Development of stock portfolio trading systems using machine learning methods

Investment portfolio theory is an important foundation for portfolio management, which is a well-studied but not saturated topic in the academic community. Integrating return forecasting in investment portfolio formation can improve the performance of portfolio optimization model. Since machine learning models have shown a superiority over statistical models, in this research, a approach of forming the stock portfolio in two stages is presented. first step, by implementing neural network, suitable stocks are selected for purchase, in the second step, using the (MV) model, the optimal weight in investment portfolio is determined for them. In particular, the stages of selecting suitable stocks and forming a stock portfolio are the two main stages of the model developed in this research. first step, a convolutional neural network model is proposed to predict stock buy and sell points for the next period.second step, stocks that are labeled as buys are selected as stocks suitable for buying, and MV model is used to determine their optimal weight in the stock portfolio. The results obtained using 5 shares of Tehran stock market as a study sample show that the efficiency and Sharpe ratio of proposed method is significantly better than traditional methods (without filtering suitable stocks)