Analogies of a Stock Market in a Highly Noisy World
What are we looking at when we look at these graphs?
We’re both looking at a picture of numbers.
While we both look at the same picture, we won’t see the same things. You, for instance, might try to order them. I might try to pick the ones that are divisible by a smaller natural number.
We will have different ideas about organizing the noise.
When it comes to stock markets, it’s not important to us whether it’s efficient or not. It’s important whether you can rule out if it is perfectly efficient. I think most of us can agree that efficiency in the markets is a sliding scale, given that equities markets crash and skyrocket without real rhyme or reason.
Equities crashing or skyrocketing is also not important to us. What is important to us is that we see the reasons, in that picture, for those equities to sway a particular direction. It won’t need to be insane dips or jumps in valuation.
In November of 2000, Jim Simons said this:
“We search through historical data looking for anomalous patterns that we would not expect to occur at random. Our scheme is to analyze data and markets to test for statistical significance and consistency over time. Once we find one, we test it for statistical significance and consistency over time. After we determine its validity, we ask, ‘Does this correspond to some aspect of behavior that seems reasonable?’”
Simons founded the most successful hedge fund in history, returning approximately 70% each year in gains in his Medallion Fund.
When it comes to the stock market, it’s not that you’re looking at the same picture as everybody else, like you and I might be above. In the stock market, the picture is so gargantuan that it might be impossible for you to even see 5% of the picture, if that.
That’s where computers and programming have come in. They can render more of that picture that we can’t see. Once they have that mapping, they can test that against the actual market to see if it fits. They can test the map’s accuracy.
For the mappers and the mapmakers, they’re almost always wrong. Even when the map is right for a while, it will eventually change. The map, the actual picture itself, is constantly evolving. It’s dynamic. Renaissance Technologies seems to have had their map work for them for about 30 years now. Their system of rendering has worked. Their evolving process has worked. And, it shows.
A few notes from it all:
- You are probably not looking at the full picture.
- Even if you are looking at the full rendering, it might not be correct.
- A few of the smartest people in the world seem to have figured out the most correct mapping.
And the numbers prove it. Jim Simons is worth approximately $22 billion.