Abstract
The framework proposed in this paper is designed such that a client gets stable returns based on his/her risk-taking capability. It gives the optimal weights allocated in different asset classes like risky and risk-free assets based on the risk aversion factor of the client. Diversification of stocks is achieved by clustering the stocks with the K-means clustering algorithm. Portfolio optimization is achieved using the genetic algorithm. The framework provides the optimal proportion to the selected individual stocks and risk-free assets. Stocks of companies listed under S&P 500 are used for evaluating the framework.
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Gupta, R., Mahajan, Y., Ahuja, P.M., Ramteke, J. (2020). Portfolio Management Using Artificial Intelligence. In: Mathur, G., Sharma, H., Bundele, M., Dey, N., Paprzycki, M. (eds) International Conference on Artificial Intelligence: Advances and Applications 2019. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-1059-5_24
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DOI: https://doi.org/10.1007/978-981-15-1059-5_24
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