BRIDGING TRADITION AND INNOVATION: A LITERATURE REVIEW ON PORTFOLIO OPTIMIZATION

Ștefan Rusu () and Marcel Boloș ()
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Ștefan Rusu: University of Oradea, Oradea, Romania
Marcel Boloș: University of Oradea, Oradea, Romania

Annals of Faculty of Economics, 2024, vol. 33, issue 1, 337-344

Abstract: Portfolio optimization plays a crucial role in investment decision-making by balancing risk and return objectives. With the aim of improving portfolio performance, while enhancing risk management, this literature review explores traditional and artificial intelligence-powered approaches for portfolio optimization. From the traditional methods of portfolio optimization, methods such as random matrix theory, shrinkage estimators, correlation asymmetries and partial correlation networks are presented. While, from the artificial intelligence realm, techniques such as machine learning efficient frontiers, performance-based regularization, neural network predictors and deep learning models for direct optimization of portfolio Sharpe ratio are highlighted. Intertwining the traditional methods, with artificial intelligence techniques, this review highlights relevant portfolio optimization research useful for academics and practitioners alike.

Keywords: artificial intelligence; machine learning; portfolio optimization; finance; investing; financial markets (search for similar items in EconPapers)
JEL-codes: C01 C02 C58 C61 G11 G17 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:ora:journl:v:33:y:2024:i:1:p:337-344

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