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This study aims to analyze Islamic financial institutions’ (IFIs) current financial engineering and product development procedures.

The paper is quantitative in nature and the survey questionnaire were collected from managers and IF experts working for Islamic Banks, Takaful and other IFIs in Turkey, Malaysia and UAE. Two-stage structural equation modeling was used to test the hypothesis.

The findings highlighted that the Shari’ah Supervisory Board, Strategy and Planning of IFIs, Legal and Regulatory framework, pricing of a new product and financial performance positively impact the new product development (NPD) process. At the same time, Islamic values have no significant positive impact.

When generalizing the research results, data collection from the right departments was the main limitation of the current study. Future research may opt to collect data only from Product Development Departments.

The findings of this study will allow IFIs to reflect on their present methods, procedures and Shari’ah compliance framework for the NPD process.

Factors affecting the product development and financial engineering process are discussed in the literature. The findings of this study can be regarded as building blocks for future academic research on product development and financial engineering in Islamic finance.

The purpose of this paper is to propose a risk-based framework to estimate the option value of infrastructure investment, accounting for the stochastic behavior of both financial and physical (engineering) variables.

This study uses a real-options approach and computes the optimal investment dates and option values using Least Squares Monte Carlo, both the original Longstaff – Schwartz algorithm and the constrained Least Squares approach of Le tourneau – Stentoft.

Real-option value for infrastructure investment is substantial. It is beneficial to model jointly financial and engineering risks to better understand the timing and real-option value of infrastructure investment. The analysis further shows which variables are option value drivers.

Future work could integrate financing constraints into the model, consider path dependency in the physical state variables or integrate sovereign risk, expropriation risk, operational risk or other project risks.

Financial practitioners and investment managers interested in infrastructure risk finance or project finance will benefit from a novel framework to analyze infrastructure investments in which engineering and financial risks interact in a tractable way.

Public decision-makers will benefit from a better understanding of what determines the value of infrastructure investments, how real-option value affects optimal investment timing and how both are determined by financial and engineering risks.

The analysis considers financial and engineering risks in a single framework to better understand option value in infrastructure investment. The framework and findings are useful both to risk finance and project finance practitioners and investors as well as engineers and public sector decision-makers.

The purpose of this paper is to report a case study in which financial feasibility integrated quality function deployment (QFD) approach was implemented.

Customer complaints were systematically gathered. The house of quality (HoQ) matrix was developed. The technical descriptors were prioritized and subjected to the financial feasibility study. The cost calculations were carried out and the actions were derived. A set of value engineering (VE) principles was used during this case study.

The study reported in this paper indicated the need for integrating financial feasibility study with QFD for enhancing the effectiveness of the method. The measures taken to prevent the customer complaints will be of considerable value to the manufacturing organizations.

During the conduct of case study, high-cost factors restricted the selection of materials which would exhibit higher performance. The case study was carried out in a single electronic switches manufacturing organization.

The manufacturing costs incurred have been reduced by incorporating changes in the part material. The outcomes of the study have been considered for further implementation in the case organisation which indicated the practicality of the study.

The concept of apportionment of HoQ cost developed with the idea of integrating the same with QFD is the original contribution of the authors.

Almost all projects in the world are delayed, and sometimes even lead to the full bankruptcy of their beneficiaries. These delays can be calculated using techniques, but most importantly, there must be a fair and realistic division of delays between project beneficiaries. The most valid delay calculation techniques belong to the SCL Global Protocol, but they also have significant drawbacks, such as these: (1) They do not have the capability to prevent project delays (Delay Risk Management); (2) The protocol identifies and introduces any delays in activities with a ratio of one to one as a delay (Effective Delay); (3) It also does not offer the capability to share delays between stakeholders, which is a huge weakness. Floating in the base schedule activities is one of the cost control tools of projects, but it can hide project delays. In this paper, the researchers believe that the floating ownership belongs to the project and not belong to the stakeholders. This is the main tool for analyzing and sharing delays in this research.

The research methodology adopted included an extensive literature review, expert interviews, use of questionnaire and designing three innovative linked together models by researchers.

In this research, an integrated technique is introduced which has the following capabilities; delay risk control, result-based delay analysis and stakeholders delay sharing. This technique with an incursive and defensive approach implements claims management principles and calculates, respectively, non-attributable and attributable delays for each beneficiary.

This creativity led to the introduction of the Incursive and Defensive (In-De) technique; in the SCL protocol techniques, none of these capabilities exist.

This paper aims to empirically test, from a regulatory portfolio management standpoint, the application of liquidity-adjusted risk techniques in the process of getting optimum and investable economic-capital structures in the Gulf Cooperation Council financial markets, subject to applying various operational and financial optimization restrictions under crisis outlooks.

The author implements a robust methodology to assess regulatory economic-capital allocation in a liquidity-adjusted value at risk (LVaR) context, mostly from the standpoint of investable portfolios analytics that have long- and short-sales asset allocation or for those portfolios that contain long-only asset allocation. The optimization route is accomplished by controlling the nonlinear quadratic objective risk function with certain regulatory constraints along with LVaR-GARCH-M (1,1) procedure to forecast conditional risk parameters and expected returns for multiple asset classes.

The author’s conclusions emphasize that the attained investable economic-capital portfolios lie-off the efficient frontier, yet those long-only portfolios seem to lie near the efficient frontier than portfolios with long- and short-sales assets allocation. In effect, the newly observed market microstructures forms and derived deductions were not apparent in prior research studies (Al Janabi, 2013).

The attained empirical results are quite interesting for practical portfolio optimization, within the environments of big data analytics, reinforcement machine learning, expert systems and smart financial applications. Furthermore, it is quite promising for multiple-asset portfolio management techniques, performance measurement and improvement analytics, reinforcement machine learning and operations research algorithms in financial institutions operations, above all after the consequences of the 2007–2009 financial crisis.

While this paper builds on Al Janabi’s (2013) optimization algorithms and modeling techniques, it varies in the sense that it covers the outcomes of a multi-asset portfolio optimization method under severe event market scenarios and by allowing for both long-only and combinations of long-/short-sales multiple asset. The achieved empirical results, optimization parameters and efficient and investable economic-capital figures were not apparent in Al Janabi’s (2013) paper because the prior evaluation were performed under normal market circumstances and without bearing in mind the impacts of the 2007–2009 global financial crunch.

Perhaps there is no area in which the practice of financial management and its academic treatment are more divergent than in the area of corporate strategy or strategic planning. The practice of financial management or financial engineering has for many years been involved in assessing various strategies and analyzing investment alternatives with dominant strategy themes (e.g. investments in research in development). Their academic counterparts, however, have had great difficulty relating the strategy literature to the finance literature. There are a number of reasons for this but perhaps the most important one has been the fact that the strategy and finance literatures have grown up with very different heritages. Financial economists, who provide the theoretical structure for the practice of financial management, have a deeply rooted heritage in the structure of the neoclassical theory of the firm and have traditionally been extremely reluctant to branch out to new areas unless those areas could somehow be incorporated into that particular theoretical structure. This seems to be changing. Part of the reason has been the movement of the strategy literature toward finance as is evidenced in the work of Michael Porter who has adopted the language of the theory of the firm to address the problem of corporate strategy. In addition, part of the movement toward convergence is due to a similar movement of the finance literature toward consideration of strategy issues using contingent claims models as is exemplified in the work of Stewart Myers. In this paper we begin to weave the story of agreement between strategy and finance. Furthermore, we identify some of the issues that must be addressed as this process of convergence continues.

This study aims to examine the theoretical foundations for multivariate portfolio optimization algorithms under illiquid market conditions. In this study, special emphasis is devoted to the application of a risk-engine, which is based on the contemporary concept of liquidity-adjusted value-at-risk (LVaR), to multivariate optimization of investment portfolios.

This paper examines the modeling parameters of LVaR technique under event market settings and discusses how to integrate asset liquidity risk into LVaR models. Finally, the authors discuss scenario optimization algorithms for the assessment of structured investment portfolios and present a detailed operational methodology for computer programming purposes and prospective research design with the backing of a graphical flowchart.

To that end, the portfolio/risk manager can specify different closeout horizons and dependence measures and calculate the necessary LVaR and resulting investable portfolios. In addition, portfolio managers can compare the return/risk ratio and asset allocation of obtained investable portfolios with different liquidation horizons in relation to the conventional Markowitz´s mean-variance approach.

The examined optimization algorithms and modeling techniques have important practical applications for portfolio management and risk assessment, and can have many uses within machine learning and artificial intelligence, expert systems and smart financial applications, financial technology (FinTech), and within big data environments. In addition, it provide key real-world implications for portfolio/risk managers, treasury directors, risk management executives, policymakers and financial regulators to comply with the requirements of Basel III best practices on liquidly risk.

The proposed optimization algorithms can aid in advancing portfolios selection and management in financial markets by assessing investable portfolios subject to meaningful operational and financial constraints. Furthermore, the robust risk-algorithms and portfolio optimization techniques can aid in solving some real-world dilemmas under stressed and adverse market conditions, such as the effect of liquidity when it dries up in financial and commodity markets, the impact of correlations factors when there is a switching in their signs and the integration of the influence of the nonlinear and non-normal distribution of assets’ returns in portfolio optimization and management.

As program managers seek to improve the quality, speed and financial benefits of the programs they manage, many are turning to process improvement methodologies, such as Lean Six Sigma (LSS). However, although existing literature includes multiple studies that apply the methodology to non-manufacturing environments, there is no specific framework for applying LSS within program management (PM). Therefore, the purpose of this paper is to examine the relationships between LSS tools, project scope, program phase and functional area and project outputs, in PM organizations.

The study uses archival data from 511 LSS projects completed from 2006 to 2015 by a large government agency in the USA composed of 13 PM organizations. The study focuses on four types of input factors: LSS tools, project scope, program phase and functional area; and two output variables: LSS project average financial benefits and percentage of improvement. Multiple regressions are applied to determine what relationships exist between the input and output variables, as well as the nature of such relationships.

The results of this study show LSS is beneficial to PM and also indicate which tools and organizational contexts have positive and negative associations with project outcomes, serving as guide for future applications. In addition, this study can provide clarity and confidence to program managers who are currently skeptical of LSS, by showing that it can provide cost, schedule and performance improvements beneficial to their programs.

Limitations of this research include the use of a single government agency in the USA, the non-experimental design of the study and limitations associated with the nature and data collection process of the archival data. Future studies should include additional PM organizations, input variables and research designs.

There is no specific framework formalizing the concept of LSS application within PM. The literature includes several studies that apply the methodology to non-manufacturing environments, but not to PM specifically. Furthermore, the existing literature on PM does not explicitly cite any continuous improvement methodology as a critical success factor or provide any detailed guidelines for the application of LSS in PM. This paper contributes by studying the relationships between LSS tools, project scope, program phase and functional area, and project outputs, in a PM environment.

Explains the importance of assessing country risk to lenders and investors, outlines previous research on techniques for doing this and describes a classification method: the multi‐group hierarchical discrimination method (MHD). Applies this to 1978‐1995 data for 143 countries, subdivided into four income groups, and compares the results with those from multiple discriminant, logit and probit analyses using jackknife procedures. Finds MHD more accurate overall and for most income groups except the lower‐middle income economies. Briefly considers other applications for MHD and avenues for further research.

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.