Mazars’ Chief Economist: what happens when Pavlov’s dogs realise there’s no food?

George Lagarias
George Lagarias, Chief Economist at Mazars.

George Lagarias(pictured), chief economist at Mazars, comments on the week ahead and market volatility: 

Outlook for the week ahead

Removing accommodation has had negative consequences across asset classes. Volatility levels remained elevated for the first two trading weeks of the year. Both bonds and equities have started 2022 in the negative, with the US 10yr actually underperforming the S&P 500 (-2.43% vs -2.11% respectively). For diversified asset allocators, only three years have started worse off for both equities and bonds since 1963 (1977,1982 and 2003).

This week will be crucial to see whether it becomes a trend, or whether a good earnings season kicking off (analysts are expecting 22% higher profits for the year to December 2021) will be enough to calm market nerves for the time being. Apart from this morning’s Chinese numbers (China reported 8.1% growth in 2021, albeit slowing in the last quarter), investors will focus on European and British inflation numbers, as well as UK retail sales.

Otherwise, the US will feature a 4-day week (Monday is M.L. King day) and probably a quiet Fed in the runup to their 25th January meeting.

What happens when dogs realise there’s no food?

In 1897, Ivan Petrovic Pavlov made a dog salivate without the presence of food. The Nobel Laureate (1904) arguably changed the twentieth century when he introduced behaviourism and the idea that human behaviour is, by and large, programmable. The notions of marketing, advertising and business development were based on the idea that client behaviour can be altered, to create a want for a particular product or service.

In the past twelve years, the Fed certainly paid homage to the idea. When Quantitative Easing was introduced to western markets in 2008-9 it was a novelty. Financial markets were reeling, and it took them time to get used to the idea. Even though traders and investors realised the potency of the Fed’s bazooka, they remained sceptical. The period from 2009 to 2015 was a long realisation for active fund managers that flows and communication can be more important than perceived fundamentals. They shed returns, underperformed indices and saw the rise of passive investing before yielding to the Fed’s awesome power and accepting that a central bank-directed bull market is as profitable as a natural one. They became acolytes in the new ‘Don’t fight the Fed’ regime. The Fed just had to remind investors on occasion that it can print money at will, to keep everyone in line.

The more pressing the conditioning, the less accepting is the subject to change, however. Inflation at 7% simply precludes the Fed from printing more money and rushes the timetable of ending quantitative easing. Talks of quantitative tightening immediately after only exacerbate the situation.

But what about those conditioned investors? We are not just talking about humans, but more importantly, about the algos. Humans, unlike dogs, are rational. They might adapt, reduce risk, wait for a stock and bond re-rating, look for opportunities and missed sectors etc. But algorithms, which account for over 80% of all trading, have never worked outside a QE regime. Their programmers, young engineers, haven’t experienced work before the 2008 Global Financial Crisis. Most of the time, in fact, they search for correlations without much regard to causation. Algorithms don’t like to wait much. In March 2020 they did not just give us one of the steepest drops in history, but also one of the quickest recoveries. If investments are a long path, say 100 miles, human investing is like walking it. Slowly, but surely, one will get to the desired destination. Algorithmic investing is like using a Ferrari. The driver may arrive at the end in less than 30 minutes, but there’s also a decent chance they might not arrive at all. Algorithms have never experienced a paradigm shift. They are the children of the current paradigm. Currently, much like Pavlov’s dogs, they continue to wait for food (QE) to arrive. But what happens when they realise there’s no more of their favourite treat?

Oddly enough, to find an answer, we may have to look back to 1996, when the world Chess champion played Deep Blue, a very advanced computer. By the last two games, Kasparov understood that Deep Blue knew all the games ever played, and responded with the best possible moves to his, much like Machine Learning does today. So he changed the paradigm. He made a series of unprecedented moves. The computer had no historical analogy and it was thrown off-balance. Deep Blue surrendered easily.

So how will a paradigm shift affect algos? ‘Only when the tide goes out do you discover who’s been swimming naked’ Warren Buffet exclaimed. ‘Smart’ algos may look for opportunity. Whether they will discover real value or not, time will tell. ‘Lazy’ algos, those based on spurious correlations, will probably not know how to react. Along with a younger generation of human investors and well-conditioned seasoned managers, they will find themselves in uncharted waters. At the very least, volatility will ensue. 2022 has seen one of its most volatile starts to the year across risk assets, and this could very well be the trend, at least until the inflation dust settles. That is not necessarily bad news.  Volatility can be an enemy of passive investing, but it can be the bread and butter for good active, well-rounded investors who have stayed in the sidelines for years. In other words, at the very least, a paradigm shift could mean a change in the types of managers that outperform.

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