MARKOV CLUSTERING FOR PORTFOLIO CONSTRUCTION UNDER STOCHASTIC ENVIRONMENT

Until recently there were still many new investors and financial consultants who face difficulties in stocks portfolio construction, both in terms of selection and deciding how large portion of each asset in the portfolio. It takes relatively longer time and hence they constantly strive to achieve faster portfolio construction because timely information can mean the difference between a deal struck or missed, which translates to substantial profit or loss. This paper aims to analyze the efficiency of Markov clustering processes for portfolio construction in order to speed up assets selection based on correlation principle. Furthermore, portfolio optimization for selected assets will be achieved with Markovian modeldriven by a Brownian motion process under stochastic environment. We compare the performance of the constructed portfolio to LQ45, Kompcisioo, and Bisnis2y indices using Sharpe Ratio, and the results show that it outperforms these benchmark indices. Hence, investors might use Markov clustering technique in the stocks selection as an alternative since it is more efficient in terms of time and in this case proven to provide better reward to risk taken by the investors