Image Source: Pexels

Both weather and the stock market are complex and chaotic systems. Complex, because they are made up of a near-infinite number of components, and chaotic because, even if we could identify all the components, we could never know how they interact with each other. Both weather and the market are emergent phenomena where the whole is greater than the sum of the parts.

Humans endeavor to predict these emergent systems, and it is fair to say that meteorologists are better able to predict weather now than they could back in 1940, but the same cannot be said about market predictions. We are no better, and perhaps worse, at predicting markets today than in the past. Why?

The more knowledge we have about the historical weather, the better and more accurate models we can build. Increased understanding of how the various components interact to create weather allows us to better predict the future. This is so obvious that it is almost trivial. However, when it comes to predicting the future of markets, a paradox arises; the more we know, the less we can predict.

As Yuval Harari points out in Homo Deus, “…as we accumulate more data and increase our computing power, events become wilder and more unexpected.” The reason for this paradox is the fact that the components of the market are conscious individuals who are themselves changed by the new knowledge.

Here is Harari once again:Imagine, for example, that one day experts decipher the basic laws of the economy. Once this happens, banks, governments, investors and costumers will begin to use this new knowledge to act in novel ways, and gain an edge over their competitors…..once people change the way they behave, the economic theories become obsolete. We may know how the economy functioned in the past — but we no longer understand how it functions in the present, not to mention the future.

That explains the old market adage, ‘if it is obvious, it is obviously wrong.’A recent example of this involves the prediction of inflation that was made by most analysts during the early years of quantitative easing (QE), post-2008. Loose monetary policies around the world had created trillions of dollars in an attempt to save the financial industry, so it made obvious sense to expect that inflation would appear alongside monetary expansion. More than a decade later, however, there was still no inflation to speak of. (Inflation appeared only after the pandemic shutdowns caused shortages, which are always the cause of inflation). The obvious was obviously wrong. Some things really are ‘different this time.’

In the case of weather, however, the causal components are not conscious individuals and are, therefore, not changed by the increased knowledge. That is the crucial difference. The more that is collectively known about the market — by the market — the more complex the market itself becomes. The more complex a system is, the less predictable it is. For this reason, we have always asked ourselves, ‘what is constant about the market and the sentient individuals who make up the market?’

The answer is two-fold: money and the human emotion of fear. Specifically, the creation of money resulting from the U.S. Treasury deficit-spending and chartered bank credit-creation, and the fear and greed (fear of missing out) that is part of the human condition. The market requires money to climb the proverbial ‘wall-of-fear’. That is why we analyse the U.S. Treasury Daily Statement and the aggregate bank credit levels, and why we track investor sentiment in a variety of ways.

Although no two markets are identical, markets do rhyme, reflecting the constancy of money and emotion in all markets. The ‘rhyming’ in the markets can be visualized in the repetitive patterns of the myriad of indicators and averages available to us. No pattern repeats with 100% fidelity, but the closer the pattern is related to money-flows and fear, the higher the probability that it will repeat.

The obvious question to be asking at this point is: What about the machine-learning algorithms that many market players are relying on these days? We know they have no consciousness and, therefore, no emotions. Because these emotionless machines are responsible for most of the trading in the market, should not the market become more “weather-like” and, therefore, more predictable?

As it turns out, the market continues to trade as emotionally as ever. We think the reason for this lies in the fact that the algorithms themselves are learning how to trade by studying historical trading patterns. These patterns, in turn, were created by emotional human traders, so it is reasonable to expect the machines to trade like their teachers — like emotional human traders — only many times faster.Fear and fund-flows continue to be the only constants we can find in an increasingly complex market — even one composed mostly of AI entities. Fear and greed, and money reign supreme — even among the machines.There are no fortune-tellers for the market, only analysts that calculate probabilities of the future. At ANG Traders, that is what we have been doing for 45-years.

More By This Author:

SPX Weakness Likely For The Next Week
Most Economic Indicators ('Screws') Remain Tight
A Summary Of The "Seven Innocent Frauds Of Economic Policy"