Stock Prediction using Decision Tree: Risk Management
Filed under: data mining stock market, decision tree, risk management
This post is the last one of a series on using decision tree for stock prediction. Here are the first, second, third and fourth posts. This final post is dedicated to the risk management part of the system.
At this step, we have as input a list of transactions containing the ticker name (e.g. “MSFT US Equity” for Microsoft, etc.), the date of transaction and the quantity. As the system is “long only”, the quantity is always positive (+1 means buy one stock).
The risk management is informally defined here as a way of managing a portfolio by closing it in certain specific situations. In this system, a simple take-profit/stop-loss at the portfolio level is used. Every day, a number of transactions are carried out. All transactions in the same day are gathered in one portfolio. At any time, the portfolio return is calculated to check for both the take-profit and stop-loss limits. If the portfolio return reaches the take-profit (e.g. +5%) or the stop-loss (-20%), it is closed.
In addition, each portfolio is subject to a maximum number of days of conservation. When this number of days is reached, the portfolio is closed. To find the best values for the take-profit, stop-loss and number of days of conservation, a three-dimensional grid search is performed. Note that there is nothing to prevent the system from overfitting at this step of the methodology (unlike the decision tree step that uses cross-validation). This could of course be improved.