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In this study, we analyze the power of the individual return-to-volatility security performance heuristic (ri/stdi) to simplify the identification of securities to buy and, consequently, to form the optimal no short sales mean–variance portfolios. The heuristic ri/stdi is powerful enough to identify the long and shorts sets. This is due to the positive definiteness of the variance–covariance matrix – the key is to use the heuristic sequentially. At the investor level, the heuristic helps investors to decide what securities to consider first. At the portfolio level, the heuristic may help us find out whether it is a good idea to invest in equity to begin with. Our research may also help to integrate individual security analysis into portfolio optimization through improved security rankings.

The purpose of this paper is to provide a decision support model for optimizing the composition of portfolios of market-driven academic programs, primarily in schools offering market-driven academic programs. This model seeks to maximize financial performance during a desired planning time period while also achieving targets for other non-financial dimensions of the portfolio (e.g. mission alignment, student demographics and faculty characteristics) by deciding the types of programs to be added, redesigned and/or removed for each year of the planning period.

This paper introduces an integer linear program (i.e. mathematical optimization) to describe the portfolio optimization problem. Integer linear programs are widely used for optimizing portfolios of financial and non-financial products and services in non-educational settings. Additionally, in order to use an integer linear program for the model, qualitative data must be incorporated into the quantitative model. To do so, this paper first discusses two methods of quantifying qualitative information related to market-driven program dimensions in the following section.

The paper provides empirical insights related to the impact of this model through an illustrative case from a school offering market-driven academic programs at a prestigious private university in the USA. The results of the case highlight the potential positive impact of utilizing a similar model for planning purposes. Financially, the model results in almost double financial surplus than without the model while also achieving higher scores for all non-financial dimensions measured for the portfolio analyzed.

This paper provides a unique and impactful model for decision support in strategic planning for market-driven academic programs, an area of intense discussion and focus in higher education today.

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.

This paper aims to propose an improved version of portfolio optimization model through the prediction of the future behavior of stock returns using a combined wavelet-based long short-term memory (LSTM).

First, data are gathered and divided into two parts, namely, “past data” and “real data.” In the second stage, the wavelet transform is proposed to decompose the stock closing price time series into a set of coefficients. The derived coefficients are taken as an input to the LSTM model to predict the stock closing price time series and the “future data” is created. In the third stage, the mean-variance portfolio optimization problem (MVPOP) has iteratively been run using the “past,” “future” and “real” data sets. The epsilon-constraint method is adapted to generate the Pareto front for all three runes of MVPOP.

The real daily stock closing price time series of six stocks from the FTSE 100 between January 1, 2000, and December 30, 2020, is used to check the applicability and efficacy of the proposed approach. The comparisons of “future,” “past” and “real” Pareto fronts showed that the “future” Pareto front is closer to the “real” Pareto front. This demonstrates the efficacy and applicability of proposed approach.

Most of the classic Markowitz-based portfolio optimization models used past information to estimate the associated parameters of the stocks. This study revealed that the prediction of the future behavior of stock returns using a combined wavelet-based LSTM improved the performance of the portfolio.

Airspace sectorization is an important task, which has a significant impact in the everyday work of air control services. Especially in recent years, because of the constant increase in air traffic, existing airspace sectorization techniques have difficulties to tackle the large air traffic volumes, creating imbalanced sectors and uneven workload distribution among sectors. The purpose of this paper is to propose a new approach to find optimal airspace sectorization balancing the traffic controller workload between sectors, subject to airspace requirements.

A constraint programming (CP) model called equitable airspace sectorization problem (EQASP) relies on ordered weighted averaging (OWA) multiagent optimization and the parallel portfolio architecture has been developed, which integrates the equity into an existing CP approach (Trandac et al., 2005). The EQASP was evaluated and compared with the method of Trandac et al. (2005), according to the quality of workload balancing between sectors and the resolution performance. The comparison was achieved using real air traffic low-altitude network data sets of French airspace for five flight information regions for 24 h a day and the Algerian airspace for three various periods (off peak hours, peak hours and 24 h).

It has been demonstrated that the proposed EQASP model, which is based on OWA multicriteria optimization method, significantly improved both the solving performance and the workload equity between sectors, while offering strong theoretical properties of the balancing requirement. Interestingly, when solving hard instances, our parallel sectorization tool can provide, at any time, a workable solution, which satisfies all geometric constraints of sectorization.

This study can be used to design well-balanced air sectors in terms of workload between control units in the strategic phase. To fulfil the airspace users’ constraints, one can refer to this study to assess the capacity of each air sector (especially the overloaded sectors) and then adjust the sector’s shape to respond to the dynamic changes in traffic patterns.

This theoretical and practical approach enables the development and support of the definition of the “Air traffic management (ATM) Concept Target” through improvements in human factors specifically (balancing workload across sectors), which contributes to raising the level of capacity, safety and efficiency (SESAR Vision of ATM 2035).

In their approach, the authors proposed an OWA-based multiagent optimization model, ensuring the search for the best equitable solution, without requiring user-defined balancing constraints, which enforce each sector to have a workload between two user-defined bounds (W_min, W_max).

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.

The authors aim to demonstrate the importance of taking into account “mean reversion” in asset prices and show that this type of modeling leads to a high share of equities in pension funds’ asset allocations.

First, the authors will study the long-run statistical characteristics of selected financial assets during the 1895-2011 period. Such an analysis corroborates the fact that, for long holding periods, equities exhibit lower risk than other asset classes. Moreover, they will provide empirical evidence that stock market returns are negatively skewed in the short term and show that this negative skewness vanishes over longer time horizons. Both these characteristics favor the use of a semi-parametric methodology.

This empirical study led to two major findings. First, the authors noticed that the distribution of stock returns is negatively skewed over short time horizons. Second, they observed that the fat-tailed shape of the returns distribution disappears for time periods longer than five years. Finally, they demonstrated that stock returns exhibit “mean-reversion”. Consequently, the optimization program should not only take into account the non-Gaussian nature of returns in the short run but also incorporate the speed at which volatility “mean reverts” to its long-run mean.

To simulate portfolio allocation, the authors used a Cornish–Fisher Value-at-Risk criterion with the advantage of providing an allocation that is independent of the saver’s preferences parameters. A backtesting analysis including a calculation of replacement rates shows a clear dominance of the “non-Gaussian” strategy because the retirement outcomes under such a strategy would be positively affected.

This study attempts to conduct a comparative analysis between dynamic and static asset allocation to achieve the long-term target return on asset liability management (ALM). This study conducts asset allocation using the ex ante expected rate of return through the outlook of future economic indicators because past economic indicators or realized rate of returns which are used as input data for expected rate of returns in the “building block” method, most adopted by domestic pension funds, does not fully reflect the future economic situation. Vector autoregression is used to estimate and forecast long-term interest rates. Furthermore, it is applied to gross domestic product and consumer price index estimation because it is widely used in financial time series data. Based on asset allocation simulations, this study derived the following insights: first, economic indicator filtering and upper-lower bound computation is needed to reduce the expected return volatility. Second, to reach the ALM goal, more stocks should be allocated than low-yielding assets. Finally, dynamic asset allocation which has been mirroring economic changes actively has a higher annual yield and risk-adjusted return than static asset allocation.

Through direct personal contact we have identified over 2000 corporations in the United States, Canada, and Europe that are either using, developing, or planning to develop some form of corporate planning model. In 1974 we mailed questionnaires to 1881 of these corporations to ascertain (1) who is using corporate models, (2) why they are being used, (3) how they are being used, (4) what resources are required, (5) which techniques and structures are being employed, (6) what the costs and benefits are, (7) what enhancements are planned, and (8) what the future holds for corporate modeling. A total of 346 corporations responded to the survey—a response rate of 19 percent. No definitive statements can be made concerning the 1535 firms that did not respond, but one possibility is that they considered the completion of a 47‐question questionnaire to be an excessive demand on their available time.

This chapter presents the survey of selected linear and mixed integer programming multi-objective portfolio optimization. The definitions of selected percentile risk measures are presented. Some contrasts and similarities of the different types of portfolio formulations are drawn out. The survey of multi-criteria methods devoted to portfolio optimization such as weighting approach, lexicographic approach, and reference point method is also presented. This survey presents the nature of the multi-objective portfolio problems focuses on a compromise between the construction of objectives, constraints, and decision variables in a portfolio and the problem complexity of the implemented mathematical models. There is always a trade-off between computational time and the size of an input data, as well as the type of mathematical programming formulation with linear and/or mixed integer variables.