Doing the stationary process

 

Doing stock pricing if everyone kept the same schedule.

The equations are written out analytically, meaning not as a random process. Then they ask what the stock price would be if the pricings had random noise, in essence.  The end terms are from a series that successively removes moments in the price distribution, making that distribution have look Gaussian.

Time synchronous means their is a risk free rate, that implies a market going to any necessary size to keep the safe rate safe. It is like look infinitely back and estimating infinitely forward.

This is OK, as a statistic good under the assumption that today we start to be stationary. The information it provides is mostly how far off balance we are, at the moment.

We can do this same method, with a finite assumption. Basically, assume the corporation is run like a Walmart structured queue, then give it the standard S/L, as per model.  Instead of assuming the corporation is stationary, we have an equivalent. We assume we have a complete sequence of sales and costs the covers a known depreciation cycle for the corporation. Then you find the S/L ratio that keeps the queues stable and you derive price as deposit gains over the cycle.  It helps if we have an equivalent S/L sequence from the complete monetary zone, or extract the corporate S/L equivalent.

Price then become the yield per unit of 'capital' is a good term. It is an accumulant over the depreciation cycle.  But are are finite in extant and have boundary conditions. The solution is quantas, units of  commutative property.  this is the economy of scale part, the 'setting the items per basket'. You are going to have baskets, boxes which can hold a bound variant amount of actual goods.

We are finding the maximum entropy scale which minimizes redundancy. We do not want to spend two coin tosses when one will do. Three binomails, corporate output, deposits and loans.   The corporation is trying to keep these balanced.