Last modified: 2018-07-07
Abstract
Portfolio optimization aims to protect investors against any risks which they may experience. Stock diversification is one of the solutions to optimize stock portfolio, where a diverse portfolio tends to have less risk then the undiversified one. Agglomerative clustering is one of hierarchical clustering method. To apply diversification concept, Agglomerative Clustering is used to cluster 40 different assets based on their financial ratio scores (EPS, PER, PEG, ROE, DER, Current Ratio and Profit Margin). Genetic algorithms (GAs) are search methods based on principles of natural selection and genetics. After the stocks are clustered, Genetic algorithm with heuristic crossover is applied on each cluster alongside to determine the weight of each stock. In this paper, a possibilistic mean-semi-absolute deviation optimization model is used where cardinality, quantity, and transaction cost are considered as constraints. We also use the assumption that the returns of risky assets are fuzzy numbers. The implementation shows that the method gave a higher level of return (32.60%) and Sharpe’s ratio (19.1868) compared to S&P 500 index in the same period of time (12.34% and 2.7 respectively).
Â