In many businesses, it is often necessary to know how to make classifications in order to make decisions. For example, banks need to be able to classify credit applicants between those who will be able to meet their obligations and those who might find it difficult to do so. Public administrators have to decide whether to allocate certain areas of the territory to playgrounds or to parking lots. Financial analysts need to be able to distinguish assets whose future value could increase from those for which it could decrease. Obviously, in order to produce good classifications, the professional or administrator on duty must have a deep knowledge of the area in which she operates and must know how to effectively use the information available.
In other words, classification is a complex task. Therefore, especially in the era of the so-called Big Data, the tools that can support the decision maker in the classification activities are welcome. (Attention: support, do not replace!)
In the scientific literature, many types of such tools have been proposed and continue to be proposed. In particular, classification tools based on the so-called Machine Learning, have recently been highlighted for their effectiveness. In qualitative terms, and simplifying a bit, with “Machine Learning” we mean a wide family of methods that are inspired by how superior living beings and Nature “produce” intelligent processes.
Decision Trees are one of such intelligent methods of classification. Again in qualitative terms, a Decision Tree is a tool which, by applying Machine Learning techniques to an initial set of data coming from the objects to be classified, is able to autonomously carry out a classification of these same objects. In particular, a Decision Tree is capable of extracting rules such as:
IF the variables considered take on certain values THEN a given action must be taken OTHERWISE another action must be taken.
Recently, in the international scientific literature a study has been published in which an automatic system based on Decision Trees has been proposed and applied to classify financial shares among those whose future price could increase (rise) and those for which it could decrease (decline).
The initial set of data used is relatively “simple”: prices (closing, opening and so on), volumes and some technical analysis indices, all quantities relating to the various actions considered.
Despite this simplicity, the ability of this automatic system to correctly classify future rises / falls in share prices, and therefore to predict their direction, is very good. Obviously, since this is a recent application, it requires further confirmation and in-depth analysis. However, the good conditions are all there.
* Forecasts based on Decision Trees of the rises / falls of the Bitcoin cryptocurrency prices for the period going from 20/01/2014 to 22/02/2021, for a total of 1779 working days. The first panel shows the logarithmic transform of Bitcoin opening price in the indicated period (using the logarithmic transform makes the price trend more readable time), in the second panel the forecastsof the direction of the price course of the cryptocurrency are reported considered for the same period (rise = 1; fall = -1).
by Marco Corazza,
Scientific Committee Egonon