Category Archives: data analysis

Coffee as the thin edge

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Pick n Pay is starting to gain some useful insights into customer behaviour and purchasing decisions at different stores. They’re using coffee as a key product to better understand who buys what, where and when.  They’re tossing out (more likely de-emphaszing) LSMs as a method of categorising customers and moving to more sophisticated measures (including whether the purchaser has children or not, but also I’d expect location, purchase frequency, average basket size, mix of goods etc.)

Pick n Pay had to spend a fortune on the Smart Shopper system and has ongoing expenses in terms of rewards and analysis. The curious thing for me is how many loyalty cards incur the system and reward costs for retailers, but without gaining the full benefit of analysis and thus insight into customers.

I don’t get tailored book suggestions from Exclusive Books. They also haven’t tried to entice me back to their stores since I started buying first from Bookfinder.com and then almost exclusively ebooks from Amazon. They’ve basically lost a customer and haven’t done anything about it.

Even my friend’s St Elmos offers sweet deals to customers who haven’t ordered in a while to entice them back. Pick n Pay turned sub R100 pm customers into R350 pm customers (at least while the special was one) by specifically targeting customers that are familiar with Pick n Pay but need a push to become regular, high-spending customers.

I haven’t had a movie card with Ster Kinekor in a while, but I always use the same email address and credit when I purchase tickets online (which I do almost universally). There have been periods of several months where I haven’t gone to the movies, but no attempt from Ster Kinekor to woo me back with free popcorn or a careful movie recommendation.

Retailers are missing a trick to get an edge over their competitors.

 

Lose a Million

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The Make a Million competition, as I’ve mentioned before, is an awful idea. It doesn’t promote investing or even “normal” trading, but rather massive, speculative risk-taking trading because the prize for performing well is nothing and the prize for performing best is significant.

I’m continually disappointed that Moneyweb continues to partner with this distraction.

As I’ve done in the past, I’ve analysed very quickly some of the results of the most recent competition. As background to that, the basic rules are:

  1. Put up R20,000 of your own money
  2. Trade over three months in currencies, commodities single stock futures and some index trackers.
  3. Whoever has the most at the end wins a million rand
  4. Everyone keeps what is left of their initial “investment”

So let’s be clear, there are no long-term investment learnings here.

The winner did return 165.5% over 3 months, which is not an impressive performance even though it might look like it.  The point is, given the volatility of the investment universe available for the competition and the encouragement towards rampant risk-taking, it’s entirely pedestrian performance.  It’s very likely an individual’s performance will be good given the wide range of possible outcomes.

Let’s look at some other statistics

Average performance -18.4%
Annualised average performance -73.4%
Proportion making a profit 26%
Total amount won -R1 020 762
Standard Deviation of performance 48.0%
Annualised standard deviation 96%

These are not performance statistics of which to be proud. They are similar to the losses incurred in prior competitions.

So in short, the competition cost the entrants in total just over a million rand. Losing a million rand is a great way to Make a Million.

How not to calibrate a model

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Any model is a simplification of reality. If it isn’t, then it isn’t a model as rather is the reality.

A MODEL ISN’T REALITY

Any simplified model I can imagine will also therefore not match reality exactly. The closer the model gets to the real world in more scenarios, the better it is.

Not all model parameters are created equal

Part of the approach to getting a model to match reality as closely as possible is calibration. Models will typically have a range of parameters. Some will be well-established and can be set confidently without much debate. Others will have a range of reasonable or possible values based on empirical research or theory. Yet others will be relatively arbitrary or unobservable.

We don’t have to guess these values, even for the unobservable parameters. Through the process of calibration, the outputs of our model can be matched as closely as possible to actual historical values by changing the input parameters. The more certain we are of the parameters a priori the less we vary the parameters to calibrate the model. The parameters with most uncertainty are free to move as much as possible to fit the desired outputs.

During this process, the more structure or relationships that can be specified the better. The danger is that with relatively few data points (typically) and relatively many parameters (again typically) there will be multiple parameter sets that fit the data with possibly only very limited difference in “goodness of fit” for the results. The more information we add to the calibration process (additional raw data, more narrowly constrained parameters based on other research, tighter relationships between parameters) the more likely we are to derive a useful, sensible model that not only fits out calibration data well but also will be useful for predictions of the future or different decisions.

How not to calibrate a model

Scientific American has a naive article outlining “why economic models are always wrong”. I have two major problems with the story: Continue reading →

Why S&P downgraded

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I don’t think many serious investors care that S&P downgraded US debt. Bond yields are down (more on this in my next post), which means prices are up. US stocks are down, but that’s more about concerns about US and global economic prospects than the credit of the US government.

Nevertheless, S&P did downgrade. Why? I don’t think it is primarily to do with a materially increased estimated probability of default. It has more to do with a change in the payoffs in a ‘game’ (as in game theory) S&P is playíng.

Consider the quadrant of options. S&P downgrades or doesn’t and the US defaults or doesn’t. I’ve constructed totally hypothetically, but perhaps plausible scenarios below, for the S&P’s potential assessment of losses under each possibility given their views and external perceptions of them before and after 2008.

Before 2008, the fallout that would come from downgrading the US and the US not defaulting would be significant and cries of “un-American” might be heard again. Even if the US were downgraded, default would still be a blow for S&P since anything above a BBB rating really shouldn’t ever default if there models are “correct”. I’ve thrown in another hypothetical, a 0.01% probability of default – in other words very low, and as you’ll see in the next scenario, not necessarily higher now for S&P to change their view.

Now, either on a traditional minimax (minimizing the maximum cost) or an expected value basis, before 20008 S&P wouldn’t downgrade the US. This is an important calibration, since S&P didn’t downgrade the US.

After 2008, even if we leave the assessed probability of default unchanged, the world is different and therefore we have different costs.  If S&P doesn’t downgrade the US – even if the US doesn’t default, there will be a cost to S&P since might share the view that the US could default now. The dent in credibility since 2008 means that S&P has to try harder to convince the skeptics that they don’t rate risky instruments as AAA. Along with this goes a massive hit if the US does default and S&P hasn’t downgraded the US. The good news is that at least now a downgrade is viewed more with more understanding even if the US doesn’t default (although be sure Obama’s White House is not happy at the moment).

After 2008, even if the assessed probability of default is unchanged, the minimax and expected value rules both suggested a downgrade is the better option for S&P.

Before 2008

 Don’t downgrade

 Downgrade

 PD

0.0001
Default

-500.0

-50.0
No Default

0.0

-1,000.0
Expected

-0.1

-999.9
After 2008

 Don’t downgrade

 Downgrade

 PD

0.0001
Default

-10,000.0

-50.0
No Default

-10.0

-10.0
Expected

-11.0

-10.0

Now the example is contrived – I chose a set of parameters that demonstrates the point I’m trying to make. This isn’t a problem since I’m not saying this is what happened. I‘m saying it is plausible that S&P made a perfectly rationale (for them) decision to downgrade even if they didn’t think the US was more likely to default now than before.

In truth, the US might be more likely to default now than before, although the change is probability not sufficient on its own to merit a downgrade at this point. Especially since S&P have their maths wrong.


Just so we’re clear on the problem

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From the Bureau of Labour Statistics

Series Id:           LNS11300000
Seasonally Adjusted
Series title:        (Seas) Labor Force Participation Rate
Labor force status:  Civilian labor force participation rate
Type of data:        Percent or rate
Age:                 16 years and over
Civilian Labour Force Participation Rate

US Civilian Labour Force Participation Rate

The above graph is possibly the most important number.  How much economic activity are we losing because US citizens simply aren’t working? The unemployment rate is depressed because those discouraged workers who give up searching for work do not count as part of the labour force, thus not factoring into unemployment rate calculations.

The downtick in the unemployment rate looks to be in danger of being reversed, in spite of this “discouraged worker” bias. Continue reading →

Egypt: Indonesian or South African parallel?

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Paul Krugman compares Egypt’s economic performance prior to its political crisis to that of Indonesia and the Philippines.

He makes a compelling point that the comparison fails.

Egypt’s economic performance has been stable and much better than, say, South Africa’s for example.  Krugman suggests:

Egypt had decent growth — but the gains weren’t trickling down, and youth unemployment was and is a huge problem.

Which sounds rather more like South Africa.  Except with recent data showing unemployment at 9.7% (massively lower than South Africa’s 23.7%) and economic growth above 5% (higher than 3%) and income inequality measured by the Gini Coefficient of 34.4 (compared to South Africa’s 65, which is just about the highest in the world, as in most unequal) Figures from CIA World Factbook

Real GDP growth Unemployment Gini Coefficient
Egypt 5.3% 9.3% 34.4
South Africa 3.0% 27.3% 65.0

So if you were to back Egypt vs South Africa as most likely to experience violent political upheaval?

Are our inflation figures fudged?

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The economist magazine compares Big Mac inflation to officially reported country inflation over the last ten years. The aim of their article is to suggest that perhaps China and Argentina have been fudging their inflation figures. However, my attention was drawn to South Africa right near the top of the chart.

This suggests that our official CPI inflation has been understated by approximately 2% over the last ten years. From conversations I’ve had there would appear to be plenty of popular support for this notion.

I’m still not convinced.

The calculation of inflation figures has been closely  watched by economists and asset managers trying to understand what the future holds for monetary policy, earnings growth and returns on inflation-linked bonds. While there have been errors in the calculation, the experts in this area have not been the ones criticising the overall methodology or results (apart from these specific errors).

So why does this 2% differential exist? I have some ideas:

  • The components of a Big Mac are not representative of the entire economy. Perhaps (and I haven’t checked) food price and wage inflation (two components I’d guess at being significant inputs into a Big Mag) may have been above average CPI.
  • Our currency has had wild swings over the last ten years, dramatically affecting the cost of imported goods and services. This won’t be reflected in the locally made from mostly locally produced ingredients Big Mac.
  • Consumer electronics, computers, office equipment etc. have all benefitted from cost reductions over this period.
  • The differentials for several other countries are significantly wider, suggesting a high variance between official inflation and Big Mac inflation. In other words, a 2% differential may not be significantly different from a 0% differential.

Argentina, on the other hand, with a 9% differential, is another story.

More on Marriage Data

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Divorce is becoming less popular, the world didn’t end at the end of the millennium and StatsSA makes it hard for us to draw conclusions.

Census data isn’t the most useful for understanding marriage and divorce patterns and trends. It’s available too infrequently, relies on self-reported status and only shows a snapshot of the population at a point in time without explicitly showing the change from one state to another.

The great thing about the census data is most of it is freely availably from StatsSA so you can slice and dice it and analyse it exactly how you want.

StatsSA also puts out a Marriages and divorces 2008, with the info taken directly from registrations. I don’t have access to the underlying data, but here are some snippets that can be useful to compare and contrast with popular notions in the media.

Month of Marriage

December is the most popular month to be married

It’s a huge pity StatsSA doesn’t release the raw data, as their analysis is sadly lacking in the focus on reporting absolute numbers with comparing this against a relevant denominator in a ratio. Knowing how many divorces there are for someone who has been married twice before isn’t very helpful unless we understand how many marriages of that type there are in the first place. Knowing how many divorces involved children doesn’t help really unless we know how many marriages at that point in time were in a family with children. This tells us nothing about the relative likelihood of divorcing with or without kids.

I’ve tried to piece together some of this by comparing this transition data with the snapshot data from the census, but I have concerns that inconsistencies between the data sets may be skewing the results enough to limit their usefulness. Continue reading →