This morning, the Japanese yen has weakened to 160 against the dollar. This is the weakest level for the Japanese currency since 1990. Depending on how close or far one is from the screen, the movements in the yen might seem dramatic. However, the truth is that in real terms—considering relative purchasing power parity (PPP)—the yen has been consistently weakening over the past three decades.

Long-term movements in the real exchange rate, similar to those in the ‘natural’ real interest rate, are influenced by the underlying economic growth of a nation. Here, demographics often play a pivotal role. In Japan, the population of working age has been declining for almost exactly 30 years, in stark contrast to the rapid growth of the Japanese workforce in the 1970s and 80s.

As the graph below shows, there is a fairly close correlation between workforce development and changes in the real yen rate. Additionally, it’s important to note that the Bank of Japan (BoJ) allowed deflation to take hold for many years but has recently shifted its policy significantly, now targeting a 2% inflation rate—mirroring the Fed and the ECB. When adjustments in the real exchange rate cannot be achieved through differences in inflation rates (as happens when Japan has deflation while the rest of the world has moderate inflation), then the nominal exchange rate must adjust.

Therefore, there is nothing particularly surprising or alarming about the weakening of the Japanese yen. It is primarily a reflection of two more permanent conditions—negative demographics and a higher inflation target than before.

Furthermore, there is currently speculation in the market about a potential devaluation of the Chinese yuan, which is essentially more of the same scenario. China is like Japan but with a 20-25 year delay—and in a worse position. The Chinese demographic outlook is also extremely negative, and the workforce has been shrinking for over 15 years—a trend set to continue. This suggests a continued real weakening of the Chinese yuan. If the People’s Bank of China (PBoC) does not allow the yuan to weaken, China will face the same deflationary pressures as Japan, with similar debt and housing market issues.

Consequently, it is natural to expect that the yuan will weaken over time, both nominally and in real terms.

The 1990s and early 2000s witnessed a remarkable technological transformation in financial markets, ultimately leading to significant advancements in transparency and efficiency on a global scale. This era saw a shift towards electronic trading, with terms like “Quants” and “Algo trading” becoming increasingly prevalent. Today, it’s estimated that over 90% of global currency trading is executed by algorithms in some form or another.

Large financial institutions began hiring quantitative analysts, commonly with backgrounds in mathematics, computer science, or even physics. The purpose of these “quants” was and still is to develop systems capable of rapidly pricing financial assets, such as options. Alongside quants, algorithms emerged, enabling automatic or semi-automatic trading activities, such as buying or selling currency.

The result has been a significant reduction in market mispricings, making it increasingly challenging to exploit opportunities for buying low and selling high, as markets tend to already be efficient. This development has been facilitated by two key factors.

Firstly, the widespread availability of the internet means that pricing information for most financial assets is instantly accessible to anyone with an internet connection.

Secondly, the general globalization of capital movements has led to a significant convergence in prices across countries. With more liquid financial markets, even “small” financial markets are swiftly moving towards what are known as efficient markets, where opportunities for profit are scarce.

Similar trends are now increasingly evident in other markets, for example in the Danish retail sector. Danish supermarket chains report that pricing for common items like milk is nearly impossible to differentiate significantly from competitors. Price tests conducted by for example the Danish news site BT often reveal minimal differences between the prices of competing products.

Apps like “beepr” allow consumers in Denmark to track the prices of a wide range of groceries in real-time, prompting supermarkets to closely monitor their competitors’ pricing strategies. While price disparities still exist between supermarkets, there is a clear trend towards reducing these differences. Both consumers and supermarkets can now track price differentials at minimal costs, reflecting a process known as commoditization.

Commoditization refers to the standardization of goods across various sectors, resembling the pricing mechanisms observed in commodities or currencies. However, these changes also present challenges for sectors currently undergoing transformation. The same developments that accelerated in financial markets during the early 2000s are now unfolding in sectors like retail. It’s undeniable that Danish supermarket chains, in the coming years, will increasingly need to think and act like “quants.” It wouldn’t be surprising to see algorithmic pricing strategies emerge in the Danish retail sector, perhaps even for everyday items like milk.

Furthermore, this evolution is poised to be further propelled by artificial intelligence (AI). AI fundamentally functions as prediction engines, and with the cost of making predictions significantly reduced, we’ll witness a widespread adoption of AI in supermarkets. This adoption won’t be limited to logistics but will extend to navigating a grocery market increasingly resembling the foreign exchange market.

Ultimately, the primary beneficiaries of these developments will be consumers. As market standardization and algorithmic pricing become more prevalent, increased competition will drive prices down and lead to better-quality products. Hence, it’s high time for the traditional “merchant” to embrace the mindset of a quant and perhaps consider hiring mathematicians or physicists to implement algorithmic trading strategies, as this is how prices in supermarkets will be determined in the future.

* This article was first published in Danish on my Linkedin profile. See here.