So, you think some people can predict the stock market. That is fantasyland. Maybe it is predictable in the long term but only based on conditions remaining as they are. The stock market, the economy, and the weather are complex systems that do not behave as we think. Perhaps intuition is a more helpful term. I’m not sure thinking is actually involved.
Stock markets are not short-term “deterministic.” What has happened influences the short run, but it does not define it. Results rhyme. There is a signal but also variability. I have seen it presented a taking a dog for a walk. You follow a known path while the dog wanders all about it. Your path has some predicting value; the dog’s short-term path does not.
A cartesian system is one that uses an X and Y axis to create a plane where a known function can generate particular locations for a family of points. If you can describe the rule, you know where every point will appear.
Complex systems are ones where there are “attractors” but no clearly defined rules that apply always and everywhere. They have variables that interact with each other, but not in a precisely rule-bound way.
What we take to be a sharp-edged factor may be a bit variable, and the variance affects how the result plays out. For example, your money manager makes decisions about what stocks to own. Today his child is seriously ill. Will he make precisely the same decisions as he would have made a month ago?
Ed Lorenz was a meteorologist and professor at MIT. He is one of the founders of what we know as Chaos Theory. What he learned about predicting weather applies in principle to any complex system. He found that predictability had limits.
“Two states differing by imperceptible amounts may eventually evolve into two considerably different states. If there is any error whatever in observing the present state—and in any real system such errors seem inevitable—an acceptable prediction of an instantaneous state in the distant future may well be impossible.
Because of the inevitable inaccuracy and incompleteness of weather observations, precise very-long-range forecasting would seem to be nonexistent.” Edward Lorenz
Tiny differences in observations of the present can end in enormous differences in outcome. Lorenz discovered this when he created a prediction model that seemed viable. He built a second version that started in the middle of the first one and found the model created an immensely different end.
He found that although the models were identical in how they created the result, the variables used in the original model had been evolved by the program, while the ones in the second version were inputted. The second model used 3-decimal accuracy, while the original had created 6-decimal accuracy by then. The result was an utterly different outcome. The 4th decimal is the 10,000th place, and the 6th is the one-millionth place. Tiny variations in the input have a massive effect.
We don’t know what is happening in the present with that degree of accuracy, so future results are beyond our ability to predict.
As Lorenz found, tiny input variations make for vast differences in outcome. The stock market is complete because there are so many people involved, and each of us is a complex system in our own right.
British statistician George Box makes a valuable observation.
“Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful.”
My answer to that question is they are helpful as long as they provide insight into the problem. Providing solutions is not one of the things models can do. Models help you understand how the variables interact with each other.
They are, nonetheless, unwelcome. We love predictability. Benoit Mandelbrot was a mathematician who studied chaotic effects. His summary is worth noticing,
“…clearly, the global economy is an unfathomably complicated machine. To all the complexity of the physical world of weather, crops, ores, and factories, you add the psychological complexity of men acting on their fleeting expectations of what may or may not happen-sheer phantasms. Companies and stock prices, trade flows and currency rates, crop yields and commodity futures-all are inter-related to one degree or another, in ways we have barely begun to understand.”
We cannot predict because we do not know the beginning conditions with sufficient precision. I suppose the processes themselves are not known in much detail either, but that’s even harder to assess.
What we can do, is understand the general direction and adjust as new interactions appear. Recall the walking a young dog idea. Your path in the short run is nearly predictable, but the dog’s path is not. How much of your portfolio would you want to invest in knowing where exactly he would be 5-seconds from now?
But in the longer run, you both end up at home. His wandering had no meaning.
If you own stock in sound businesses, the best advice for your trading activity seems to be Morgan Housel’s admonition, “Shut up and wait.” Waiting is the hardest thing. Patience.
Own stock in sound businesses.
The market variability is just noise. There is little information in the noise.
Unfortunately, market price variability has been called risk. Risk is unnerving. Pay less attention to variability unless you need the money in the short term. In that case, you should not be in the market at all.
I build strategic, fact-based estate and income plans. The plans identify alternate ways to achieve spending and estate distribution goals. In the past, I have been a planner with a large insurance, employee benefits, and investment agency, a partner in a large international public accounting firm, CEO of a software start-up, a partner in an energy management system importer, and briefly in the restaurant business. I have appeared on more than 100 television shows on financial planning. I have presented to organizations as varied as the Canadian Bar Association, The Ontario Institute of Chartered Accountants, The Ontario Ministry of Agriculture and Food, and Banks – from CIBC to the Business Development Bank.
Be in touch at 705-927-4770 or by email at firstname.lastname@example.org.