THE APPLICATION OF MODEL PREDICTIVE CONTROL ON STOCK PORTFOLIO OPTIMIZATION WITH PREDICTION BASED ON GEOMETRIC BROWNIANMOTION-KALMAN FILTER

Abstract

A stock portfolio is a collection of assets owned by investors, such as companies or individuals. The determination of the optimal stock portfolio is an important issue for the investors. Management of investors’ capital in a portfolio can be regarded as a dynamic optimal control problem. At the same time, the investors should also consider about the prediction of stock prices in the future time. Therefore, in this research, we propose Geometric Brownian Motion-Kalman Filter (GBM-KF) method to predict the future stock prices. Subsequently, the stock returns will be calculated based on the forecasting results of stock prices. Furthermore, Model Predictive Control (MPC) will be used to solve the portfolio optimization problem. It is noticeable that the management strategy of stock portfolio in this research considers the constraints on assets in the portfolio and the cost of transactions. Finally, a practical application of the solution is implemented on 3 company’s stocks. The simulation results show that the performance of the proposed controller satisfies the state’s and the control’s constraints. In addition, the amount of capital owned by the investor as the output of system shows a significant increase.