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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 Final Clarke and Dawe


 

Compounding – if you live long enough to enjoy it.

I have just finished reading Edward O Thorps autobiography A Man For All Markets which is an excellent little read and a good addition to any traders library. In the book Thorp talks about he value of compounding returns. There is no doubt that success is trading or investing is based upon compounding your gains over the long term. Compounding is a wonderful tool in that what seem to be small quanta of difference can over time lead to an enormous difference in returns. For example an investment with a return of 10% compounded annually for 10 years yields $259,374 whereas the same investment compound at 11% yields $283,942. Extend the holding time to 20 years and the figures becomes $672,750 and $806,231 respectively. Time is the key to compounding and this is a point Thorp makes, he also makes the important point that most lack the patience to do this.

However, there is a sting in the tale of compounding that I have noticed that those on the sell side of the business either abuse or simply do not understand and that is one of scale. You will often see very long term charts of an index or an instrument and it shows a wonderful upward trajectory (well you wouldn’t show things that didn’t work) and the message is that you simply have to hold for whatever the requisite time is and you will eventually have a small pot of gold. The key word here is eventually because what is often overlooked is the time to achieve these mythical gains. There is no doubt at all that compounding is a very powerful tool and when combined with consistency and patience achieves remarkable things.

However there is always a but we need to be aware of. To demonstrate this I found a centuries worth of data on the All Ords and using $1 as the starting investment plotted what the return would be over the next 116 years.

$1

If you had started with $1 in 1900 and simply let the compounding returns of the index take its course you would have $487,801.23. At first glance this is quite impressive – the markets very long term rate of return sits at about 9% and if you let it do its thing for a long period of time then you get an impressive number at the end. However, there are two things to be aware of in viewing this data. Firstly, the time taken to achieve your goals, not only is the time itself a problem but the erosion of the value of your investment over time is a problem. I had a cursory look for long term inflation data but couldn’t find much dating back beyond the 1940’s but if you assumed an average inflation rate of 4% then this puts a large hole in the real end value of your investment. The second issue that is not addressed is the trajectory of the journey – the chart above is not of a capital guaranteed term deposit but of an index. The somewhat linear trajectory of the graph is deceiving since it does not take into account the extended and deep bear markets that were experienced. There were years when the market went nowhere and these events are testing for even the most hardened buy and hold advocate.

Time is both the ally and enemy of those who understand how to use compounding and it is this dualism that we need to be aware of. The practical implication of this is to leave your money in your trading account for as long as possible before taking it out and spending it. The impact of large withdrawals is quite remarkable in the damage it does to accounts but some people cannot resist spending in the short term to ensure they live in poverty in the long term

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.

 

 

 

 

A Basic Misunderstanding

The chart below is from a site called Spurious Correlations, it takes seemingly disparate facts and matches them together to create the illusion of a positive correlation. It is a simple and effective way of illustrating the problem of mistaking causation for correlation which is constant problem in the way people both think and view data.

correlation

If you were to take this at face value you would accept that there is a link between the number of films that Nicholas Cage has appeared in and the number of people who drowned by falling into a pool. You could even stretch the data a little and suggest that these drownings were not an accident and anyone who had even seen a Nicholas Cage film would nod sagely in agreement. Now consider the chart below which looks at significant historical events and the rise of the Dow.

This rather imposing looking chart is the centrepiece of an article titled The Dow’s tumultuous 120-year history, in one chart which appears on the MarketWatch site.  The article boldly claims the following –

At its simplest, the chart proves once again that over the long term, the stock market always rises because “intelligence, creativity, and innovation always trump fear,” according to Kacher.

No it doesnt – this is mistaking causation and correlation. What the chart shows is the profound upward bias of the Dow and this is the driving force of the index moving higher. This is an example of survivor bias nothing more. The original Dow components were as follows

Dow

It is obvious that these components would change over time and that this change would drag the index higher as non performing or irrelevant issues were moved out. The notion that it is innovation that is moving the market higher is not true and can be illustrated by the simple fact that Apple arguably the most innovative technology company of recent times was only added to the index in 2015. Google whose technology permeates everyday life and Amazon who have revolutionized retailing are nowhere to be seen. The Dow has remained technology light since its inception – if technology and creativity were the drivers of the market then these new companies would be added to the index very quickly.

It is quite a simple matter to generate events stick them on a chart and say they have some significance but simply saying it doesn’t make it true. As I explored last week news and significant events tends to have a complex relationship with an index and the question of does news move markets has been answered in the negative.

The article then goes onto make the bold claim –

 Investing is more challenging than brain surgery,” Kacher told MarketWatch.

I will leave others to ponder the idiocy of this last quote.

Does News Move Markets….Sort Of…Maybe…Well No Actually

I was chatting the other day with someone who was having trouble with their trading system. Their approach was based on trading news events. Such a plan is predicated on the notion that news events move markets in certain ways and whilst this movement might not be wholly predictable it will at least generate some form of activity. Such a trading system has a single giant assumption – that news and news related events move price. If this maxim does not hold up then the system is a bust.

It has been sometime since I looked at this question and I had a vague recollection of research done in the 1980’s that looked at this question and found that news as a source of trading ideas was a bust. So armed with the dimmest of memories I went looking through my archive and found what I was looking for. David Cutler, James Poterba and Lawrence Summers produced a working paper titled What Moves Stock Prices for the Department of Economics at MIT in 1988. This paper looked at the 50 largest single day moves in the US market since World War Two – I have included the events from the original monograph below.

Event 3

If you take a cursory look at the events above you could argue that news events do move markets. However, there is a glitch in that some movements defy explanation – there is simply no event that can be seen as a casual trigger for a market move. Cutler et al stated that news events could really only be useful as an agent for movement in about half of all cases of the variance in stock price movement and in my world half is a fluke.

The interesting side issue with the work of Cutler et al is that it puts another hole in the Efficient Market Hypothesis because stock price movements according to the EMH reflect the assessment of investors to new information. If markets move without the the addition of new information to the system then something else is happening that is not explained by the EMH. And it seems in the case of broad brush analysis as performed by Cutler that prices move without any significant input.

This initial work has been expanded upon by Ray Fair at Yale University who looked at outsized movements in the S&P500 futures contract. This new work had much greater granularity to it in that it looked at five minute data, something that would have been difficult in the original work by Cutler and crew simply because the available technology would not have allowed it. Fair compared what he defined as big movements with news items emanating from the Dow Jones News Service, Associated Press and New York Times. The upshot of this investigation seemed to be that the majority of large events have no news based driver. They were only able to attribute a news item to 69 of the 1159 big moves that were examined. Recent  flash crashes seem to support this notion of significant market moves  occurring without a notional driver.

So we come back to the original assumption that news events drive markets and that these moves offer opportunities that can be traded. It would seem on the evidence available that this notion is false.

‘Earnings bonanza’ to fuel strong growth for Australian shares in 2017

I do enjoy it when people email me to ask me about something they have read in the media. My enjoyment comes from the simple fact that I dont read business publications. I find them irrelevant, stupid, depressing and generally lacking in original thought. However, in the spirit of being polite I did take a look at this article from the Fairfax trash pile. The basic contention is that corporate earnings will go through the roof and that this will drive share prices through the roof. Implicit within any such article are two very basic assumptions and these assumptions are the foundations upon which this argument sits. At the heart of the piece is the assumption that analysts are capable of making accurate predictions regarding the direction of earnings. Secondly, it is assumed  that perceptions of future earnings drive share prices.

With regard to the former, the forecasting track record of advisors is poor with a tendency to consistently overestimate earnings as shown by the image below. Analysts are persistently in the grip of optimism bias when it comes to forecasting and this adversely impacts their ability to make any form of accurate forward judgement. This lack of ability is also clouded by the hubris involved in thinking that you can make an accurate prediction about anything.

error

Source – Dr Ed Yardeni

The psychology behind this incompetence is reasonably easy to understand once you understand the nature of the finance industry. This is the dont bite the hand that feeds you syndrome. Within the finance industry very little money is now made by the sell or advisory side of the industry. The big money has always been in corporate advising, that is restructurings, capital raising and the like. It is here that the fees total in the tens of millions of dollars. As such you dont want to offend companies that you might do very lucrative work for by telling everyone that their business is crap and that the company is run by people who would struggle to run a Mr Whippy van. It is much better to tell everyone that that the sun shines out of their proverbial and that everyone will get a free unicorn in the morning.

The second assumption is whether or not earnings drive share prices and this to my way of thinking is a more interesting problem since it moves into the realm of investor perception. To get a sense of this I looked at the year on year changes in earnings for the S&P 500 and compared that to the yearly return for the S&P 500 and plotted these initially in the form of a simple bar chart to get a sense of any form of relationship.

earnings bar

However bar charts dont really give a true sense of relationships or correlations so I  plotted the data as a smoothed scattergram.

scattergram

So the question is what do the squiggly lines tell me. They tell me that sometimes earnings growth and share prices move together and sometimes they dont and if I do a bit of dodgy stats-fu on my trusty old Casio I find that that changes in earnings and share price growth have a very weak correlation of 0.37. The reverse intepretation is that most of the time they dont share a relationship. But there are some caveats in generating correlations. The correlation I generated is what is known as a Pearsons correlation, this looks at the linear interdependence of variables and it can be affected by outliers such as we see in the rebound from the GFC. I also wonder about true independence between variables over time – my concern comes from the fact that markets seem to have memory and this in turn loops back to the impact of data on investor perceptions over time.

The wonderful thing about being a trader is that our perceptions and our benchmarks are very simple and they revolve around the idea of whether something can help us to make money. In this instance neither the faulty predictions of analysts nor their profoundly weak impact upon price movements convinces me that that either idea lives up to their hype.

 

 

 

 

 

 

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