Abstract
One of the major tasks in portfolio management is to determine the number of stocks with relatively high net value on the stock market. This work presents a knapsack based portfolio selection model that considers the expected returns, prices and budget. It represents a typical resource allocation model in which limited resource is apportioned among a finite number of stocks. The objective is to maximize an associated return function. The work is implemented for some numerical data to illustrate the application of the model and demonstrate the effectiveness of the designed algorithm. Numerical results have shown that the optimization model yields promising results.
Publisher
African - British Journals
Reference25 articles.
1. Anagnostopoulos, K. & Mamanis, G. (2011). The Mean-Variance Cardinality Constrained Portfolio Optimization Problem: An Experimental Evaluation of Five Multi-objective Evolutionary Algorithms. Expert Systems with Applications, 38(11), 14208-14217.
2. Bevilaqua, N., Da Silva, O., & De Mattos, G. (2020). Risk Return Optimization using the Knapsack Problem in the Formation of a Stocks Portfolio. Case Study of a Brazilian Investment Site. International Journal for Innovation Education and Research, 8(9), 280-289.
3. Bhattacharyya, R., Kar, S. & Majumder, D. (2011). Fuzzy Mean-Variance-Skewness Portfolio Selection Models by Interval Analysis. Computers & Mathematics with Applications, 61(1), 126-137.
4. Bitar, A., De Carvalho, N. & Gatignol, V. (2023). Covariance Matrix Estimation for Robust Portfolio Allocation (Doctoral Dissertation, Centrale Supelec; Universite de Technologie de Troyes; Universite Paris Cite).
5. Casarone, F., Scozzari, A. & Tardella, F. (2013). A New Method for Mean-Variance Portfolio Optimization with Cardinality Constraints. Annals of Operations Research, 205(1), 213-234.