TY - GEN

T1 - Implementation of agglomerative clustering and genetic algorithm on stock portfolio optimization with possibilistic constraints

AU - Yusuf, R.

AU - Handari, B. D.

AU - Hertono, G. F.

PY - 2019/11/4

Y1 - 2019/11/4

N2 - 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 than the undiversified one. Agglomerative clustering is a hierarchical clustering method. Applying diversification concept, agglomerative clustering is used to cluster 40 different assets based on their financial ratio scores (Current Ratio, Debt-Equity Ratio, Profit Margin, Return on Equity, Price/Earnings per Growth, EPS diluted, and Price/Earnings Ratio). Genetic algorithm is search method based on principles of natural selection and genetics. After the stocks are clustered, Genetic algorithm with heuristic crossover is applied to each cluster alongside to determine the proportion 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 (29.77 %) and Sharpe's ratio (18.71) compared to S&P 500 index in the same period of time (12.34 % and 2.7 respectively).

AB - 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 than the undiversified one. Agglomerative clustering is a hierarchical clustering method. Applying diversification concept, agglomerative clustering is used to cluster 40 different assets based on their financial ratio scores (Current Ratio, Debt-Equity Ratio, Profit Margin, Return on Equity, Price/Earnings per Growth, EPS diluted, and Price/Earnings Ratio). Genetic algorithm is search method based on principles of natural selection and genetics. After the stocks are clustered, Genetic algorithm with heuristic crossover is applied to each cluster alongside to determine the proportion 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 (29.77 %) and Sharpe's ratio (18.71) compared to S&P 500 index in the same period of time (12.34 % and 2.7 respectively).

KW - agglomerative clustering

KW - genetic algorithm

KW - optimization model

KW - Portfolio optimization

UR - http://www.scopus.com/inward/record.url?scp=85075005935&partnerID=8YFLogxK

U2 - 10.1063/1.5132455

DO - 10.1063/1.5132455

M3 - Conference contribution

AN - SCOPUS:85075005935

T3 - AIP Conference Proceedings

BT - Proceedings of the 4th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2018

A2 - Mart, Terry

A2 - Triyono, Djoko

A2 - Anggraningrum, Ivandini T.

PB - American Institute of Physics Inc.

T2 - 4th International Symposium on Current Progress in Mathematics and Sciences 2018, ISCPMS 2018

Y2 - 30 October 2018 through 31 October 2018

ER -