I was sitting in my networks class today, thinking of how it would be possible to implement an algorithm for taking into consideration the similarity of documents for teasing apart temporal interference, when I started coming to a more coherent model of what I’ve been trying to do in general.  This article will set up some early ideas for a model of what’s going on, what we’re attempting to accomplish, and possible general procedures for doing so.  It also sets up some terminology.

Essentially, at this stage we have a data history.  This data history is made of two parts, a set of price point information and a set of several relevant news articles.

We will call our price point information, the Time-Sensitive Response Variable, or TSRV.  Let us explore what we are assuming about the TSRV.

I began to think about the idea of making analogies to physics, because that’s something I understand a little better than economics.  I think the way a lot of people approach the stock market for investing is to think about price as position.  This gives way to the idea that the market may often find itself trending one way or the other.  The idea behind a trend is that the price has a certain velocity, which is resistant to change (intertia),  until some outside influence (force/acceleration) causes a reversal or something of that nature.

Having looked at a lot of stock graphs, I am not so sure this is the case.  I understand many successful traders would disagree with me, but for the sake of this project, we are going to think of the TSRV for price as velocity, that is it is resistant to movement without outside influence–it experiences inertia.  In this concept the price over time is a derivative of some unknown value behind the scenes, which I intuitively feel might exist, that behaves more like the traditional concept of price.

You might say that the price over time is constantly gyrating madly about, so thinking that the TSRV is resistant to change is ridiculous, but keep in mind I said it was resistant to change…undisturbed.  There is a constant barrage of outside influence coming in to affect the price.  I consider these outside forces, the analogy of a force in physics, which is proportional to acceleration.

We will be calling these outside influence perturbations.  Perturbations could inevitably take many forms, but for simplicity we will be thinking of individual perturbations as being discrete chunks of constant force with finite lengths.  In physics, we know that the acceleration observed is due to the sum of forces acting on an object.  So too is the effect of the perturbations additive.

We understand that there are a great number of perturbations affecting any TSRV, some of which we know about, many of which we do not.  Furthermore, we generally only know the existence of some perturbations, not any details of their strength, direction, or duration.  The perturbations we do not know about, will generally seem to manifest themselves as noise, but it should be known that under this model, there is no random TSRV movement, only movement due to unconsidered perturbations.

For our purposes with regards to automated news analysis, we have a set of several relevant news articles that we assume have some effect on the movement of the price, in this fashion.  Our end-goal is to approximate the effect that news articles have.  According to our definition of the effect of perturbations, there are two dimensions of the effect of a perturbation, the strength/direction of the acceleration caused, the influence, and the length of time it effects the price, the  duration.

Given a set of perturbations, we want to find some way to determine their characteristics, so that we can hopefully use some similarity metric to indicate possibly future price moves.

It is well understood that the duration will vary for most news articles, but the computational difficulty of determining both seems at this juncture beyond the scope of what I can hope to accomplish.  Perhaps sometime in the future we can devise more complex algorithms, but for now we will focus on determining the influence of perturbations given some predetermined duration.

Now that we have a more formal understanding of our basic assumptions, we can consider possible means of accomplishing the approximation of the characteristics of perturbations, but I’ll save that for next time.

And yes, that’s a picture of Shwayze.