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- W4323786197 abstract "In today's high inflation environment, it is very important to protect our capital from depreciation. One way to preserve your capital is to try to invest it in stocks anticipating for big return. But sometimes the expectations may fail due to underestimating the risk level of stock portfolio investments. The objective of this work is to develop a risk-return ratio optimization model for stock portfolio enabling to screen the adequate equities for inclusion to investment portfolio and set its capital allocation ratio. We propose a two-stage model, firstly enabling to select the initial set of equities by applying Self-Organizing Maps (SOM) by identifying a set of most influential factors to use as input variables for SOM. The method for the second stage of research is proposed for deciding the weight-based ratios for capital to be distributed among the portfolio equities. The nature-inspired Grey Wolf Optimization (GWO) algorithm is applied for finding the optimal weights allocation among the portfolio shares based on Mean−Variance portfolio minimization condition, which correspondingly define the Risk and Return rating of portfolio. The sensitivity of the GWO algorithm to the number of iterations, wolf herd size and stocks weight limits was investigated for defining optimal values of these parameters for the best portfolio diversification. The experimental verification of the model was performed on stock set from S&P500 companies. The proposed model based on SOM selected and GWO balanced portfolio outperformed the direct investment to S&P500 index by 3.52% higher profitability." @default.
- W4323786197 created "2023-03-11" @default.
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- W4323786197 date "2023-01-01" @default.
- W4323786197 modified "2023-09-25" @default.
- W4323786197 title "Stock Portfolio Risk-Return Ratio Optimisation Using Grey Wolf Model" @default.
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- W4323786197 doi "https://doi.org/10.1007/978-3-031-24453-7_10" @default.
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