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              <text>A brief review of portfolio optimization techniques</text>
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              <text>Deep learning; Evolutionary techniques; Machine learning; Portfolio optimization; Quantum computing; Reinforcement learning; Statistical measures</text>
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              <text>Portfolio optimization has always been a challenging proposition in finance and management. Portfolio optimization facilitates in selection of portfolios in a volatile market situation. In this paper, different classical, statistical and intelligent approaches employed for portfolio optimization and management are reviewed. A brief study is performed to understand why portfolio is important for any organization and how recent advances in machine learning and artificial intelligence can help portfolio managers to take right decisions regarding allotment of portfolios. A comparative study of different techniques, first of its kind, is presented in this paper. An effort is also made to compile classical, intelligent, and quantum-inspired techniques that can be employed in portfolio optimization.  2022, The Author(s), under exclusive licence to Springer Nature B.V.</text>
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              <text>Gunjan A.; Bhattacharyya S.</text>
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              <text>Artificial Intelligence Review, Vol-56, No. 5, pp. 3847-3886.</text>
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              <text>&lt;a href="https://doi.org/10.1007/s10462-022-10273-7" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/s10462-022-10273-7&lt;/a&gt;
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              <text>ISSN: 2692821; CODEN: AIRVE</text>
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              <text>Gunjan A., Christ Deemed to be University, Bangalore, India; Bhattacharyya S., Rajnagar Mahavidyalaya, Birbhum, India</text>
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