Search results

The optimization of investment portfolios is a topic of major importance in financial decision making, with many relevant models available in the relevant literature. The purpose of this paper is to perform a thorough comparative assessment of different bi-objective models as well as multi-objective one, in terms of the performance and robustness of the whole set of Pareto optimal portfolios.

In this study, three bi-objective models are considered (mean-variance (MV), mean absolute deviation, conditional value-at-risk (CVaR)), as well as a multi-objective model. An extensive comparison is performed using data from the Standard and Poor’s 500 index, over the period 2005–2016, through a rolling-window testing scheme. The results are analyzed using novel performance indicators representing the deviations between historical (estimated) efficient frontiers, actual out-of-sample efficient frontiers and realized out-of-sample portfolio results.

The obtained results indicate that the well-known MV model provides quite robust results compared to other bi-objective optimization models. On the other hand, the CVaR model appears to be the least robust model. The multi-objective approach offers results which are well balanced and quite competitive against simpler bi-objective models, in terms of out-of-sample performance.

This is the first comparative study of portfolio optimization models that examines the performance of the whole set of efficient portfolios, proposing analytical ways to assess their stability and robustness over time. Moreover, an extensive out-of-sample testing of a multi-objective portfolio optimization model is performed, through a rolling-window scheme, in contrast static results in prior works. The insights derived from the obtained results could be used to design improved and more robust portfolio optimization models, focusing on a multi-objective setting.

This study aims to utilize the mean–variance optimization framework of Markowitz (1952) and the generalized reduced gradient (GRG) nonlinear algorithm to find the optimal portfolio that maximizes return while keeping risk at minimum.

This study applies the portfolio optimization concept of Markowitz (1952) and the GRG nonlinear algorithm to a portfolio consisting of the 30 leading stocks from the three different sectors in Amman Stock Exchange over the period from 2009 to 2013.

The selected portfolios achieve a monthly return of 5 per cent whilst keeping risk at minimum. However, if the short-selling constraint is relaxed, the monthly return will be 9 per cent. Moreover, the GRG nonlinear algorithm enables to construct a portfolio with a Sharpe ratio of 7.4.

The results of this study are vital to both academics and practitioners, specifically the Arab and Jordanian investors.

To the best of the author’s knowledge, this is the first study in Jordan and in the Arab world that constructs optimum portfolios based on the mean–variance optimization framework of Markowitz (1952) and the GRG nonlinear algorithm.

Standard market risk optimization tools, based on assumptions of normality, are ineffective for evaluating credit risk. In this article, the authors develop three scenario optimization models for portfolio credit risk. They first create the trading risk profile and find the best hedge position for a single asset or obligor. The second model adjusts all positions simultaneously to minimize the regret of the portfolio subject to general linear restrictions. Finally, a credit risk‐return efficient frontier is constructed using parametric programming. While scenario optimization of quantile‐based credit risk measures leads to problems that are not generally tractable, regret is a relevant and tractable measure that can be optimized using linear programming. The three models are applied to optimizing the risk‐return profile of a portfolio of emerging market bonds.

This paper aims to identify a possible mismatch between the theory found in academic research and the practices of investment managers in Brazil.

The chosen approach is a field survey. This paper considers 78 survey responses from 274 asset management companies. Data obtained are analyzed using independence tests between two variables and multiple regressions.

The results show that most Brazilian investment managers have not adopted current best practices recommended by the financial academic literature and that there is a significant gap between academic recommendations and asset management practices. The modern portfolio theory is still more widely used than the post-modern portfolio theory, and quantitative portfolio optimization is less often used than the simple rule of defining a maximum concentration limit for any single asset. Moreover, the results show that the normal distribution is used more than parametrical distributions with asymmetry and kurtosis to estimate value at risk, among other findings.

This study may be considered a pioneering work in portfolio construction, risk management and performance evaluation in Brazil. Although academia in Brazil and abroad has thoroughly researched portfolio construction, risk management and performance evaluation, little is known about the actual implementation and utilization of this research by Brazilian practitioners.

The purpose of this paper is to contribute to the literature in three ways: first, the authors investigate the impact of the sampling errors on optimal portfolio weights and on financial investment decision. Second, the authors advance a comparative analysis between various domestic and international diversification strategies to define a stochastic optimal choice. Third, the authors propose a new methodology combining the re-sampling method, stochastic optimization algorithm, and nonparametric stochastic dominance (SD) approach to analyze a stochastic optimal portfolio choice for risk-averse American investors who care about benefits of domestic diversification relative to international diversification. The authors propose a new portfolio optimization model involving SD constraints on the portfolio return rate. The authors define a portfolio with return dominating the benchmark portfolio return in the second-order stochastic dominance (SSD) and having maximum expected return. The authors combine re-sampling procedure and stochastic optimization to establish more flexibility in the investment decision rule.

The authors apply the re-sampling procedure to consider the sampling error in the optimization process. The authors try to resolve the problem of the stochastic optimal investment strategy choice using the nonparametric SD test by Linton et al. (2005) based on sub-sampling simulated p values. The authors apply the stochastic portfolio optimization algorithm with SSD constraints to define optimal diversified portfolios beating benchmark indices.

First, the authors find that reducing sampling error increases the dominance relationships between different portfolios, which, in turn, alters portfolio investment decisions. Though international diversification is preferred in some cases, the study’s results show that for risk-averse US investors, in general, there is no difference between the diversification strategies; this implies that there is no increase in the expected utility of international diversification for the period before and after the 2007-2008 financial crisis. Nevertheless, the authors find that stochastic diversification in domestic, global, and Europe, Australasia, and Far East markets delivers better risk returns for the US risk averters during the crisis period.

The originality of the idea in this paper is to introduce a new methodology combining the concept of portfolio re-sampling, stochastic portfolio optimization with SSD constraints, and the nonparametric SD test by Linton et al. (2005) based on subsampling simulated p values to analyze the impact of sampling errors on optimal portfolio returns and to investigate the problem of stochastic optimal choice between international and domestic diversification strategies. The authors try to prove more coherence in the portfolio choice with the stochastically and the uncertainty characters of the paper.

New methods of integrated risk modeling play an important role in determining the efficiency of bank portfolio management. The purpose of this paper is to suggest a systematic approach for risk strategies formulation based on risk‐return optimized portfolios, which applies different methodologies of risk measurement in the context of actual regulatory requirements.

Optimization problems to illustrate different levels of integrated bank portfolio management has been set up. It constrains economic capital allocation using different risk aggregation methodologies. Novel methods of financial engineering to relate actual bank capital regulations to recently developed methods of risk measurement value‐at‐risk (VaR) and conditional value‐at‐risk (CVaR) deviation are applied. Optimization problems with the portfolio safeguard package by American Optimal Decision (web site: www.AOrDA.com) are run.

This paper finds evidence that risk aggregation in Internal Capital Adequacy Assessment Process (ICAAP) should be based on risk‐adjusted aggregation approaches, resulting in an efficient use of economic capital. By using different values of confidence level α in VaR and CVaR, deviation, it is possible to obtain optimal portfolios with similar properties. Before deciding to insert constraints on VaR or CVaR, one should analyze properties of the dataset on which computation are based, with particular focus on the model for the tails of the distribution, as none of them is “better” than the other.

This study should further be extended by an inclusion of simulation‐based scenarios and copula approaches for integrated risk measurements.

The suggested optimization models support a systematic generation of risk‐return efficient target portfolios under the ICAAP. However, issues of practical implementation in risk aggregation and capital allocation still remain unsolved and require heuristic implementations.

This chapter presents selected multiobjective methods for multiperiod portfolio optimization problem. Portfolio models are formulated as multicriteria mixed integer programs. Reference point method together with weighting approach is proposed. The portfolio selection problem considered is based on a multiperiod model of investment, in which the investor buys and sells securities in successive investment periods. The problem objective is to allocate the wealth on different securities to optimize the portfolio expected return, the probability that the return is not less than a required level. Multiobjective methods were used to find tradeoffs between risk, return, and the number of securities in the portfolio. In computational experiments the data set of daily quotations from the Warsaw Stock Exchange were used.

In this study, to maximize returns and minimize investment risk, an attempt was made to form an optimal portfolio under conditions where the capital market has a price bubble. According to the purpose, the research was of the applied type, in terms of data, quantitative and postevent, and in terms of the type of analysis, it was of the descriptive-correlation type. Sequence, skewness and kurtosis tests were used to identify the months with bubbles from 2015 to 2021 in the Tehran Stock Exchange. After identifying the bubble courses, artificial bee colony meta-heuristic and invasive weed algorithms were used to optimize the portfolio. The purpose of this paper is to address these issues.

The existence of bubbles in the market, especially in the capital market, can prevent the participation of investors in the capital market process and the correct allocation of financial resources for the economic development of the country. However, due to the goal of investors to achieve a portfolio of high returns with the least amount of risk, there is need to pay attention to these markets increases.

The results identify 14 periods of price bubbles during the study period. Additionally, stock portfolios with maximum returns and minimum risk were selected for portfolio optimization. According to the results of using meta-heuristic algorithms to optimize the portfolio, in relation to the obtained returns and risk, no significant difference was observed between the returns and risk of periods with price bubbles in each of the two meta-heuristic algorithms. This study can guide investors in identifying bubble courses and forming an optimal portfolio under these conditions.

One of the limitations of this research is the non-generalizability of the findings to stock exchanges of other countries and other time periods due to the condition of the price bubble, as well as other companies in the stock market due to the restrictions considered for selecting the statistical sample.

This study intends to form an optimal stock portfolio in a situation where the capital market suffers from a price bubble. This study provides an effective and practical solution for investors in the field of stock portfolio optimization.

Previous studies on the Malaysian securitised real estate market have largely emphasised on performance analysis, whereas the importance of securitised real estate in asset allocation is largely ignored. Therefore, the purpose of this paper is to examine the role of Malaysian property shares and real estate investment trusts (REITs) in a mixed‐asset portfolio from 1991 to 2006.

The mean‐variance and downside risk optimisations were utilised to assess the role of REITs and property shares in a mixed‐asset portfolio allocation. More specifically, the portfolio diversification potential and return enhancement benefits for both assets were examined.

The results showed that property shares offer little diversification benefits or portfolio return enhancement, whereas the equally weighted REITs portfolio does provide some diversification benefits and return enhancements under the mean‐variance and downside risk frameworks. However, the benefits have diminished in recent years. Besides, the results also revealed that the equally‐ and value‐weighted REIT portfolios do behave differently.

This study has several important implications for investors. Importantly, investors should consider the inclusion of REITs rather than property shares in their portfolios.

This paper is one of few studies in emerging markets, although Malaysia was the first country to introduce REITs in Asia. Additionally, it could be the first attempt to assess the downside risk of Malaysian securitised real estate.

With suitable optimization criteria, hybrid models have proven to be efficient for preparing portfolios in capital markets of developed countries. This study adapts and investigates these methods for a developing country, thus providing a novel approach to the application of banking and finance. Our specific objectives are to employ a stochastic dominance criterion to evaluate the performances of over-the-counter (OTC) companies in a developing country and to analyze them with a hybrid model involving particle swarm optimization and artificial neural networks.

In order to achieve these aims, the authors conduct a case study of OTC companies in Iran. Weekly and daily returns of 36 companies listed in this market are calculated for one year during 2014–2015. The hybrid model is particularly interesting, and the results of the study identify first-, second- and third-order stochastic dominances among these companies. The study’s chosen model uses the best performing combination of activation functions in our analysis, corresponding to TPT, where T represents hyperbolic tangent transfers and P represents linear transfers.

Our portfolios are based on the shares of companies ranked with respect to the stochastic dominance criterion. Considering the minimum and maximum numbers of shares to be 2 and 10 for each portfolio, an eight-share portfolio is determined to be optimal. Compared with the index of Iran OTC during the research period of this study, our selected portfolio achieves a significantly better performance. Moreover, the methods used in this analysis are shown to be as efficient as they were in the capital markets of developed countries.

The problem of optimizing investment portfolios has to allow for correlations among returns from the financial maintenance period under consideration if an asymmetric distribution of returns exists (Babaei et al., 2015). Therefore, it is desirable to select an appropriate criterion in order to prepare an optimal portfolio and prioritize investment options. Although a back propagation technique is very popular in artificial neural (ANN) training, it is time-consuming to train a network in this way, and other methods such as particle swarm optimization (PSO) should be considered instead. In the hybrid combination of PSO and ANN, it is not the structure of a neural network that changes. Rather, the weighting method and the training technique chosen for the network are the important aspects, and these relate to PSO, so the only role ANN plays in this process is to reduce the errors.

The hybrid model combining ANN and PSO is seen to be considerably successful for generating optimal results and appropriate activation functions. These results are consistent with the theoretical findings of Das et al. (2013) and an application of the simple PSO in a study conducted by Pederson and Chipperfield (2010). Our research results also confirm the efficiency of stochastic dominance criteria as noted in the studies conducted by Roman et al. (2013), ANN as in a study carried out by Kristijanpoller et al. (2014) and PSO as in studies conducted by Liu et al. (2015) and Deng et al. (2012). These studies were carried out in the capital markets of developed countries, whereas the authors’ analysis relates to a developing country.

The authors deduce that the tools and methods whose efficiency was proven in the capital markets of developed countries also apply to, and demonstrate efficiency in, two novel applications of portfolio optimization within developing countries. The first of these is gaining familiarity with the theory and practice of these research tools and the methods that enrich financial knowledge of investors in developing countries. The second of these is the application of tools and methods identified by investors in the capital markets of developing countries, which enables optimal allocation of financial resources and growth of the markets. The authors expect that these findings will contribute to improving the economies of developing countries and thus help with economic development and facilitation of improving trends.