Insights from a Decision Tree

Assuming that an analyst has made all necessary pre-processing tasks prior to the data mining phase, we are ready to deploy analytical methods such as decision tree learners that can classify unseen cases. For the goal of stock prediction we assume that we have the following data collected :

The column named as XAACLASS is the target column that we wish to classify. Essentially here we have the following classes :

-price change percentage greater than 2%
-price change percentage less than -2%
-price change percentage greater than 0% and +2% inclusive
-price change percentage between -2% inclusive and 0% inclusive

In other words, each line shows us the state of the stock we wish to predict, that occurs given the rest of the market indices (such as realTimeFTSE, realTimeDAX, etc).

So, let us assume that we are ready to build such a model. However, we have to decide the time window that our predictions will be made for...do we wish to predict what the stock price change will be 2 hours ahead? How about 1 day ahead?

Before dealing with this issue, i wanted to see how good a predictive model is by predicting the stock price percentage change right now, based on the current market conditions. Here is a decision tree that is created from such data:

More to come on the next post where the model seen above will be explained in detail. Until then please read the post from this blog about the same problem. If you can, read Fooled By Randomness also...