The decision tree theory is one of the main components of an accurate financial report; however, not every person manages to fully grasp the concept. Decision trees are present in economic forecasting and corporate finance setting but are also essential to investment – both in theory and practice.
The Basics of the Decision Tree Theory
When it comes to the decision tree theory, the idea is as follows: at some point, someone has a decision to make and determine whether to take on a capital project or not – or simply pick between two opposite ventures. This is usually referred to as the decision node.
This decision is generally made on possible outcomes that are a result of a particular course of action. The outcomes would sound something like “the cash flow is expected to decrease (or increase) by $2 million,” and it will all be represented by the end nodes.
Still, since the events shown in the end nodes have their place in the possible future, it still remains uncertain. Even if there’s a 99% chance of the outcome being as predicted, there is still a chance of the other 1% to also occur. This will mean a different end node.
Therefore, the decision tree is a map of nodules that will present the potential outcomes. Each event can lead to a loss or a gain – and depending on the number of nodules, you may make the correct decision for your company. The decision can be made after a personal analysis, or by using a special algorithm for that.
Elements and Rules
Going from left to right, a decision tree is a network of splitting paths – but with no converging paths. As a result, one of these decision trees can get fairly big – and can become increasingly difficult to draw manually.
While decision trees used to be drawn manually in the past, nowadays companies prefer the use of some kind of software.
This software was made with decision tree theory in mind, and can automatically predict possible outcomes by means of algorithms and AIs.
Each decision tree is generally linearized by means of decision rules – and within these rules, the outcome is generally the content of a particular leaf node. Each result is conditioned by a certain path that uses the “if” clause.
Typically, the rule will have the following form: if 1st condition and 2nd condition and 3rd condition then outcome. The target is variable on the right, and the relations can be either casual or temporal.
A decision tree theory can be combined with other techniques in order to provide better data. With a brief explanation, virtually everyone can understand and analyze a decision tree.
Still, calculations can get very complex – particularly if the values are uncertain or if there are existent links in the outcomes.
The decision tree theory is a good tool for predicting the future of a company, provided you introduce correct data. Once every variable is taken into account, you can make the right decision that will bring you the profit that you want.