016. At the same time, they invested $314 billion in smart beta mutual funds.

More here – Bloomberg Gadfly

PS: This is not so much fear the bots as fear the model. It has long been known that humans cannot beat simple models that can be run on home computers. Stock pickers or analysts generally do not have a model for stock/instrument selection rather they have a very loose aggregation of factors that are based around the need for a compelling narrative. And narrative is the least reliable mechanism we have for making a judgement since it more often than not flies in the face of data.

WTF Is All The Fuss About

This article is apparently doing the rounds and it purports to look at the supposedly new development of predatory short selling and uses the attack on Quintix by the US group Galucus as proof of this and along the way we get the usual dose of perceived wisdom from Gerry Harvey. Whenever such a piece on short selling appears it is predicated on a few basic assumptions.

  1. Short sellers drive down the price of instruments thereby engaging in a form of pseudo market manipulation
  2. Short sellers tend to target decent businesses and decent people and are therefore un-Australian
  3. Short sellers know what they are doing and are always profitable.
  4. Knowing which stocks are being shorted will give you an edge.
  5. Predatory short selling is a new development.

The article identifies a series of stocks that are among the most shorted on the ASX and I have reproduced this list below since it gives me a starting point for looking at some of the actual data surrounding these stocks.

shorts

What I wanted to look at was some of the performance figures that you might derive from shorting these stocks. The first thing I did was assume that exactly one year ago 1 shorted $1 of each of these stocks. I then valued these stocks as of last nights close and generated the following table.

value of $1

The current value of this basket of stocks is $10.9, so in a year I have made $0.10, if I had simply bought the index and held it passively for the same period I would have made $0.11. Speculation has to be worth the effort, particularly speculation such as short selling that exposes you to substantial risks and can be regulatory and management nightmare. However, this sort of comparison is unfair since short selling is a trading strategy – it requires active management. So it would be more appropriate to look at the peak to trough movements in these stocks over the past year and this is what the table below tracks.

up_down

As can be seen some of these stocks have had substantial movements in the past year and there are only three where movement to the upside outpaces the move down. Interestingly, as a statistical fluke the average gain and average loss sits at 31%. From a trading perspective there always needs to be a recognition that stock prices move in both directions – unfortunately for passive investors fund managers only seem to accept that stocks prices move up. The value of short sellers is the knowledge that markets move in both directions and that this provides an opportunity for profit. However, this raises the additional question of whether short selling has both an influence on price and is utilized to make a profit. For this to occur large short selling positions need to be put in place whilst the stock is stagnant and then used to drive prices down. Therefore we should see an increase in the number of shorts before a stock falls and for this number to accelerate as pressure was brought to bear. To test this assumption I looked at some charts from the Shortman.com.au site which is used as the basis for the first graph in this piece and I have reproduced them below.

table

To be honest I am buggered if I can see a relationship between the lift on the number of short sellers and a decline in price. What I see is a mixed bag of short sellers being late, being early, not being there at all and getting lucky. Granted using the old Mark 1 eyeball is a dangerous thing and I cant extract the data to look at the true correlation between short sellers and price. But if it isn’t obvious then crunching it statistically to find some form of relationship isn’t reliable. So we come back to the basic questions I posed above and it is worth summarising an answer to each –

Short sellers drive down the price of instruments thereby engaging in a form of pseudo market manipulation

Not from what I can see. In fact if anything short selling is a boon to the market since it aids in liquidity, price discovery and as ASIC found much to its chagrin during the GFC it dampens volatility.

Short sellers tend to target decent businesses and decent people and are therefore un-Australian

People who complain about short selling fail to understand the basic mechanics of all trading is to elicit price discovery. The marekt then votes on its future view of this discovery – markets look forward not nackswards. So when you see the price of groups such as HVN get the wobbles it is the market voting about what it perceives to be the future prospects of this company in light of changes in technology, consumer bahvious and competition.

Short sellers know what they are doing and are always profitable.

Not from what I have seen

Knowing which stocks are being shorted will give you an edge.

See above – also consider the most you can make is 100% and that is functionally impossible. Simply Google best performing stocks of 2016 and this will give you an idea of the side of the market you want to be on.

Predatory short selling is a new development.

From my historical perspective I would say that short selling now is harder than it used to be. There are restrictions on naked short selling and the settlement system we operate under makes it hard to game the system. Back in the day when we had 14 day settlement you could short sell a company and buy it back before settlement and if you were careful no one was any the wiser. With instantaneous settlement this is actually very hard to get away with. As I said from a simple back office perspective short selling equities is a pain in the arse.

 

 

 

 

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