Jan

22

This is the best explanation I have seen so far concerning the Poisson Process & Poisson Distribution. It has clearly defined math variables (something explanations involving maths seldom do) & very clear practical examples. I wish more people describing math concepts wrote like this.

A Poisson process is a model for a series of discrete events where the average time between events is known, but the exact timing of events is random. The arrival of an event is independent of the event before (waiting time between events is memoryless).

Zubin Al Genubi comments:

Seems useful to study occurrences of crash or bear market.

Big Al offers:

3Blue1Brown does some great math videos, eg:

Binomial distributions | Probabilities of probabilities, part 1

H. Humbert is skeptical:

It's hard to know without a lot of study whether this is useful for any real-world applications. This distribution has been used in network traffic modeling since the advent of networks because networks have packets and packets have rates that COULD be pretty stable over the period of interest. It worked pretty well for legacy telephone networks, but not so much as computer networks become more and more complex. People still like it because it's a relatively simple formula where if you know the lambda you know everything, and it has no memory of the past so you don't need to store the past, but it doesn't really work well. It doesn't even work that well for predicting meteor showers because the rate itself is subject to change, so can it really work well as a predictive tool for the markets?

Andrew Moe writes:

Poisson has shown to be useful in predicting soccer and hockey scores. In the markets, one test might be to model uncorrelated markets against each other in a double Poisson, like the soccer quants do. Offense and defense, up markets and down.


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