Abstract
Forecasting future moves of stock markets has been and always will be of great interest to researchers and practitioners. This paper proposes a multi-objective programming methodology to select the optimum technical indicators to be used as input in a Neural Network (NN) in order to predict stock market prices. A new mathematical model will be proposed which involves objective functions and constraints to filter out the noisy signals and maximize the prediction power. The 0–1 multi-objective model aims to select the indicators maximizing the covariance of the indicators with the output of the NN while minimizing the covariance among the indicators themselves. The Multi-objective model is transformed via the Epsilon Constraint technique. Many efficient configurations of indicators for different values of epsilon are evaluated and their resulting errors are presented. Our approach provides a systematic methodology in order to choose the variables that significantly affect price movements. The methodology is applied on the NIKKEI225 stock market index.