Abstract
For the investors, portfolio selection from the set of assets in the population is very essential practice in the capital market. Risk minimization is one of the main concerns in portfolio selection problem. Therefore, risk distribution is very common practice in risk-budgeted portfolio optimization. In this paper, a novel risk-budgeted portfolio selection strategy using invasive weed optimization (IWO) is proposed to maximize sharp ratio. Invasive weed optimization is a nature based approach inspired by the invasion process of weeds in the plants. The natural invasion has been mathematically modeled to search the optimum solution in the solution space. An experimental study has been done to evaluate the proposed strategy by comparing its performance with Genetic Algorithm on dataset of the S&P BSE Sensex of Indian stock exchange (30 stocks). Study reveals the superior performance of the proposed strategy.
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Acknowledgements
This work is supported by the major research project funded by ICSSR with sanction No. F.No.-02/47/2019-20/MJ/RP.
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Shahid, M., Ansari, M.S., Shamim, M., Ashraf, Z. (2022). A Risk-Budgeted Portfolio Selection Strategy Using Invasive Weed Optimization. In: Tiwari, R., Mishra, A., Yadav, N., Pavone, M. (eds) Proceedings of International Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-3802-2_30
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DOI: https://doi.org/10.1007/978-981-16-3802-2_30
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