A novel approach to fulfill active portfolio management and automatic stock trading based on feature selection algorithm

This paper intends to present an integrated method to fulfill active portfolio management approach that is based on predicted prices of each six- chosen stock in a four-year time period. First of all Markowitz model is implemented to achieve weight of each stock in each year of four year period. Then, twenty two technical indicators as the features of each stock were are regarded as the input data of genetic algorithm (GA) known as a feature selection technique. Two well-performed forecasting methods called k-nearest neighborhood (kNN) and artificial neural network (ANN) are integrated with GA to extract predicted prices of each stock in defined time period. According to predicted price resulted by GA-NN and GA-kNN, a trading strategy is proposed that has an input signal (-1 , 0, 1) which imply to buy, sell and do nothing respectively. Returns of created portfolios were compared with the return of buy and hold strategy as the representative of passive portfolio management approach. The portfolio resulted by GA-NN outperformed two other portfolios for our given data. This conclusion emphasizes on superiority of employing active portfolio management to passive portfolio management in terms of tackling fund management issue for our given data.