Advances in Portfolio Optimization and Computational Finance: Bridging Theory, Machine Learning, and Real-World Applications

Dear Colleagues,

In the ever-evolving field of computational finance, portfolio optimization stands as a central challenge; in this context, researchers and practitioners seek to develop strategies that effectively balance risk, return, and other critical constraints. The traditional portfolio optimization model, grounded in Markowitz’s mean-variance analysis, has been expanded via various innovative methodologies that address practical constraints, adapt to market dynamics, and respond to investor-specific objectives. The current literature regarding portfolio optimization explores numerous approaches, from those that refine risk-adjusted return metrics to those that accommodate market conditions, transaction costs, and investor preferences. These advancements underscore the importance of integrating mathematical rigor with real-world applicability, prompting a more nuanced examination of the dynamic interactions between risk, return, and financial constraints.

This Special Issue welcomes the submission of high-quality research papers that address various aspects of portfolio optimization and computational finance, including theoretical, empirical, and computational contributions that provide new insights, propose innovative methodologies, or enhance existing models to tackle challenges in this evolving field. The scope of this Special Issue includes, but is not limited to, the following topics:

• Advanced mathematical models for portfolio optimization, incorporating novel risk and return measures;
• Multi-objective optimization frameworks and their applications in portfolio management;
• Robust and stochastic optimization approaches in uncertain market conditions;
• Data-driven and machine learning techniques for enhancing portfolio allocation strategies;
• Risk-budgeting, risk-parity, and other alternative risk allocation strategies;
• Dynamic portfolio rebalancing and transaction cost modelling;
• Evaluation of portfolio performance using benchmarks and backtesting frameworks;
• Computational techniques and algorithms for large-scale portfolio optimization;
• Incorporating environmental, social, and governance (ESG) factors in portfolio decisions;
• Applications of behavioural finance principles in portfolio optimization.

We encourage submissions from researchers and practitioners that aim to advance the field of portfolio optimization, with contributions that bridge the gap between theoretical developments and practical implementations.

Prof. Dr. Pilar Grau-Carles
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• portfolio optimization
• computational finance
• machine learning in asset allocation
• risk management
• stochastic optimization models