Let someone smarter than you do the work. 2/n

A couple of years ago now, I almost bought a small business as an escape plan out of my day to day. I still think about it fairly often and there is a robust twitter community around the idea of small business acquisition. I have no idea if I’d be any good at it but the idea of not being marked against an index and being my own boss appealed (still does, actually).

Mostly it appeals because almost all businesses below a certain valuation threshold change hands for 4 x EBIT, and usually less. Which is cheap. But of course then you have to run it, you are dealing with weird problems you might not have any real interest in, and its more than full-time (small businesses do not clock off in my experience).

I almost ended up funding a searcher - essentially providing capital to someone who was looking for this type of business to run (they pulled out at the last minute). The returns would have been slightly less - I’m guessing mid-teens, and required nothing from me other than capital. But the big leap of faith was giving cash to someone with no track record and no demonstrable history of being able to do the thing that they said they would do.

It probably stands to reason that there would be people who consistently make acquisitions from sellers (sometimes motivated, sometimes not) that manage to consistently deliver attractive returns. And it turns out that there are a few, in various different industries, all over the world (although concentrated in nordic countries, for some reason).

Would I like to hire these people to invest my cash in new businesses that deliver attractive returns? Turns out that I would like that very much.

Invest with the best: 1/n

This is likely the first of a few, explaining how my investment style has changed/is changing over time.

The more time I spend investing, the more I understand that it in the general scheme of things, I am not that bright. I work daily with people who are outstanding in their niche - whether on the buy side or the sell-side. Increasingly I am convinced that being brilliant is not a prerequisite for achieving excess returns in markets.

One of the things I increasingly believe is that who you invest with, rather than what you invest in, is the driver of sustainable excess returns , particularly if you control for business quality. I think there are some business models that will always deliver sub-par returns - an exceptional manager in that industry will always be pushing the proverbial up hill. I also think there are some industries that should deliver strong profits that are plagued by a succession of “professional managers” who consistently become CEOs for five years in order to maximise their own personal wealth.

None of this is new - Buffett has written about it, so anyone likely reading this is already aware of the importance of management. But like everything he writes, even though you might think you understand, typically something needs to occur that will crystallise the understanding for you.

For me, a recent incident that highlights the concept is Ian Narev essentially becoming CEO of Seek, while Bassat becomes a full time investor and Executive Chairman. While Andrew Bassat was in charge of that company, I had faith that the investment would turn out well. With Narev in charge, I found myself triple checking my (largely useless) financial model. Is Ian Narev a bad manager? Probably not. Do I have faith that he will take care of Seek as well as the Bassat’s did? Absolutely not.

I don’t really comment on Australian markets because its a little to close to home with the day job (its also why I have previously posted mostly graphs and statistics work). Most of my personal investing is done internationally for compliance reasons.

I’ve done better internationally than I have domestically, mostly because I focussed on finding businesses that were run by owner operators or had a significant commitment to a culture that had historically driven excellent returns.

The next posts will look at the what and how.

Credit to GDP Gaps

Quick one today on credit to GDP gaps. Data is sourced from the Bank for International Settlements and is produced in order to inform central banking  of the build up of economy wide risk and inform the requirements for counter cyclical capital buffers.

The credit-to-GDP gap is defined as the difference between the credit-to-GDP ratio and its long-run trend. The credit-to-GDP ratio as published in the BIS database of total credit to the private non-financial sector, capturing total borrowing from all domestic and foreign sources, is used as input data.

Credit_to_GDP.png

Of interest are the large and sychronised increases in this measure in 2007. This has not been matched recently.

On this measure at least, system wide risks are lower than previous.

RBA Sentiment Analysis, 2011-2017

I stumbled across an interesting book the other day called Tidy Text Mining. It was nice to find something in R that looks intuitive to me.

I've always wondered whether RBA minutes are indicative of future movements in interest rate prices. There is certainly enough time spent attempting to decode RBA releases to make something like this worthwhile. Maybe some of those poor analysts could free up a bit more of their time?

Anyway - I wrote up something simple that ingested the minutes beginning 2011 and finishing 2017 and charted the sentiment of the individual releases over time.

 

CombinedChart.png

The above shows the trends of counts of positive words, negative words and (Positive - Negative) over the corpus.

Interestingly, the RBA keeps with its dour reputation by always being more negative than positive (except for one lonely month!).

 

PositiveLessNegative.png

As always, I'd love to hear any comments about this analysis - and the code can be found on my github.

Regime Detection in financial time series.

Hidden Markov models analyse changes in data series in order to identify regime shifts. The purpose of the technique is therefore to monitor for changes in regimes in order to identify the difference between potential downturns or short term blips.

Given some large changes in housing markets recently, I thought it would be interesting to have a look at changes in the house price series for Sydney. House prices are also an interesting time series in this case since momentum is relatively persistent in house prices. I used CoreLogic Data, and as per usual, the code is available on github.

RegimeDetection1.png

 

While useful as a proof of concept, one thing that makes the results slightly more robust is calculating rolling year returns and using that as a time series. Unfortunately, using daily changes in prices means that downturns are very often described as regime changes - using longer periods allows you to identify more interesting turning points.

Certainly, if anyone has an accurate and long term house price data series, I would love to take a look at it!

Do you really want to invest in an industry likes this?

Myer delivered another downgrade this week, which should come as no surprise to almost anybody.

Here's a simple chart of department store sales over time.

Department_Store_sales.png

 

Specifically - department store value proposition was based on a collection of brands attractively retailed. I've been of the belief for years that this has been superseded by e-commerce. Retailers have defended their networks with experiential shopping - aiming for theatre to draw foot traffic. Myer in particular has been attempting to focus on theatre while also cutting christmas hours for staff.

If anyone can point me to a retailing situation that was improved by cutting staff, please let me know.

Investing is hard enough - at least give yourself a chance with the industry!

 

The Bitcoin Post

A brief post for my first.

Very few people would argue that bitcoin is currently not in a bubble. A simple chart with a logarithmic y-axis highlights the explosive rate of return. A very simple way to find potential bubbles are assets that are increasing at an increasing rate, as BTC currently is. 

LogBTC.png

Perhaps a more interesting way of testing for bubbles is testing for a unit root in a time series. The Augmented Dickey-Fuller (adf) Test, when applied on a rolling basis, has a reasonable success rate in identifying the formation of asset bubbles.

When the p values are high, there is strong evidence that the data is non-stationary - that is, the time series is exhibiting bubble like behaviour. Below, I highlight the current p values on both a 1 year and 3 year rolling basis.

 

ADFTest1YearRolling.png
ADFTest3YearRolling.png

Neither looks particularly promising.