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Advanced Portfolio Optimization and Management
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Advanced Portfolio Optimization and Management

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Mathematics and Finance".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 31347

Special Issue Editor

Dr. Mario Maggi

E-Mail Website
Guest Editor
Department of Economics and Management, University of Pavia, 27100 Pavia, Italy
Interests: financial risk; portfolio optimization; data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Although modern portfolio theory dates back to Markowitz’s seminal contribution, the evolution of financial markets requires updated models and techniques to face the new challenges. In the last two decades, various national and worldwide events have produced large waves of risk propagation. The analysis of the financial risk should thus adopt a richer framework than the classic mean-variance, considering various aspects related to the risk. The recent events that have affected the worldwide markets, influenced the dependence among the various markets and countries, calling for a careful analysis of risk and diversification. This Special Issue aims at collecting papers from academics and practitioners focused on the recent developments of models, techniques, and empirical analyses related to portfolio optimization and asset allocation, with a particular interest in the analysis of financial risk and diversification.

The topics covered in this Special Issue will include, but are not limited to, the following:

  • Theoretical research on portfolio optimization and asset allocation;
  • Empirical application of portfolio optimization and asset allocation;
  • Financial economics issues related to portfolio optimization;
  • Financial risk and other related risks analysis;
  • Numerical aspects and software development to support portfolio optimization and management.

Dr. Mario Maggi
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. Journal of Risk and Financial Management is an international peer-reviewed open access monthly 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 1400 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.

Keywords

  • portfolio optimization
  • asset management
  • financial risk
  • portfolio diversification
  • systemic events
  • financial crisis

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

28 pages, 19598 KiB  
Article
Portfolio Performance of European Target Prices
by Joana Almeida and Raquel M. Gaspar
J. Risk Financial Manag. 2023, 16(8), 347; https://doi.org/10.3390/jrfm16080347 - 25 Jul 2023
Cited by 1 | Viewed by 1735
Abstract
This paper examines the performance of actively managed portfolios constructed using target price recommendations provided by analysts. We propose two methods for constructing portfolios based on Bloomberg’s 12-month target price consensus, which serves as a signal to buy or sell assets. Using a [...] Read more.
This paper examines the performance of actively managed portfolios constructed using target price recommendations provided by analysts. We propose two methods for constructing portfolios based on Bloomberg’s 12-month target price consensus, which serves as a signal to buy or sell assets. Using a sample of 50 European stocks over a 19-year period (from 1 April 2004 to 31 March 2023), we compare the performance of target-price-based portfolios to traditional alternatives, such as a naïve homogeneous portfolio and the Eurostoxx 50 index, as well as to passive portfolios based on average recommendations. We also look into the mean-variance efficiency of these portfolios and find that all exhibit similar levels of efficiency, which are well below the performance of the theoretical tangent portfolios. Our results indicate that target-price-based portfolios show performance very close to that of the naïve homogeneous portfolio. Even the passive “average” target price portfolios, which require previous knowledge of targets for the entire investment period, are unable to outperform the naïve portfolio. Our main findings are based on a 15-year investment horizon but are robust when considering smaller maturities and out-of-sample data. We also investigate the impact of rebalancing on portfolio performance and find that it does pay off in the long run (over an 8-year investment period), but the frequency of rebalancing matters. Rebalancing only once a year is as detrimental to performance as not rebalancing at all. However, it is unclear whether the transaction costs associated with frequent rebalancing would offset any relative outperformance. Overall, our study contributes to the literature on portfolio management and market efficiency by demonstrating the potential benefits and limitations of using target price recommendations to construct portfolios, highlighting the importance of carefully considering rebalancing strategies to achieve optimal performance. Full article
(This article belongs to the Special Issue Advanced Portfolio Optimization and Management)
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25 pages, 1358 KiB  
Article
Algorithm Aversion as an Obstacle in the Establishment of Robo Advisors
by Ibrahim Filiz, Jan René Judek, Marco Lorenz and Markus Spiwoks
J. Risk Financial Manag. 2022, 15(8), 353; https://doi.org/10.3390/jrfm15080353 - 8 Aug 2022
Cited by 12 | Viewed by 3815
Abstract
Within the framework of a laboratory experiment, we examine to what extent algorithm aversion acts as an obstacle in the establishment of robo advisors. The subjects had to complete diversification tasks. They could either do this themselves or they could delegate them to [...] Read more.
Within the framework of a laboratory experiment, we examine to what extent algorithm aversion acts as an obstacle in the establishment of robo advisors. The subjects had to complete diversification tasks. They could either do this themselves or they could delegate them to a robo advisor. The robo advisor evaluated all the relevant data and always made the decision which led to the highest expected value for the subjects’ payment. Although the high level of efficiency in the robo advisor was clear to see, the subjects only entrusted their decisions to the robo advisor in around 40% of cases. In this way, they reduced their success and their payment. Many subjects orientated themselves towards the 1/n-heuristic, which also contributed to their suboptimal decisions. As long as the subjects had to make decisions for others, they noticeably made a greater effort and were also more successful than when they made decisions for themselves. However, this did not have an effect on their acceptance of robo advisors. Even when they made decisions on behalf of others, the robo advisor was only consulted in around 40% of cases. This tendency towards algorithm aversion among subjects is an obstacle to the broader establishment of robo advisors. Full article
(This article belongs to the Special Issue Advanced Portfolio Optimization and Management)
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34 pages, 516 KiB  
Article
Crypto-Coins and Credit Risk: Modelling and Forecasting Their Probability of Death
by Dean Fantazzini
J. Risk Financial Manag. 2022, 15(7), 304; https://doi.org/10.3390/jrfm15070304 - 11 Jul 2022
Cited by 8 | Viewed by 7513
Abstract
This paper examined a set of over two thousand crypto-coins observed between 2015 and 2020 to estimate their credit risk by computing their probability of death. We employed different definitions of dead coins, ranging from academic literature to professional practice; alternative forecasting models, [...] Read more.
This paper examined a set of over two thousand crypto-coins observed between 2015 and 2020 to estimate their credit risk by computing their probability of death. We employed different definitions of dead coins, ranging from academic literature to professional practice; alternative forecasting models, ranging from credit scoring models to machine learning and time-series-based models; and different forecasting horizons. We found that the choice of the coin-death definition affected the set of the best forecasting models to compute the probability of death. However, this choice was not critical, and the best models turned out to be the same in most cases. In general, we found that the cauchit and the zero-price-probability (ZPP) based on the random walk or the Markov Switching-GARCH(1,1) were the best models for newly established coins, whereas credit-scoring models and machine-learning methods using lagged trading volumes and online searches were better choices for older coins. These results also held after a set of robustness checks that considered different time samples and the coins’ market capitalization. Full article
(This article belongs to the Special Issue Advanced Portfolio Optimization and Management)
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11 pages, 523 KiB  
Article
Unleveraged Portfolios and Pure Allocation Return
by Barbara Alemanni, Mario Maggi and Pierpaolo Uberti
J. Risk Financial Manag. 2021, 14(11), 550; https://doi.org/10.3390/jrfm14110550 - 13 Nov 2021
Viewed by 2455
Abstract
In asset management, the portfolio leverage affects performance, and can be subject to constraints and operational limitations. Due to the possible leverage aversion of the investors, the comparison between portfolio performances can be incomplete or misleading. We propose a procedure to unleverage the [...] Read more.
In asset management, the portfolio leverage affects performance, and can be subject to constraints and operational limitations. Due to the possible leverage aversion of the investors, the comparison between portfolio performances can be incomplete or misleading. We propose a procedure to unleverage the mean-variance efficient portfolios to satisfy a leverage requirement. We obtain a class of unleveraged portfolios that are homogeneous in terms of leverage, so therefore properly comparable. The proposed unleverage procedure permits isolating the pure allocation return, i.e., the return component, due to the qualitative choice of portfolio allocation, from the return component due to the portfolio leverage. Theoretical analysis and empirical evidence on actual data show that efficient mean-variance portfolios, once unleveraged, uncover mean-variance dominance relations hidden by the leverage contribution to portfolio return. Our approach may be useful to practitioners proposing to take long positions on “short assets” (e.g. inverse ETF), thereby considering short positions as active investment choices, in contrast with the usual interpretation where are used to overweight long positions. Full article
(This article belongs to the Special Issue Advanced Portfolio Optimization and Management)
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24 pages, 1511 KiB  
Article
Bitcoin and Portfolio Diversification: A Portfolio Optimization Approach
by Walid Bakry, Audil Rashid, Somar Al-Mohamad and Nasser El-Kanj
J. Risk Financial Manag. 2021, 14(7), 282; https://doi.org/10.3390/jrfm14070282 - 22 Jun 2021
Cited by 30 | Viewed by 14241
Abstract
This study investigates the performance of Bitcoin as a diversifier under different constraining portfolio optimization frameworks. The study employs different constraining optimization frameworks that seek to maximize risk-adjusted returns (Sharpe ratio) of the portfolio by optimizing allocations to each asset class (asset allocation). [...] Read more.
This study investigates the performance of Bitcoin as a diversifier under different constraining portfolio optimization frameworks. The study employs different constraining optimization frameworks that seek to maximize risk-adjusted returns (Sharpe ratio) of the portfolio by optimizing allocations to each asset class (asset allocation). The performance attributes are evaluated by comparing the portfolios both with and without Bitcoin under frameworks ranging from equal-weighted, risk-parity, and semi-constrained to unconstrained. This study suggests that Bitcoin, due to its exotic nature, unwavering appeal, and unknown set of drivers, could act as a diversifier in normal market conditions, and it might also have some borderline hedge to safe haven properties. The results further suggest that while Bitcoin may be a potential diversifier for a risk-seeking investor, the risk-averse investor must exercise caution by limiting their exposure to Bitcoin in their portfolios, as unnecessary exposure may increase the probability of losses in extreme market conditions. Full article
(This article belongs to the Special Issue Advanced Portfolio Optimization and Management)
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