Non Stationarity, False Certainty and China

1 min
January 18, 2021
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In an increasingly uncertain world, people crave reassurance. Unfortunately those appearing to offer certainty and ‘science’ are in many cases fooling themselves along with their customers. Covid has revealed many problems with the systems that we use, not least the problem presented to forecasters by the uncertainty generated by the rather dry sounding issue of Non Stationarity of Variables, or, to put it in Mark Twain language, the fact that history does not repeat itself – although it does rhyme.

Most models of the Economy, the Climate and the Pandemic are fatally flawed as predictors of anything

Unfortunately, just when we thought that we needed them the most, models of the economy, the Climate and the Pandemic have all proven to be ‘almost worst than useless’ – although the latter two are fast becoming a protected species immune from criticism – but that is not entirely their fault, for they were designed to help us understand the issue rather than necessarily to predict the future – even if that is what many of those selling them were claiming they were able to do. It is our own wish to see patterns and for history to repeat itself that leads us to believe that ‘the data’ and ‘the models’ can tell us what to do next. Embedded in almost all forecasting models (including those used by epidemiologists) is the assumption that the mean and the variance of a time series of data are unchanged over time, or to put it in the jargon that they ‘display stationarity’. However, a moments’ reflection can usually lead us to recognise that in most real-world examples, this is actually unlikely to be so. For just one example, as one of the more renowned econometricians of our era David Hendry points out in this paper, while in the 1860s the average age of death in the UK was 45, a number of people nevertheless lived to twice that age. Today, with an average age of death at 82 (ironically lower than the average age of Covid deaths) nobody lives to twice the average – both the mean and the variance are non-stationary.

One result then of assuming stationarity (largely in order to make the models work in the first place) is that they can provide a false degree of ‘certainty’ as to the predictions and the problem comes when policy makers take this false certainty as a basis of ‘science’. The same occurs with Capital Markets and while there are many aspects of behaviour that we consider are broadly stationary as part of our Market Thinking (including the need to believe in the certainty of models!), the benchmarks we use and the observable universe most certainly are very much not stationary and this is most obvious when we factor in the massive disruption over the last 30 years from China.

Consider, for example, how last week we discussed that a weaker US$ has historically been correlated with stronger performance by emerging markets and cited it as one reason (among several) for diversifying away from the US Equity market. Certainly the economics appears to support why a correlation with the US$ should exist, after all a lot of traditional emerging markets in Latin America, South America and South East Asia had ‘benefited’ from the Washington Consensus development model such that they had imported US monetary policy along with their huge $ debt burdens. Moreover, a lot of the industries previously heavily represented in the EM index such as Financials, Energy, Telecoms and Materials either had a lot of debt, or were otherwise interest rate sensitive.

Market Benchmarks can change rapidly over time, probably none more so than the Emerging Markets Index.

However, while we would agree with what now increasingly appears to be a consensus position on EM (remember we said that Q1 is when asset allocators discuss the positions they will take in Q2) we would add that the historic relationships are now heavily distorted by China and that an EM allocation is increasingly a view on North Asia, not the traditional EM countries. For example, if we look at table 1, we can see how the relative weighting of the MSCI Emerging Market benchmark has changed over the period since the Global Financial Crisis (GFC) in 2008.

Table 1: Importance of different Countries in MSCI Emerging Markets Index over time.

Source MSCI, Bloomberg, Market Thinking

Thus if we start from the (previously) fashionable notion of the BRICS (Brazil, Russia, India, China and South Africa) we can see that in 2008 they represented around half of the Emerging Market Benchmark. In fact when the term was invented by Goldman Sachs these were seen as the fast growing economies of the future. Oops. In fact, as the second column shows, ex China, this group has shrunk by almost half in terms of index weight, while China itself has almost tripled. Indeed, as far as the economic ‘logic’ associated with US $ debt goes, the three countries making up almost two thirds of the benchmark are now all in North Asia and US$ debt is barely a feature.

In a similar fashion, the sector breakdown is also now very different; ex IT, the major sectors of Financials, Energy, Materials and Telecoms, have shrunk from almost 2/3rds to little more than a quarter of the benchmark, while IT has tripled to almost a third of the index. Given their relative levels of debt as well as their economic sensitivity, it would be perhaps unwise to impose too much certainty on how the past can predict the future behavior of the Index.

Table 2: Sector Breakdowns also non-Stationary

Source MSCI, Bloomberg, Market Thinking

This is not to say that in the short term the index can not move in line with previous correlations for the simple reason that if everyone is speculating/hedging/investing according to that same historic model, then it can become self fulfilling. For a while.

The importance of The China Price

The reality is, therefore, that while Emerging Markets and the EEM US ETF may well currently be trading on the idea of a correlation with the US $, a far more important trend is that of China and the notion of “The China price”. This emerged over the last 30 years as China moved from largely backward, agricultural economy to the world’s largest Industrial manufacturer. Essentially, everything China started making went down in price, while everything China started buying went up in price. As such, emerging economies and their capital markets boomed for a decade as China imported large amounts of commodities, before being usurped by Western Economies and markets selling higher value added goods and services into China while simultaneously utilising the Chinese manufacturing base to cut their own production costs through offshoring. The biggest suppliers of materials to China were the likes of Brazilian miner Vale or Australia’s BHP and RIO – the latter two of whom, being quoted in the UK, helped the UK index to perform well at the same time as emerging markets. Another spurious correlation. While the miners will continue to do well – especially in areas like copper – they do not have the pricing power they did in 2009/10. Similarly with the Oil companies where supply really has been brought on in response to earlier higher prices, share prices are reflecting a recovery from Covid rather than a period of super-normal profits.

For longer term investors, integration with the China supply chain despite the best efforts of the US, or selling value added things to the China consumer market remain the key drivers to growth in the region. Investors may therefore want to get more nuanced ways to play future growth, but for those simply looking to diversify out of the US, the EM benchmark may still be the easiest first step.

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