Comment by EdwardAF-IT | Hacker News Reader

Comment by EdwardAF-IT | Hacker News Reader

A very interesting article, suzyahyah. I especially appreciated your definition of stationarity, a concept with which I struggled in my own time series class. If I understand correctly, it sounds like the basic premise is that a fundamentally statistical methodology (LLMs) can't realistically predict a non-stationary data generation, which makes sense.

Separately, I've wondered for some time if there might be some reliable way to predict non-stationary data. While I don't have the answer, it occurs to me that it will possibly be a non-statistical method due to the fundamental incompatibilities. However, it also occurs to me that, given enough information, every data-generating process actually could be predicted. For instance, in the stock example, if you could model every single input into the system of a single company's stock, including every variable affecting every human that might conduct a transaction of it (daunting and unrealistic as that might be, but this is a thought experiment), then I believe the problem of prediction stops being non-stationary and in fact becomes completely deterministic, if complex. In such a scenario, wouldn't you be able to accurately make your prediction? I believe that perhaps chaos theory could present us with some solutions here where pure statistics (or, rather, simple statistics) cannot.

Just my 2 cents..

Chaos theory does provide us with a tool here: The Lyapunov exponent[1] and the related Lyapunov time[2]. These allow you to characterize how far into the future you can expect to be able to predict the behavior of a dynamical system. To Prerok’s example of predicting the weather, this is why 3 day weather forecasts tend to be great, 10 day weather forecasts tend to be good, and weather forecasts two months out are just the general average trends for the climate in that time and place.

There are chemical systems where Lyapunov time is small enough that you can only predict seconds or minutes into the future and astronomical systems that are nonlinear and chaotic but have a long enough Lyapunov time that you can make reasonable predictions for millions of years. For both of these scales, the Lyapunov time still bounds how far into the future you can expect your predictions to remain near to the actual behavior of the system.

I do not know the Lyapunov times of the financial markets. That said, mathematically-sophisticated professional analysis frequently get their predictions wrong in major ways, so I expect there is a pretty hard bound on predictive quality caused by a short Lyanpunov time of the markets themselves.

[1] https://en.wikipedia.org/wiki/Lyapunov_exponent

[2] https://en.wikipedia.org/wiki/Lyapunov_time

I think there's a close linkage between "predictable" and "stationary". Ultimately, if you strip everything away, either there's a core where the future looks like the past (which is equivalent to being stationary), or there isn't. That core could be "the laws of physics and base conditions are stationary", and everything else is deterministic functions applied on top, but the fundamental process is trying to find that stationary core.

In the finance space, the stationary core is often some 'stylized fact' that you're hypothesizing will hold true. This could be e.g., the momentum factor, that if you strip away the noise, there's an underlying trend that will hold over an extended duration.

That's a bit of a vacuous statement. Stationarity makes sample statistics meaningful due to the LLN, sure, but almost nothing is stationary (and even if something is, there is no way to know anyway, you can only assume). If you condition on enough variables and do enough transformations you may get something seemingly stationary and therefore trivially predictable. But all the practical complexity is in that structure you need to specify. The true prediction "problem", when people work on such "problems", really is in the stuff besides the thing that you can just use averages to predict.

The weather is an example of a non-linear dynamic system. Try as we might, we still cannot predict it really even a few days in advance. The stock market is much worse, even, so, no, it cannot be predicted.

This brings up the philosophical question of whether humans have free will or is everything deterministic following the laws of physics.

Exists randomness or is it just lack of information? :)

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